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<title>Jat Ai News &amp; Unveiling the Future of Intelligence &amp; : AWS</title>
<link>https://news.jatlink.uk/rss/category/tools-aws</link>
<description>Jat Ai News &amp; Unveiling the Future of Intelligence &amp; : AWS</description>
<dc:language>en</dc:language>
<dc:rights>Copyright 2024 Jat Link Limited &amp; All Rights Reserved.</dc:rights>

<item>
<title>The art and science of hyperparameter optimization on Amazon Nova Forge</title>
<link>https://news.jatlink.uk/12691</link>
<guid>https://news.jatlink.uk/12691</guid>
<description><![CDATA[ Fine-tuning for domain-specific tasks means improving performance in one area without degrading the model’s general capabilities, and getting that balance right is harder than it looks. This post walks through how to navigate that balance, from selecting the right customization strategy for your data and task, to configuring the training parameters that most influence outcomes, like learning rate, batch size, and checkpointing. We also cover the common mistakes that lead to wasted training runs and how to catch them early, so you can improve domain performance without degrading general capabilities or burning through compute on avoidable failures. By the end, you will know how to improve domain performance without degrading general capabilities and how to avoid the expensive failures that come from getting the balance wrong. ]]></description>
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<pubDate>Tue, 02 Jun 2026 21:00:14 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>The, art, and, science, hyperparameter, optimization, Amazon, Nova, Forge</media:keywords>
</item>

<item>
<title>Object detection with Amazon Nova 2 Lite</title>
<link>https://news.jatlink.uk/12692</link>
<guid>https://news.jatlink.uk/12692</guid>
<description><![CDATA[ In this post, we&#039;ll walk through implementing object detection with Amazon Nova 2 Lite. You&#039;ll learn how to deploy an object detection application using Amazon Bedrock, AWS Lambda, and Amazon API Gateway. You&#039;ll also learn how to craft effective prompts, process structured JSON output, and visualize results. We explore practical applications across manufacturing, agriculture, and logistics. ]]></description>
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<pubDate>Tue, 02 Jun 2026 21:00:14 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Object, detection, with, Amazon, Nova, Lite</media:keywords>
</item>

<item>
<title>How Baz improved its AI Agent Code Review accuracy using Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/12678</link>
<guid>https://news.jatlink.uk/12678</guid>
<description><![CDATA[ This post walks through how Baz built their Spec Review agent using Amazon Bedrock and Amazon Bedrock AgentCore. We&#039;ll cover the architecture decisions, implementation details, and the business outcomes they achieved by leveraging these AWS services to automate their code review process ]]></description>
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<pubDate>Tue, 02 Jun 2026 17:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Baz, improved, its, Agent, Code, Review, accuracy, using, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Building a secure auth code flow setup using AgentCore Gateway with MCP clients</title>
<link>https://news.jatlink.uk/12632</link>
<guid>https://news.jatlink.uk/12632</guid>
<description><![CDATA[ This post demonstrates how to implement Open Authorization (OAuth) Code flow as an inbound authorization mechanism for MCP servers hosted on Amazon Bedrock AgentCore Gateway. By the end of this guide, you will have a production-ready setup where each AI assistant request is authenticated with a valid user identity token issued from your organization’s identity provider. ]]></description>
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<pubDate>Tue, 02 Jun 2026 05:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, secure, auth, code, flow, setup, using, AgentCore, Gateway, with, MCP, clients</media:keywords>
</item>

<item>
<title>OpenAI models and Codex on Amazon Bedrock are now generally available</title>
<link>https://news.jatlink.uk/12616</link>
<guid>https://news.jatlink.uk/12616</guid>
<description><![CDATA[ GPT-5.5, GPT-5.4, and Codex are now generally available on Amazon Bedrock. Deploy them in production applications and agents today, on Bedrock’s high performance inference engine.  ]]></description>
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<pubDate>Tue, 02 Jun 2026 01:00:15 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>OpenAI, models, and, Codex, Amazon, Bedrock, are, now, generally, available</media:keywords>
</item>

<item>
<title>Transforming rare cancer research with Amazon Quick: Integrating biomedical databases for breakthrough discoveries</title>
<link>https://news.jatlink.uk/12615</link>
<guid>https://news.jatlink.uk/12615</guid>
<description><![CDATA[ In this post, we walk through how to use Amazon Quick Research to integrate biomedical data sources for rare cancer research. The walkthrough uses pediatric sarcoma as the research domain and draws on publicly available datasets from PubMed and other open biomedical repositories. It covers the end-to-end workflow: defining a research objective, configuring data sources, reviewing the AI-generated research plan, running the investigation, and iterating on results using the revision and versioning system. ]]></description>
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<pubDate>Tue, 02 Jun 2026 01:00:15 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Transforming, rare, cancer, research, with, Amazon, Quick:, Integrating, biomedical, databases, for, breakthrough, discoveries</media:keywords>
</item>

<item>
<title>Reference your own AWS Secrets Manager secrets in Amazon Bedrock AgentCore Identity</title>
<link>https://news.jatlink.uk/12614</link>
<guid>https://news.jatlink.uk/12614</guid>
<description><![CDATA[ Today, we’re excited to announce the ability to reference a secret in AWS Secrets Manager for AgentCore Identity, so you can reference your own preconfigured secret from Secrets Manager and retain full control over how it is managed. With this ability, you can extend your organization’s existing secrets governance processes to AgentCore. You can provide an existing, preconfigured AWS Secrets Manager secret to use with your credential provider resources. You retain full control over its encryption configuration, rotation, replication, tags, and resource policies, just as you would manage other secrets in Secrets Manager. You can also choose a secret from another AWS account within the same AWS Region, though cross-Region secret sharing isn’t supported. This also supports secrets brought in through AWS Secrets Manager external connectors, enabling integration with third-party secret managers. ]]></description>
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<pubDate>Tue, 02 Jun 2026 01:00:14 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Reference, your, own, AWS, Secrets, Manager, secrets, Amazon, Bedrock, AgentCore, Identity</media:keywords>
</item>

<item>
<title>AgentOps: Operationalize agentic AI at scale with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/12596</link>
<guid>https://news.jatlink.uk/12596</guid>
<description><![CDATA[ When you build agentic AI solutions, you face unique operational challenges. Agents make unpredictable decisions, costs spiral unexpectedly, and debugging non-deterministic failures seems impossible. Agentic AI applications don&#039;t just execute predetermined workflows. They reason, adapt, and make autonomous decisions, and DevOps practices need to be adapted. That&#039;s where AgentOps comes in, the operational discipline for deploying, managing, and continuously improving AI agents in production. ]]></description>
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<pubDate>Mon, 01 Jun 2026 21:00:14 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>AgentOps:, Operationalize, agentic, scale, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Accelerate LLM model loading and increase context windows with GPUDirect on Amazon FSx for Lustre and TurboQuant</title>
<link>https://news.jatlink.uk/12597</link>
<guid>https://news.jatlink.uk/12597</guid>
<description><![CDATA[ If you’re iterating on deploying large language models (LLMs) on AWS GPU instances, you’ve probably noticed the larger the model to be loaded into GPU High Bandwidth Memory (HBM), the longer the painful wait until the GPUs are ready for inference. As models grow to hundreds of billions of parameters and GPU environments grow ever […] ]]></description>
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<pubDate>Mon, 01 Jun 2026 21:00:14 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerate, LLM, model, loading, and, increase, context, windows, with, GPUDirect, Amazon, FSx, for, Lustre, and, TurboQuant</media:keywords>
</item>

<item>
<title>Amazon Quick integration with time&amp;series databases for market intelligence using MCP</title>
<link>https://news.jatlink.uk/12598</link>
<guid>https://news.jatlink.uk/12598</guid>
<description><![CDATA[ In this post, we walk through a practical implementation using KDB-X MCP server integration with Amazon Quick, demonstrating how traders and analysts can ask questions using conversational language and receive actionable insights from datasets. You can apply this same integration pattern across various domains, from financial market analysis to IoT sensor monitoring to DevOps performance dashboards, where you need to simplify access to time series insights. ]]></description>
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<pubDate>Mon, 01 Jun 2026 21:00:14 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Amazon, Quick, integration, with, time-series, databases, for, market, intelligence, using, MCP</media:keywords>
</item>

<item>
<title>Enable safe agentic payments with built&amp;in guardrails using Amazon Bedrock AgentCore payments</title>
<link>https://news.jatlink.uk/12595</link>
<guid>https://news.jatlink.uk/12595</guid>
<description><![CDATA[ In this post, we address several key risks that surface when designing an agentic payment system, and how to address them with the capabilities of AgentCore payments. ]]></description>
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<pubDate>Mon, 01 Jun 2026 21:00:13 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Enable, safe, agentic, payments, with, built-in, guardrails, using, Amazon, Bedrock, AgentCore, payments</media:keywords>
</item>

<item>
<title>Extending MCP support for Amazon Bedrock AgentCore Gateway</title>
<link>https://news.jatlink.uk/12593</link>
<guid>https://news.jatlink.uk/12593</guid>
<description><![CDATA[ While deploying Model Context Protocol (MCP) servers in production, enterprises need fine-grained access control across servers, observability into which teams use which tools, security guarantees against data exfiltration, and centralized credential management, all at scale. Amazon Bedrock AgentCore Gateway sits between MCP servers and the clients that consume them, centralizing credential management, observability, and secure […] ]]></description>
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<pubDate>Mon, 01 Jun 2026 21:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Extending, MCP, support, for, Amazon, Bedrock, AgentCore, Gateway</media:keywords>
</item>

<item>
<title>Secure AI agents with Policy and Lambda interceptors in Amazon Bedrock AgentCore gateway</title>
<link>https://news.jatlink.uk/12594</link>
<guid>https://news.jatlink.uk/12594</guid>
<description><![CDATA[ In this post, we use a lakehouse data agent to demonstrate how you can use Policy for deterministic access control and Lambda interceptors for dynamic validation. We then show how to combine Lambda interceptors and Policy to implement a geography-based access control which requires both dynamic validation and deterministic access control. ]]></description>
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<pubDate>Mon, 01 Jun 2026 21:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Secure, agents, with, Policy, and, Lambda, interceptors, Amazon, Bedrock, AgentCore, gateway</media:keywords>
</item>

<item>
<title>Comprehensive observability for Amazon SageMaker AI LLM inference: From GPU utilization to LLM quality</title>
<link>https://news.jatlink.uk/12381</link>
<guid>https://news.jatlink.uk/12381</guid>
<description><![CDATA[ This post demonstrates a comprehensive observability solution using Amazon Managed Grafana dashboards that provides a holistic view of both quality and quantity for LLMs served on Amazon SageMaker AI endpoints with inference components. ]]></description>
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<pubDate>Sat, 30 May 2026 01:00:11 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Comprehensive, observability, for, Amazon, SageMaker, LLM, inference:, From, GPU, utilization, LLM, quality</media:keywords>
</item>

<item>
<title>Streamline external access to Amazon SageMaker MLflow using a REST API proxy</title>
<link>https://news.jatlink.uk/12300</link>
<guid>https://news.jatlink.uk/12300</guid>
<description><![CDATA[ In this post, we demonstrate how to build a secure Flask-based MLflow proxy service that provides HTTPS access to Amazon SageMaker MLflow without requiring the MLflow SDK. This solution is for organizations undergoing cloud transformation who want to preserve their existing ML workflows while adopting cloud-native services. ]]></description>
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<pubDate>Fri, 29 May 2026 01:00:17 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Streamline, external, access, Amazon, SageMaker, MLflow, using, REST, API, proxy</media:keywords>
</item>

<item>
<title>Evaluating Deep Agents using LangSmith on AWS</title>
<link>https://news.jatlink.uk/12301</link>
<guid>https://news.jatlink.uk/12301</guid>
<description><![CDATA[ This post combines learnings from LangChain’s work on evaluating deep agents and Anthropic’s guide to demystifying evals for AI agents into a practical guide. In this post, you will learn how to: 1) apply five evaluation patterns for deep agents, 2) build offline evaluations using pytest and LangSmith, and 3) configure online monitoring for production. The walkthrough uses a text-to-SQL deep agent with Amazon Bedrock for the full development to production lifecycle. ]]></description>
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<pubDate>Fri, 29 May 2026 01:00:17 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Evaluating, Deep, Agents, using, LangSmith, AWS</media:keywords>
</item>

<item>
<title>Build a custom portal with embedded Amazon SageMaker AI MLflow Apps</title>
<link>https://news.jatlink.uk/12299</link>
<guid>https://news.jatlink.uk/12299</guid>
<description><![CDATA[ In this post, you learn how to build a custom portal with embedded SageMaker AI MLflow Apps UI. You walk through the architecture pattern behind a React front end paired with a Flask reverse proxy that handles AWS Signature Version 4 (SigV4) authentication, deploy the entire stack through the AWS Cloud Development Kit (AWS CDK), validate the deployment, and review security considerations and cleanup procedures. ]]></description>
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<pubDate>Fri, 29 May 2026 01:00:16 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, custom, portal, with, embedded, Amazon, SageMaker, MLflow, Apps</media:keywords>
</item>

<item>
<title>Training Azerbaijani language models on Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/12298</link>
<guid>https://news.jatlink.uk/12298</guid>
<description><![CDATA[ Azercell Telecom LLC, Azerbaijan&#039;s leading telecommunications provider, wanted to build an Azerbaijani large language model (LLM) on Amazon SageMaker AI for telecom use cases and a customer-facing chatbot. The challenge: adapting foundation models (FMs) to a morphologically rich language with limited training data and no existing blueprint for efficient LLM training in Azerbaijani. In a six-week collaboration, Azercell worked with the AWS Generative AI Innovation Center to establish a production-ready framework on Amazon SageMaker AI. ]]></description>
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<pubDate>Fri, 29 May 2026 01:00:15 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Training, Azerbaijani, language, models, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Claude Opus 4.8 is now available on AWS</title>
<link>https://news.jatlink.uk/12281</link>
<guid>https://news.jatlink.uk/12281</guid>
<description><![CDATA[ This post covers Opus 4.8&#039;s improvements and practical guidance for AI engineers integrating the model into agentic systems and production inference workloads on Amazon Bedrock. ]]></description>
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<pubDate>Thu, 28 May 2026 21:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Claude, Opus, 4.8, now, available, AWS</media:keywords>
</item>

<item>
<title>Build a test suite that grows with your agent with dataset management in Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/12280</link>
<guid>https://news.jatlink.uk/12280</guid>
<description><![CDATA[ Agent evaluation is most powerful when you combine fast-moving online signals with stable offline baselines. To understand whether your agent is truly improving over time, you need a fixed benchmark alongside your changing real-world traffic. Managing test cases for evaluation baselines as a dataset in Amazon Bedrock AgentCore brings the discipline of versioned test fixtures […] ]]></description>
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<pubDate>Thu, 28 May 2026 21:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, test, suite, that, grows, with, your, agent, with, dataset, management, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Automate AML alert triage with Amazon Quick and Snowflake Cortex AI</title>
<link>https://news.jatlink.uk/12282</link>
<guid>https://news.jatlink.uk/12282</guid>
<description><![CDATA[ This post demonstrates that integration in action by automating one of the most labor-intensive workflows in financial services: anti-money laundering (AML) alert triage. You will build a triage workflow using Amazon Quick Flows and Snowflake Cortex, connected through the Amazon Quick Model Context Protocol (MCP) integration. In our testing environment, automated workflows built using Amazon Quick reduced alert investigation time from 30-90 minutes to under 5 minutes. Actual results may vary based on alert complexity and data volume. ]]></description>
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<pubDate>Thu, 28 May 2026 21:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Automate, AML, alert, triage, with, Amazon, Quick, and, Snowflake, Cortex</media:keywords>
</item>

<item>
<title>Building AI agents for business support using Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/12206</link>
<guid>https://news.jatlink.uk/12206</guid>
<description><![CDATA[ In this post, we share how the AWS Generative AI Innovation Center (GenAIIC) collaborated with Works Human Intelligence (WHI) to build two AI agents using Amazon Bedrock AgentCore. We discuss the challenges encountered and the solutions that reduced costs by up to 97% while improving operational efficiency. ]]></description>
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<pubDate>Thu, 28 May 2026 01:00:13 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, agents, for, business, support, using, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>From data overload to actionable insights: How Verizon Connect scaled agentic AI to 100,000 users</title>
<link>https://news.jatlink.uk/12207</link>
<guid>https://news.jatlink.uk/12207</guid>
<description><![CDATA[ In this post, we show you how Verizon Connect built and scaled an agentic AI solution to transform overwhelming fleet data into clear, actionable insights for 100,000 users daily. We walk you through the architectural decisions, implementation challenges, and measurable results that can guide your own data-to-insights transformation. ]]></description>
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<pubDate>Thu, 28 May 2026 01:00:13 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>From, data, overload, actionable, insights:, How, Verizon, Connect, scaled, agentic, 100, 000, users</media:keywords>
</item>

<item>
<title>Process financial documents using Amazon Bedrock Data Automation</title>
<link>https://news.jatlink.uk/12205</link>
<guid>https://news.jatlink.uk/12205</guid>
<description><![CDATA[ In this post, we explore how Amazon Bedrock Data Automation can accurately extract information from four common types of financial documents: bank statements, W-2 forms, 1099-B tax forms, and vendor contracts. We highlight the complexity in the documents, detail the custom extraction created in Amazon Bedrock Data Automation, and describe the outcomes of the extraction process. ]]></description>
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<pubDate>Thu, 28 May 2026 01:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Process, financial, documents, using, Amazon, Bedrock, Data, Automation</media:keywords>
</item>

<item>
<title>Powering agentic AI sales strategy with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/12190</link>
<guid>https://news.jatlink.uk/12190</guid>
<description><![CDATA[ As agent adoption scaled, we saw a common pattern emerge across enterprises, including our own sales organization: specialized agents deliver value, but without orchestration, users carry the cognitive load of choosing between them. At AWS Sales, this meant more than 20 domain-specific agents deployed across the global organization, with representatives context-switching between systems instead of […] ]]></description>
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<pubDate>Wed, 27 May 2026 21:00:15 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Powering, agentic, sales, strategy, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>How AWS SMGS uses an AI&amp;powered conversational assistant to transform business management with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/12189</link>
<guid>https://news.jatlink.uk/12189</guid>
<description><![CDATA[ In this post, we share how we built NarrateAI using Amazon Bedrock AgentCore to deliver business intelligence at scale for the AWS SMGS (Sales, Marketing and Global Services) organization. You will learn about: the two-layer architecture that separates batch processing from real-time interaction, the specialized AI agents that power intelligent routing and validation, key engineering patterns for production deployment, and how to build similar solutions with AWS services. ]]></description>
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<pubDate>Wed, 27 May 2026 21:00:14 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, AWS, SMGS, uses, AI-powered, conversational, assistant, transform, business, management, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>AgentWatch: Proactive AWS monitoring with ambient agents</title>
<link>https://news.jatlink.uk/12102</link>
<guid>https://news.jatlink.uk/12102</guid>
<description><![CDATA[ In this post, we demonstrate the capabilities of AgentWatch through practical implementation. You will see how the solution performs infrastructure checks every 15 minutes, summarizing CloudWatch metrics, logs, and alarms across multiple AWS accounts. The agent delivers actionable reports directly to Slack and responds to natural language queries about your infrastructure state. Throughout, we explore three human-in-the-loop patterns that maintain appropriate oversight while maximizing automation. ]]></description>
<enclosure url="http://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/05/26/20132.png" length="49398" type="image/jpeg"/>
<pubDate>Tue, 26 May 2026 21:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>AgentWatch:, Proactive, AWS, monitoring, with, ambient, agents</media:keywords>
</item>

<item>
<title>From idea to AI app: Creating intelligent research assistants with Strands</title>
<link>https://news.jatlink.uk/12103</link>
<guid>https://news.jatlink.uk/12103</guid>
<description><![CDATA[ Building an AI app shouldn’t require a PhD in machine learning (ML) or months of wrestling with complex architectures. Yet that’s exactly what happens when you try to orchestrate multiple API calls, manage conversation state, and create agents that can reason on their own. I’ve seen straightforward AI ideas balloon into sprawling projects that demand […] ]]></description>
<enclosure url="http://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/05/21/ML-19684.png" length="49398" type="image/jpeg"/>
<pubDate>Tue, 26 May 2026 21:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>From, idea, app:, Creating, intelligent, research, assistants, with, Strands</media:keywords>
</item>

<item>
<title>Build an enterprise observability solution for Amazon Quick</title>
<link>https://news.jatlink.uk/12104</link>
<guid>https://news.jatlink.uk/12104</guid>
<description><![CDATA[ When hundreds to thousands of users are onboarded to an enterprise AI platform, business leaders and platform owners need visibility into who is using the platform, whether users are satisfied with the answers they receive, and which capabilities are driving the most engagement. Without a centralized observability solution, this data is scattered across multiple AWS […] ]]></description>
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<pubDate>Tue, 26 May 2026 21:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, enterprise, observability, solution, for, Amazon, Quick</media:keywords>
</item>

<item>
<title>Technical deep dive: AgentCore payments and innovation in agentic commerce</title>
<link>https://news.jatlink.uk/12099</link>
<guid>https://news.jatlink.uk/12099</guid>
<description><![CDATA[ Amazon Bedrock AgentCore payments is now available in preview, it provides instant payments to paid external services with no manual billing setup per provider, stablecoin support for cost-effective microtransactions that make sub-cent transactions economically viable, and configurable spending guardrails that give you fine-grained control over agent budgets and transaction limits. In this post, we walk you through a technical deep dive of AgentCore payments. ]]></description>
<enclosure url="http://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/05/26/21056_2.png" length="49398" type="image/jpeg"/>
<pubDate>Tue, 26 May 2026 21:00:11 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Technical, deep, dive:, AgentCore, payments, and, innovation, agentic, commerce</media:keywords>
</item>

<item>
<title>Build highly scalable serverless LangGraph multi&amp;agent systems in AWS with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/12100</link>
<guid>https://news.jatlink.uk/12100</guid>
<description><![CDATA[ In this post, we provide a solution to build highly scalable, serverless multi-agent generative AI systems on AWS using LangGraph Agents as orchestrators integrated with Amazon Bedrock AgentCore Memory and Amazon Bedrock AgentCore Observability. ]]></description>
<enclosure url="http://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/05/26/20165.png" length="49398" type="image/jpeg"/>
<pubDate>Tue, 26 May 2026 21:00:11 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, highly, scalable, serverless, LangGraph, multi-agent, systems, AWS, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Build high&amp;performance generative AI systems with Strands Agents, NVIDIA NIM, and Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/12101</link>
<guid>https://news.jatlink.uk/12101</guid>
<description><![CDATA[ In this post you&#039;ll learn how to build a multi-agent campaign review system that demonstrates parallel reasoning, context persistence, and traceable execution paths using an integrated architecture that combines NVIDIA NIM for GPU-accelerated inference. Amazon Bedrock AgentCore provides managed runtime, shared memory and built-in observability and Strands Agents provide serverless multi-agent orchestration. This approach supports performance, scalability, and operational insight in production environments. While the example focuses on marketing content review, the same pattern applies to digital assistants, review automation, and retrieval-augmented generation pipelines. ]]></description>
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<pubDate>Tue, 26 May 2026 21:00:11 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, high-performance, generative, systems, with, Strands, Agents, NVIDIA, NIM, and, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Transforming professional work: How Amazon Quick turns document creation from hours into minutes</title>
<link>https://news.jatlink.uk/12087</link>
<guid>https://news.jatlink.uk/12087</guid>
<description><![CDATA[ In this post, we explore how the Amazon Quick document and visualization creation capabilities work, what you can build with them, and how professionals across roles are using them to reclaim hours of their workweek. From technical execution to strategic judgment Most professional roles carry an unspoken assumption that a significant portion of your time […] ]]></description>
<enclosure url="http://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/05/21/20925.png" length="49398" type="image/jpeg"/>
<pubDate>Tue, 26 May 2026 17:00:11 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Transforming, professional, work:, How, Amazon, Quick, turns, document, creation, from, hours, into, minutes</media:keywords>
</item>

<item>
<title>Amazon Nova Act is now HIPAA eligible</title>
<link>https://news.jatlink.uk/11753</link>
<guid>https://news.jatlink.uk/11753</guid>
<description><![CDATA[ In this post, you will learn what Nova Act offers, how HIPAA eligibility applies to agentic AI, and how to get started. ]]></description>
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<pubDate>Fri, 22 May 2026 01:00:15 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Amazon, Nova, Act, now, HIPAA, eligible</media:keywords>
</item>

<item>
<title>Build an AI&amp;powered recruitment assistant using Amazon Bedrock</title>
<link>https://news.jatlink.uk/11738</link>
<guid>https://news.jatlink.uk/11738</guid>
<description><![CDATA[ In this post, we demonstrate how to build an AI-powered recruitment assistant using Amazon Bedrock that brings efficiencies to candidate evaluation, generates personalized interview questions, and provides data-driven insights for human hiring decisions. This post presents a reference architecture for learning purposes — not a production-ready solution. Amazon Bedrock and the AWS services used here are general-purpose tools that customers can combine to support a wide variety of use cases, including recruitment workflows. The architecture demonstrates one possible approach; customers should adapt it to their specific requirements. ]]></description>
<enclosure url="http://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/05/21/18419.png" length="49398" type="image/jpeg"/>
<pubDate>Thu, 21 May 2026 21:00:11 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, AI-powered, recruitment, assistant, using, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Building multi&amp;tenant agents with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/11735</link>
<guid>https://news.jatlink.uk/11735</guid>
<description><![CDATA[ This post explores design considerations for architecting multi-tenant agentic applications and the framework needed to address SaaS architecture challenges with Amazon Bedrock AgentCore. ]]></description>
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<pubDate>Thu, 21 May 2026 21:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, multi-tenant, agents, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Build AI agents for business intelligence with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/11737</link>
<guid>https://news.jatlink.uk/11737</guid>
<description><![CDATA[ In this post, we show you how OPLOG developed three AI agents using the Strands Agents SDK, deployed them to Amazon Bedrock AgentCore, and integrated Amazon Bedrock with Anthropic’s Claude Sonnet and Amazon Bedrock Knowledge Bases for Retrieval Augmented Generation (RAG). ]]></description>
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<pubDate>Thu, 21 May 2026 21:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, agents, for, business, intelligence, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Intelligent radiology workflow optimization with AI agents</title>
<link>https://news.jatlink.uk/11733</link>
<guid>https://news.jatlink.uk/11733</guid>
<description><![CDATA[ Many healthcare organizations report that traditional worklist systems rely on rigid rules that ignore critical context, radiologist specialization, current workload, fatigue levels, and case complexity. This creates a persistent challenge: radiologists cherry-pick easier, higher-value cases while avoiding complex studies, leading to diagnostic delays and increased costs. Research across 62 hospitals analyzing 2.2 million studies found […] ]]></description>
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<pubDate>Thu, 21 May 2026 21:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Intelligent, radiology, workflow, optimization, with, agents</media:keywords>
</item>

<item>
<title>Integrating AWS API MCP Server with Amazon Quick using Amazon Bedrock AgentCore Runtime</title>
<link>https://news.jatlink.uk/11734</link>
<guid>https://news.jatlink.uk/11734</guid>
<description><![CDATA[ This post shows you how to use Amazon Bedrock AgentCore Runtime with Model Context Protocol (MCP) support to connect Amazon Quick with AWS services through the AWS API MCP Server, creating a conversational AI assistant that translates natural language into AWS Command Line Interface (AWS CLI) commands, without the need to switch between tools during critical moments. ]]></description>
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<pubDate>Thu, 21 May 2026 21:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Integrating, AWS, API, MCP, Server, with, Amazon, Quick, using, Amazon, Bedrock, AgentCore, Runtime</media:keywords>
</item>

<item>
<title>Break the context window barrier with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/11736</link>
<guid>https://news.jatlink.uk/11736</guid>
<description><![CDATA[ In this post, you will learn how to implement Recursive Language Models (RLM) using Amazon Bedrock AgentCore Code Interpreter and the Strands Agents SDK. By the end, you will know how to process documents of varying lengths, with no upper bound on context size, use Bedrock AgentCore Code Interpreter as persistent working memory for iterative document analysis, and orchestrate sub-large language model (sub-LLM) calls from within a sandboxed Python environment to analyze specific document sections. ]]></description>
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<pubDate>Thu, 21 May 2026 21:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Break, the, context, window, barrier, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Build AI&amp;powered dashboard automation agents with NLP on Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/11712</link>
<guid>https://news.jatlink.uk/11712</guid>
<description><![CDATA[ This solution combines the power of Amazon Bedrock AgentCore, Strands Agents, and Amazon Quick transforms to deliver a secure, scalable, and intelligent system for building and operating AI agents while transforming data into actionable business insights. ]]></description>
<enclosure url="http://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/05/20/image-35.png" length="49398" type="image/jpeg"/>
<pubDate>Thu, 21 May 2026 17:00:11 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, AI-powered, dashboard, automation, agents, with, NLP, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Announcing OpenAI&amp;compatible API support for Amazon SageMaker AI endpoints</title>
<link>https://news.jatlink.uk/11680</link>
<guid>https://news.jatlink.uk/11680</guid>
<description><![CDATA[ Today, Amazon SageMaker AI introduces OpenAI-compatible API support for real-time inference endpoints. If you use the OpenAI SDK, LangChain, or Strands Agents, you can now invoke models on SageMaker AI by changing only your endpoint URL. You don’t need a custom client, a SigV4 wrapper, or code rewrites. Overview With this launch, SageMaker AI endpoints […] ]]></description>
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<pubDate>Thu, 21 May 2026 05:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Announcing, OpenAI-compatible, API, support, for, Amazon, SageMaker, endpoints</media:keywords>
</item>

<item>
<title>Build real&amp;time voice applications with Amazon SageMaker AI and vLLM</title>
<link>https://news.jatlink.uk/11651</link>
<guid>https://news.jatlink.uk/11651</guid>
<description><![CDATA[ Voice agents, live captioning, contact center analytics, and accessibility tools all depend on real-time speech-to-text, where your application streams audio in and receives transcription back simultaneously over a single persistent connection. Traditional request-response inference falls short here because transcription cannot begin until the entire audio recording has been received, adding latency that breaks the real-time […] ]]></description>
<enclosure url="http://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/05/20/20779.png" length="49398" type="image/jpeg"/>
<pubDate>Wed, 20 May 2026 21:00:13 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, real-time, voice, applications, with, Amazon, SageMaker, and, vLLM</media:keywords>
</item>

<item>
<title>Multimodal evaluators: MLLM&amp;as&amp;a&amp;judge for image&amp;to&amp;text tasks in Strands Evals</title>
<link>https://news.jatlink.uk/11650</link>
<guid>https://news.jatlink.uk/11650</guid>
<description><![CDATA[ If you’re building visual shopping, image or document understanding, or chart analysis, you need a way to verify whether your model’s response is actually grounded in the source image. A text-only evaluator cannot tell you whether a caption faithfully describes an image, whether an extracted invoice total matches the document, or whether a screen summary […] ]]></description>
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<pubDate>Wed, 20 May 2026 21:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Multimodal, evaluators:, MLLM-as-a-judge, for, image-to-text, tasks, Strands, Evals</media:keywords>
</item>

<item>
<title>Implementing programmatic tool calling on Amazon Bedrock</title>
<link>https://news.jatlink.uk/11564</link>
<guid>https://news.jatlink.uk/11564</guid>
<description><![CDATA[ In this post, we show three ways to implement Programmatic tool calling (PTC) on Amazon Bedrock: a self-hosted Docker sandbox on ECS for maximum control, a managed solution using Amazon Bedrock AgentCore Code Interpreter, and an Anthropic SDK-compatible path through a proxy for teams that prefer that developer experience. ]]></description>
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<pubDate>Tue, 19 May 2026 21:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Implementing, programmatic, tool, calling, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Scalable voice agent design with Amazon Nova Sonic: multi&amp;agent, tools, and session segmentation</title>
<link>https://news.jatlink.uk/11561</link>
<guid>https://news.jatlink.uk/11561</guid>
<description><![CDATA[ In this post, you’ll learn how to use Amazon Nova Sonic, Amazon Bedrock AgentCore, and Strands BidiAgent to build scalable, maintainable voice agents that handle these challenges efficiently, resulting in more responsive and intelligent customer interactions. We’ll explore three popular architectural patterns for voice agents, highlighting their trade-offs and best practices for minimizing latency. ]]></description>
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<pubDate>Tue, 19 May 2026 21:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Scalable, voice, agent, design, with, Amazon, Nova, Sonic:, multi-agent, tools, and, session, segmentation</media:keywords>
</item>

<item>
<title>Extending conversational memory in Kiro CLI using Amazon Bedrock AgentCore Memory</title>
<link>https://news.jatlink.uk/11562</link>
<guid>https://news.jatlink.uk/11562</guid>
<description><![CDATA[ In this post, we demonstrate how you can extend the conversational memory of Kiro CLI by implementing a custom Model Context Protocol (MCP) server that integrates with Amazon Bedrock AgentCore Memory. You can use Kiro CLI to interact with AI agents of Kiro directly from your terminal. Amazon Bedrock AgentCore Memory is a fully managed service that allows AI agents to retain information from past interactions, creating more intelligent and context-aware conversations. By implementing a custom MCP server, you can provide Kiro CLI with tools to store and retrieve conversation context, monitor memory usage, and manage the underlying Bedrock Agent Core Memory infrastructure. ]]></description>
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<pubDate>Tue, 19 May 2026 21:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Extending, conversational, memory, Kiro, CLI, using, Amazon, Bedrock, AgentCore, Memory</media:keywords>
</item>

<item>
<title>Accelerate ML feature pipelines with new capabilities in Amazon SageMaker Feature Store</title>
<link>https://news.jatlink.uk/11563</link>
<guid>https://news.jatlink.uk/11563</guid>
<description><![CDATA[ Today, we’re announcing three new capabilities available in SageMaker Python SDK v3.8.0. In this post, we walk through each capability with code examples you can use to get started. For complete end-to-end walkthroughs, see the accompanying notebooks for Lake Formation governance and Iceberg table properties in the SageMaker Python SDK repository. ]]></description>
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<pubDate>Tue, 19 May 2026 21:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerate, feature, pipelines, with, new, capabilities, Amazon, SageMaker, Feature, Store</media:keywords>
</item>

<item>
<title>Prompting Amazon Nova 2 for content moderation</title>
<link>https://news.jatlink.uk/11497</link>
<guid>https://news.jatlink.uk/11497</guid>
<description><![CDATA[ In this post, you learn how to prompt Amazon Nova 2 Lite for content moderation using structured and free-form approaches, grounded in the MLCommons AILuminate Assessment Standard. The prompting techniques use the AILuminate taxonomy as an example, but they work equally well with your own custom moderation policy. You can swap in your own category definitions and the prompt structure stays the same. We also benchmark the content moderation capabilities of Amazon Nova 2 Lite against several foundation models (FMs) on three public datasets. ]]></description>
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<pubDate>Mon, 18 May 2026 23:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Prompting, Amazon, Nova, for, content, moderation</media:keywords>
</item>

<item>
<title>Aderant transforms cloud operations with Amazon Quick</title>
<link>https://news.jatlink.uk/11483</link>
<guid>https://news.jatlink.uk/11483</guid>
<description><![CDATA[ In this post, we share how Aderant used the AI-powered capabilities of Amazon Quick to unify search across six vendor systems and automate documentation workflows, achieving 90 percent faster search times and 75 percent documentation acceleration, and how others can apply these approaches to their operations. ]]></description>
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<pubDate>Mon, 18 May 2026 19:00:14 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Aderant, transforms, cloud, operations, with, Amazon, Quick</media:keywords>
</item>

<item>
<title>Integrate Atlassian Confluence Cloud with Amazon Quick</title>
<link>https://news.jatlink.uk/11484</link>
<guid>https://news.jatlink.uk/11484</guid>
<description><![CDATA[ In this post, you will learn how to set up the Confluence Cloud integration with Quick. This includes creating a knowledge base for semantic search, setting up Actions to query and manage Confluence pages, and organizing resources in Quick Spaces. Quick integrates with your current enterprise technology stack, from internal knowledge repositories and corporate intranets to business-critical applications and AWS data services. ]]></description>
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<pubDate>Mon, 18 May 2026 19:00:14 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Integrate, Atlassian, Confluence, Cloud, with, Amazon, Quick</media:keywords>
</item>

<item>
<title>Build custom code&amp;based evaluators in Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/11485</link>
<guid>https://news.jatlink.uk/11485</guid>
<description><![CDATA[ In this post, you will implement four Lambda-based custom code evaluators for a financial market-intelligence agent, register each with AgentCore, and run them in on-demand and online modes. You will also see how to combine custom code-based evaluators with built-in evaluators and how to call other AWS services for grounded fact-checking, PII detection, and real-time alerting. ]]></description>
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<pubDate>Mon, 18 May 2026 19:00:14 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, custom, code-based, evaluators, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Restrict access to sensitive documents in your Amazon Quick knowledge bases for Amazon S3</title>
<link>https://news.jatlink.uk/11296</link>
<guid>https://news.jatlink.uk/11296</guid>
<description><![CDATA[ In this post, we walk through how to configure document-level ACLs for your S3 knowledge base in Amazon Quick. You will learn how to set up and verify an ACL configuration that enforces document-level permissions across chat and automated workflows. ]]></description>
<enclosure url="http://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/05/15/ml-20551.png" length="49398" type="image/jpeg"/>
<pubDate>Fri, 15 May 2026 19:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Restrict, access, sensitive, documents, your, Amazon, Quick, knowledge, bases, for, Amazon</media:keywords>
</item>

<item>
<title>From siloed data to unified insights: Cross&amp;account Athena Access for Amazon Quick</title>
<link>https://news.jatlink.uk/11214</link>
<guid>https://news.jatlink.uk/11214</guid>
<description><![CDATA[ Today, we&#039;re announcing cross-account Athena access for Amazon Quick. With this feature, customers can query Athena data in other AWS accounts using AWS Identity and Access Management (IAM) role chaining, with query costs billed to the account where the data resides. ]]></description>
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<pubDate>Thu, 14 May 2026 19:00:13 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>From, siloed, data, unified, insights:, Cross-account, Athena, Access, for, Amazon, Quick</media:keywords>
</item>

<item>
<title>Control where your AI agents can browse with Chrome enterprise policies on Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/11215</link>
<guid>https://news.jatlink.uk/11215</guid>
<description><![CDATA[ In this post, you will configure Chrome enterprise policies to restrict a browser agent to a specific website, observe the policy enforcement through session recording, and demonstrate custom root CA certificates using a public test site. The walkthrough produces a working solution that researches Amazon Bedrock AgentCore documentation while operating under enterprise browser restrictions. ]]></description>
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<pubDate>Thu, 14 May 2026 19:00:13 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Control, where, your, agents, can, browse, with, Chrome, enterprise, policies, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Improve bot accuracy with Amazon Lex Assisted NLU</title>
<link>https://news.jatlink.uk/11212</link>
<guid>https://news.jatlink.uk/11212</guid>
<description><![CDATA[ In this post, you will learn how to implement Assisted NLU effectively. You will learn how to improve your bot design with effective intent and slot descriptions, validate your implementation using Test Workbench, and plan your transition from traditional NLU to Assisted NLU for both new and existing bots. ]]></description>
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<pubDate>Thu, 14 May 2026 19:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Improve, bot, accuracy, with, Amazon, Lex, Assisted, NLU</media:keywords>
</item>

<item>
<title>Real&amp;time voice agents with Stream Vision Agents and Amazon Nova 2 Sonic</title>
<link>https://news.jatlink.uk/11213</link>
<guid>https://news.jatlink.uk/11213</guid>
<description><![CDATA[ In this post, you learn how to combine Stream&#039;s Vision Agents open-source framework with Amazon Bedrock and Amazon Nova 2 Sonic to build real-time voice agents that can be production-ready in minutes. You&#039;ll learn how the integration works under the hood, walk through code examples, and explore advanced capabilities like function calling, automatic reconnection, and multilingual voice support. ]]></description>
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<pubDate>Thu, 14 May 2026 19:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Real-time, voice, agents, with, Stream, Vision, Agents, and, Amazon, Nova, Sonic</media:keywords>
</item>

<item>
<title>Build financial document processing with Pulse AI and Amazon Bedrock</title>
<link>https://news.jatlink.uk/11138</link>
<guid>https://news.jatlink.uk/11138</guid>
<description><![CDATA[ This post demonstrates how to build a documentation extraction and model fine-tuning pipeline that addresses challenges when processing the complex financial documents. By combining Pulse AI&#039;s advanced document understanding capabilities with the powerful AI services of Amazon Bedrock, organizations can achieve enterprise-grade accuracy and extract contextually relevant financial insights at scale. ]]></description>
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<pubDate>Wed, 13 May 2026 23:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, financial, document, processing, with, Pulse, and, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Fine&amp;tune LLM with Databricks Unity Catalog and Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/11120</link>
<guid>https://news.jatlink.uk/11120</guid>
<description><![CDATA[ In this post, we demonstrate how to build a secure, complete LLM fine-tuning workflow that integrates Unity Catalog with Amazon SageMaker AI using Amazon EMR Serverless for preprocessing. The solution shows how to securely access governed data, maintain lineage across services, fine-tune the Ministral-3-3B-Instruct model, and register trained artifacts back into Unity Catalog. With this approach, you can continue using your existing services while preserving central governance, tracking data lineage without compromising security or compliance requirements. ]]></description>
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<pubDate>Wed, 13 May 2026 19:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Fine-tune, LLM, with, Databricks, Unity, Catalog, and, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Build real&amp;time voice streaming applications with Amazon Nova Sonic and WebRTC</title>
<link>https://news.jatlink.uk/11118</link>
<guid>https://news.jatlink.uk/11118</guid>
<description><![CDATA[ Building end-to-end live streaming applications with real-time voice interaction presents several challenges. This post introduces a solution based on Amazon Nova 2 Sonic (Nova Sonic) and Amazon Kinesis Video Streams WebRTC (WebRTC) that addresses these challenges. In this post, we’ll walk through the solution architecture, implementation patterns, and two real-world scenario examples. ]]></description>
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<pubDate>Wed, 13 May 2026 19:00:11 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, real-time, voice, streaming, applications, with, Amazon, Nova, Sonic, and, WebRTC</media:keywords>
</item>

<item>
<title>Securing AI agents: How AWS and Cisco AI Defense scale MCP and A2A deployments</title>
<link>https://news.jatlink.uk/11119</link>
<guid>https://news.jatlink.uk/11119</guid>
<description><![CDATA[ The Cisco and AWS partnership addresses three challenges enterprises face when scaling AI agents: visibility gaps, security bottlenecks, and compliance risks. In this post, we explore how you can overcome AI security challenges through automated scanning and unified governance. ]]></description>
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<pubDate>Wed, 13 May 2026 19:00:11 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Securing, agents:, How, AWS, and, Cisco, Defense, scale, MCP, and, A2A, deployments</media:keywords>
</item>

<item>
<title>How Amazon Finance streamlines regulatory inquiries by using generative AI on AWS</title>
<link>https://news.jatlink.uk/11040</link>
<guid>https://news.jatlink.uk/11040</guid>
<description><![CDATA[ In this post, we demonstrate how Amazon FinTech teams are using Amazon Bedrock and other AWS services to build a scalable AI application to transform how regulatory inquiries are handled. Each team using this solution creates and maintains its own dedicated knowledge base, populated with that team&#039;s specific documents and reference materials. ]]></description>
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<pubDate>Tue, 12 May 2026 19:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Amazon, Finance, streamlines, regulatory, inquiries, using, generative, AWS</media:keywords>
</item>

<item>
<title>Automate schema generation for intelligent document processing</title>
<link>https://news.jatlink.uk/11041</link>
<guid>https://news.jatlink.uk/11041</guid>
<description><![CDATA[ In this post, we&#039;ll show you how our multi-document discovery feature solves this problem. It serves as an automated pre-processing step, analyzing unknown documents, clustering them by type, and generating schemas ready for the IDP Accelerator. You&#039;ll learn how the new capability uses visual embeddings for automatic clustering and agents for schema generation. We&#039;ll also walk you through running the solution on your own document collections. ]]></description>
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<pubDate>Tue, 12 May 2026 19:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Automate, schema, generation, for, intelligent, document, processing</media:keywords>
</item>

<item>
<title>Navigating EU AI Act requirements for LLM fine&amp;tuning on Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/11042</link>
<guid>https://news.jatlink.uk/11042</guid>
<description><![CDATA[ In this post, we show you how to set up FLOPs tracking during LLM fine-tuning using the open source Fine-Tuning FLOPs Meter toolkit on Amazon SageMaker AI. You learn how to determine your compliance status with a single configuration flag and generate audit-ready documentation. ]]></description>
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<pubDate>Tue, 12 May 2026 19:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Navigating, Act, requirements, for, LLM, fine-tuning, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Building web search&amp;enabled agents with Strands and Exa</title>
<link>https://news.jatlink.uk/10978</link>
<guid>https://news.jatlink.uk/10978</guid>
<description><![CDATA[ In this post, you will learn how to set up the Exa integration in Strands Agents, understand the two core tools it exposes, and walk through real-world use cases that show how agents use web search to complete multi-step tasks. ]]></description>
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<pubDate>Mon, 11 May 2026 23:00:13 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, web, search-enabled, agents, with, Strands, and, Exa</media:keywords>
</item>

<item>
<title>Introducing Claude Platform on AWS: Anthropic’s native platform, through your AWS account</title>
<link>https://news.jatlink.uk/10979</link>
<guid>https://news.jatlink.uk/10979</guid>
<description><![CDATA[ Today, we&#039;re excited to announce the general availability of Claude Platform on AWS. Claude Platform on AWS is a new service that gives customers direct access to Anthropic&#039;s native Claude Platform experience through their AWS account, with no separate credentials, contracts, or billing relationships required. AWS is the first cloud provider to offer access to the native Claude Platform experience. In this post, we explore how Claude Platform on AWS works and how you can start using it today. ]]></description>
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<pubDate>Mon, 11 May 2026 23:00:13 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Claude, Platform, AWS:, Anthropic’s, native, platform, through, your, AWS, account</media:keywords>
</item>

<item>
<title>Manufacturing intelligence with Amazon Nova Multimodal Embeddings</title>
<link>https://news.jatlink.uk/10961</link>
<guid>https://news.jatlink.uk/10961</guid>
<description><![CDATA[ In this post, we build a multimodal retrieval system for aerospace manufacturing documents using Amazon Nova Multimodal Embeddings on Amazon Bedrock and Amazon S3 Vectors. We evaluate the system on 26 manufacturing queries and compare generation quality between a text-only pipeline and the multimodal pipeline. ]]></description>
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<pubDate>Mon, 11 May 2026 19:00:11 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Manufacturing, intelligence, with, Amazon, Nova, Multimodal, Embeddings</media:keywords>
</item>

<item>
<title>How Miro uses Amazon Bedrock to boost software bug routing accuracy and improve time&amp;to&amp;resolution from days to hours</title>
<link>https://news.jatlink.uk/10962</link>
<guid>https://news.jatlink.uk/10962</guid>
<description><![CDATA[ In this post, we dive deep into the architecture and techniques we used to improve Miro’s bug routing, achieving six times fewer team reassignments and five times shorter time-to-resolution powered by Amazon Bedrock. ]]></description>
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<pubDate>Mon, 11 May 2026 19:00:11 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Miro, uses, Amazon, Bedrock, boost, software, bug, routing, accuracy, and, improve, time-to-resolution, from, days, hours</media:keywords>
</item>

<item>
<title>Amazon Quick: Accelerating the path from enterprise data to AI&amp;powered decisions</title>
<link>https://news.jatlink.uk/10963</link>
<guid>https://news.jatlink.uk/10963</guid>
<description><![CDATA[ Amazon Quick helps turn your large enterprise data into fast and accurate AI-powered decisions. In this post, you will learn about five new capabilities of Amazon Quick that accelerate how data professionals deliver trusted AI-powered insights at enterprise scale. ]]></description>
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<pubDate>Mon, 11 May 2026 19:00:11 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Amazon, Quick:, Accelerating, the, path, from, enterprise, data, AI-powered, decisions</media:keywords>
</item>

<item>
<title>Halliburton enhances seismic workflow creation with Amazon Bedrock and Generative AI</title>
<link>https://news.jatlink.uk/10747</link>
<guid>https://news.jatlink.uk/10747</guid>
<description><![CDATA[ In this post, we&#039;ll explore how we built a proof-of-concept that converts natural language queries into executable seismic workflows while providing a question-answering capability for Halliburton&#039;s Seismic Engine tools and documentation. We&#039;ll cover the technical details of the solution, share evaluation results showing workflow acceleration of up to 95%, and discuss key learnings that can help other organizations enhance their complex technical workflows with generative AI. ]]></description>
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<pubDate>Fri, 08 May 2026 15:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Halliburton, enhances, seismic, workflow, creation, with, Amazon, Bedrock, and, Generative</media:keywords>
</item>

<item>
<title>Secure short&amp;term GPU capacity for ML workloads with EC2 Capacity Blocks for ML and SageMaker training plans</title>
<link>https://news.jatlink.uk/10679</link>
<guid>https://news.jatlink.uk/10679</guid>
<description><![CDATA[ In this post, you will learn how to secure reserved GPU capacity for short-term workloads using Amazon Elastic Compute Cloud (Amazon EC2) Capacity Blocks for ML and Amazon SageMaker training plans. These solutions can address GPU availability challenges when you need short-term capacity for load testing, model validation, time-bound workshops, or preparing inference capacity ahead of a release. ]]></description>
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<pubDate>Thu, 07 May 2026 19:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Secure, short-term, GPU, capacity, for, workloads, with, EC2, Capacity, Blocks, for, and, SageMaker, training, plans</media:keywords>
</item>

<item>
<title>Overcoming reward signal challenges: Verifiable rewards&amp;based reinforcement learning with GRPO on SageMaker AI</title>
<link>https://news.jatlink.uk/10680</link>
<guid>https://news.jatlink.uk/10680</guid>
<description><![CDATA[ In this post, you will learn how to implement reinforcement learning with verifiable rewards (RLVR) to introduce verification and transparency into reward signals to improve training performance. This approach works best when outputs can be objectively verified for correctness, such as in mathematical reasoning, code generation, or symbolic manipulation tasks. You will also learn how to layer techniques like Group Relative Policy Optimization (GRPO) and few-shot examples to further improve results. You’ll use the GSM8K dataset (Grade School Math 8K: a collection of grade school math problems) to improve math problem solving accuracy, but the techniques used here can be adapted to a wide variety of other use cases. ]]></description>
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<pubDate>Thu, 07 May 2026 19:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Overcoming, reward, signal, challenges:, Verifiable, rewards-based, reinforcement, learning, with, GRPO, SageMaker</media:keywords>
</item>

<item>
<title>Agents that transact: Introducing Amazon Bedrock AgentCore Payments, built with Coinbase and Stripe</title>
<link>https://news.jatlink.uk/10657</link>
<guid>https://news.jatlink.uk/10657</guid>
<description><![CDATA[ Today, we&#039;re announcing a preview of Amazon Bedrock AgentCore Payments, a new set of features in Amazon Bedrock AgentCore that enables AI agents to instantly access and pay for what they use. AgentCore Payments was developed in partnership with Coinbase and Stripe. ]]></description>
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<pubDate>Thu, 07 May 2026 15:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Agents, that, transact:, Introducing, Amazon, Bedrock, AgentCore, Payments, built, with, Coinbase, and, Stripe</media:keywords>
</item>

<item>
<title>Cost effective deployment of vision&amp;language models for pet behavior detection on AWS Inferentia2</title>
<link>https://news.jatlink.uk/10592</link>
<guid>https://news.jatlink.uk/10592</guid>
<description><![CDATA[ Tomofun, the Taiwan-headquartered pet-tech startup behind the Furbo Pet Camera, is redefining how pet owners interact with their pets remotely. To reduce costs and maintain accuracy, Tomofun turned to EC2 Inf2 instances powered by AWS Inferentia2, the Amazon purpose-built AI chips. In this post, we walk through the following sections in detail. ]]></description>
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<pubDate>Wed, 06 May 2026 19:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Cost, effective, deployment, vision-language, models, for, pet, behavior, detection, AWS, Inferentia2</media:keywords>
</item>

<item>
<title>Introducing agent quality optimization in AgentCore, now in preview</title>
<link>https://news.jatlink.uk/10520</link>
<guid>https://news.jatlink.uk/10520</guid>
<description><![CDATA[ Generate recommendations from production traces, validate them with batch evaluation and A/B testing, and ship with confidence. AI agents that perform well at launch don’t stay that way. As models evolve, user behavior shifts, and prompts get reused in new contexts they were never designed for. Agent quality quietly degrades. In most teams, the improvement […] ]]></description>
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<pubDate>Tue, 05 May 2026 23:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, agent, quality, optimization, AgentCore, now, preview</media:keywords>
</item>

<item>
<title>How Hapag&amp;Lloyd uses Amazon Bedrock to transform customer feedback into actionable insights</title>
<link>https://news.jatlink.uk/10502</link>
<guid>https://news.jatlink.uk/10502</guid>
<description><![CDATA[ Hapag-Lloyd&#039;s Digital Customer Experience and Engineering team, distributed between Hamburg and Gdańsk, drives digital innovation by developing and maintaining customer-facing web and mobile products. In this post, we walk you through our generative AI–powered feedback analysis solution built using Amazon Bedrock, Elasticsearch, and open-source frameworks like LangChain and LangGraph ]]></description>
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<pubDate>Tue, 05 May 2026 19:00:11 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Hapag-Lloyd, uses, Amazon, Bedrock, transform, customer, feedback, into, actionable, insights</media:keywords>
</item>

<item>
<title>Streamlining generative AI development with MLflow v3.10 on Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/10503</link>
<guid>https://news.jatlink.uk/10503</guid>
<description><![CDATA[ Today, we’re excited to announce that Amazon SageMaker AI MLflow Apps now support MLflow version 3.10, bringing enhanced capabilities for generative AI development and streamlined experiment tracking to your generative AI workflows. Building on the foundations established with Amazon SageMaker AI MLflow Apps, this latest version introduces powerful new features for observability, evaluation, and generative […] ]]></description>
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<pubDate>Tue, 05 May 2026 19:00:11 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Streamlining, generative, development, with, MLflow, v3.10, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Introducing OS Level Actions in Amazon Bedrock AgentCore Browser</title>
<link>https://news.jatlink.uk/10504</link>
<guid>https://news.jatlink.uk/10504</guid>
<description><![CDATA[ We’re announcing OS Level Actions for AgentCore Browser. This new capability unblocks these scenarios by exposing direct OS control through the InvokeBrowser API, so agents can interact with content visible on the screen, not only what&#039;s accessible through the browser&#039;s web layer. By combining full-desktop screenshots with mouse and keyboard control at the OS level, agents can observe native UI, reason about it, and act on it within the same session. This post walks through how OS Level Actions work, what actions are supported, and how to get started. ]]></description>
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<pubDate>Tue, 05 May 2026 19:00:11 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Level, Actions, Amazon, Bedrock, AgentCore, Browser</media:keywords>
</item>

<item>
<title>Secure AI agents with Amazon Bedrock AgentCore Identity on Amazon ECS</title>
<link>https://news.jatlink.uk/10505</link>
<guid>https://news.jatlink.uk/10505</guid>
<description><![CDATA[ AI agents in production require secure access to external services. Amazon Bedrock AgentCore Identity, available as a standalone service, secures how your AI agents access external services whether they run on compute platforms like Amazon ECS, Amazon EKS, AWS Lambda, or on-premises. This post implements Authorization Code Grant (3-legged OAuth) on Amazon ECS with secure session binding and scoped tokens. ]]></description>
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<pubDate>Tue, 05 May 2026 19:00:11 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Secure, agents, with, Amazon, Bedrock, AgentCore, Identity, Amazon, ECS</media:keywords>
</item>

<item>
<title>Intelligence&amp;driven message defense and insights using Amazon Bedrock</title>
<link>https://news.jatlink.uk/10506</link>
<guid>https://news.jatlink.uk/10506</guid>
<description><![CDATA[ In this post, you will learn how you can use Amazon Nova Foundation Models in Amazon Bedrock to apply generative AI techniques for both business protection and enhancement. You can identify obvious and disguised attempts at direct contact while gaining valuable insights into customer sentiment and service improvement opportunities. ]]></description>
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<pubDate>Tue, 05 May 2026 19:00:11 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Intelligence-driven, message, defense, and, insights, using, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Introducing the agent quality loop: AgentCore Optimization now in preview</title>
<link>https://news.jatlink.uk/10451</link>
<guid>https://news.jatlink.uk/10451</guid>
<description><![CDATA[ Generate recommendations from production traces, validate them with batch evaluation and A/B testing, and ship with confidence. AI agents that perform well at launch don’t stay that way. As models evolve, user behavior shifts, and prompts get reused in new contexts they were never designed for. Agent quality quietly degrades. In most teams, the improvement […] ]]></description>
<enclosure url="http://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/05/04/ml-20827.png" length="49398" type="image/jpeg"/>
<pubDate>Tue, 05 May 2026 03:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, the, agent, quality, loop:, AgentCore, Optimization, now, preview</media:keywords>
</item>

<item>
<title>Capacity&amp;aware inference: Automatic instance fallback for SageMaker AI endpoints</title>
<link>https://news.jatlink.uk/10420</link>
<guid>https://news.jatlink.uk/10420</guid>
<description><![CDATA[ Today, Amazon SageMaker AI introduces capacity aware instance pool for new and existing inference endpoints. You define a prioritized list of instance types, and SageMaker AI automatically works through your list whenever capacity is constrained at creation, during scale-out, and during scale-in. Your endpoint provisions on available AI Infrastructure without manual intervention. This capability is available for Single Model Endpoints, Inference Component-based endpoints, and Asynchronous Inference endpoints. ]]></description>
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<pubDate>Mon, 04 May 2026 19:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Capacity-aware, inference:, Automatic, instance, fallback, for, SageMaker, endpoints</media:keywords>
</item>

<item>
<title>Agent&amp;guided workflows to accelerate model customization in Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/10416</link>
<guid>https://news.jatlink.uk/10416</guid>
<description><![CDATA[ Amazon SageMaker AI now offers an agentic experience that changes this. Developers describe their use case using natural language, and the AI coding agent streamlines the entire journey, from use case definition and data preparation through technique selection, evaluation, and deployment. In this post, we walk you through the model customization lifecycle using SageMaker AI agent skills. ]]></description>
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<pubDate>Mon, 04 May 2026 19:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Agent-guided, workflows, accelerate, model, customization, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Generate dashboards from natural language prompts in Amazon Quick</title>
<link>https://news.jatlink.uk/10417</link>
<guid>https://news.jatlink.uk/10417</guid>
<description><![CDATA[ Building meaningful dashboards demands hours of manual setup, even for experienced BI professionals. Amazon Quick now generates complete multi-sheet dashboards from natural language prompts, taking you from one or more datasets to a production-ready analysis in minutes. Data analysts building recurring operations reports, program managers preparing a leadership review, or engineers exploring a new dataset can […] ]]></description>
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<pubDate>Mon, 04 May 2026 19:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Generate, dashboards, from, natural, language, prompts, Amazon, Quick</media:keywords>
</item>

<item>
<title>From data lake to AI&amp;ready analytics: Introducing new data source with S3 Tables in Amazon Quick</title>
<link>https://news.jatlink.uk/10418</link>
<guid>https://news.jatlink.uk/10418</guid>
<description><![CDATA[ Amazon Quick introduces Amazon S3 Tables (Apache Iceberg tables) as a new data source. With this feature, customers can directly query and visualize Apache Iceberg tables stored in an Amazon S3 table bucket without the need for intermediate data layers. In this post, we explored how Amazon Quick’s new Amazon S3 Tables data source enables near real-time analytics while streamlining modern data architectures. ]]></description>
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<pubDate>Mon, 04 May 2026 19:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>From, data, lake, AI-ready, analytics:, Introducing, new, data, source, with, Tables, Amazon, Quick</media:keywords>
</item>

<item>
<title>Introducing Dataset Q&amp;amp;A: Expanding natural language querying for structured datasets in Amazon Quick</title>
<link>https://news.jatlink.uk/10419</link>
<guid>https://news.jatlink.uk/10419</guid>
<description><![CDATA[ In this post, you learn how to get started with Dataset Q&amp;A, explore real-world use cases with hands-on examples, and discover advanced capabilities like auto-discovery across all your data assets and multi-dataset querying in a single conversation. ]]></description>
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<pubDate>Mon, 04 May 2026 19:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Dataset, Q&amp;A:, Expanding, natural, language, querying, for, structured, datasets, Amazon, Quick</media:keywords>
</item>

<item>
<title>Beyond BI: How the Dataset Q&amp;amp;A feature of Amazon Quick powers the next generation of data decisions</title>
<link>https://news.jatlink.uk/10414</link>
<guid>https://news.jatlink.uk/10414</guid>
<description><![CDATA[ Business leaders across industries rely on operational dashboards as the shared source of truth that their teams execute against daily. But dashboards are built to answer known questions. When teams need to explore further, ad-hoc, multi-dimensional, or unforeseen questions, they hit a bottleneck. They wait hours or days for BI teams to build new views […] ]]></description>
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<pubDate>Mon, 04 May 2026 19:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Beyond, BI:, How, the, Dataset, Q&amp;A, feature, Amazon, Quick, powers, the, next, generation, data, decisions</media:keywords>
</item>

<item>
<title>Introducing the agent performance loop: AgentCore Optimization now in preview</title>
<link>https://news.jatlink.uk/10415</link>
<guid>https://news.jatlink.uk/10415</guid>
<description><![CDATA[ Generate recommendations from production traces, validate them with batch evaluation and A/B testing, and ship with confidence. AI agents that perform well at launch don’t stay that way. As models evolve, user behavior shifts, and prompts get reused in new contexts they were never designed for. Agent quality quietly degrades. In most teams, the improvement […] ]]></description>
<enclosure url="http://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/05/04/20827.png" length="49398" type="image/jpeg"/>
<pubDate>Mon, 04 May 2026 19:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, the, agent, performance, loop:, AgentCore, Optimization, now, preview</media:keywords>
</item>

<item>
<title>AWS Transform now automates BI migration to Amazon Quick in days</title>
<link>https://news.jatlink.uk/10229</link>
<guid>https://news.jatlink.uk/10229</guid>
<description><![CDATA[ In this post, we walk through the full journey, from setting up your migration workspace in AWS Transform to subscribing to partner agents through AWS Marketplace to unlocking Amazon Quick capabilities that change how your organization consumes data. ]]></description>
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<pubDate>Fri, 01 May 2026 23:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>AWS, Transform, now, automates, migration, Amazon, Quick, days</media:keywords>
</item>

<item>
<title>Reinforcement fine&amp;tuning with LLM&amp;as&amp;a&amp;judge</title>
<link>https://news.jatlink.uk/10157</link>
<guid>https://news.jatlink.uk/10157</guid>
<description><![CDATA[ In this post, we take a deeper look at how RLAIF or RL with LLM-as-a-judge works with Amazon Nova models effectively. ]]></description>
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<pubDate>Thu, 30 Apr 2026 23:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Reinforcement, fine-tuning, with, LLM-as-a-judge</media:keywords>
</item>

<item>
<title>Sun Finance automates ID extraction and fraud detection with generative AI on AWS</title>
<link>https://news.jatlink.uk/10140</link>
<guid>https://news.jatlink.uk/10140</guid>
<description><![CDATA[ In this post, we show how Sun Finance used Amazon Bedrock, Amazon Textract, and Amazon Rekognition to build an AI-powered identity verification (IDV) pipeline. The solution improved extraction accuracy from 79.7% to 90.8%, cut per-document costs by 91%, and reduced processing time from up to 20 hours to under 5 seconds. You&#039;ll learn how combining specialized OCR with large language model (LLM) structuring outperformed using either tool alone. You&#039;ll also learn how to architect a serverless fraud detection system using vector similarity search. ]]></description>
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<pubDate>Thu, 30 Apr 2026 19:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Sun, Finance, automates, extraction, and, fraud, detection, with, generative, AWS</media:keywords>
</item>

<item>
<title>Unleashing Agentic AI Analytics on Amazon SageMaker with Amazon Athena and Amazon Quick</title>
<link>https://news.jatlink.uk/10141</link>
<guid>https://news.jatlink.uk/10141</guid>
<description><![CDATA[ This post demonstrates how agentic AI assistant from Amazon Quick transform data analytics into a self-service capability by using Amazon Simple Storage Service (Amazon S3) as a storage, Amazon SageMaker and AWS Glue for lakehouse, Amazon Athena for serverless SQL querying across multiple storage formats (S3 Table, Iceberg, and Parquet). ]]></description>
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<pubDate>Thu, 30 Apr 2026 19:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Unleashing, Agentic, Analytics, Amazon, SageMaker, with, Amazon, Athena, and, Amazon, Quick</media:keywords>
</item>

<item>
<title>Configuring Amazon Bedrock AgentCore Gateway for secure access to private resources</title>
<link>https://news.jatlink.uk/10142</link>
<guid>https://news.jatlink.uk/10142</guid>
<description><![CDATA[ In this post, you will configure Amazon Bedrock AgentCore Gateway to access private endpoints using Resource Gateway, a managed construct that provisions Elastic Network Interfaces (ENIs) directly inside your Amazon VPC, one per subnet. You will explore two implementation modes (managed and self-managed) and walk through three practical scenarios: connecting to a private Amazon API Gateway endpoint, integrating with a MCP server on Amazon Elastic Kubernetes Service (Amazon EKS), and accessing a private REST API. ]]></description>
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<pubDate>Thu, 30 Apr 2026 19:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Configuring, Amazon, Bedrock, AgentCore, Gateway, for, secure, access, private, resources</media:keywords>
</item>

<item>
<title>AWS Generative AI Model Agility Solution: A comprehensive guide to migrating LLMs for generative AI production</title>
<link>https://news.jatlink.uk/10139</link>
<guid>https://news.jatlink.uk/10139</guid>
<description><![CDATA[ In this post, we introduce a systematic framework for LLM migration or upgrade in generative AI production, encompassing essential tools, methodologies, and best practices. The framework facilitates transitions between different LLMs by providing robust protocols for prompt conversion and optimization. ]]></description>
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<pubDate>Thu, 30 Apr 2026 19:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>AWS, Generative, Model, Agility, Solution:, comprehensive, guide, migrating, LLMs, for, generative, production</media:keywords>
</item>

<item>
<title>Organizing Agents’ memory at scale: Namespace design patterns in AgentCore Memory</title>
<link>https://news.jatlink.uk/10067</link>
<guid>https://news.jatlink.uk/10067</guid>
<description><![CDATA[ In this post, you will learn how to design namespace hierarchies, choose the right retrieval patterns, and implement AWS Identity and Access Management (IAM)-based access control for AgentCore Memory. ]]></description>
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<pubDate>Wed, 29 Apr 2026 23:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Organizing, Agents’, memory, scale:, Namespace, design, patterns, AgentCore, Memory</media:keywords>
</item>

<item>
<title>Extracting contract insights with PwC’s AI&amp;driven annotation on AWS</title>
<link>https://news.jatlink.uk/10066</link>
<guid>https://news.jatlink.uk/10066</guid>
<description><![CDATA[ This post was co-written with Yash Munsadwala, Adam Hood, Justin Guse, and Hector Hernandez from PwC. Contract analysis often consumes significant time for legal, compliance, and procurement teams, especially when important insights are buried in lengthy, unstructured agreements. As contract volumes grow, finding specific clauses and assessing extracted terms can become increasingly difficult to scale. […] ]]></description>
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<pubDate>Wed, 29 Apr 2026 23:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Extracting, contract, insights, with, PwC’s, AI-driven, annotation, AWS</media:keywords>
</item>

<item>
<title>Building AI&amp;ready data: Vanguard’s Virtual Analyst journey</title>
<link>https://news.jatlink.uk/10036</link>
<guid>https://news.jatlink.uk/10036</guid>
<description><![CDATA[ In this post, you&#039;ll learn how Vanguard built their Virtual Analyst solution by focusing on eight guiding principles of AI-ready data, the AWS services that powered their implementation, and the measurable business outcomes they achieved. ]]></description>
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<pubDate>Wed, 29 Apr 2026 15:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, AI-ready, data:, Vanguard’s, Virtual, Analyst, journey</media:keywords>
</item>

<item>
<title>Run custom MCP proxies serverless on Amazon Bedrock AgentCore Runtime</title>
<link>https://news.jatlink.uk/10037</link>
<guid>https://news.jatlink.uk/10037</guid>
<description><![CDATA[ This post shows you how to deploy a serverless MCP proxy on Amazon Bedrock AgentCore Runtime that gives you a programmable layer to implement proper governance, controls, and observability aligned with an organization&#039;s security policies. ]]></description>
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<pubDate>Wed, 29 Apr 2026 15:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Run, custom, MCP, proxies, serverless, Amazon, Bedrock, AgentCore, Runtime</media:keywords>
</item>

<item>
<title>Migrating a text agent to a voice assistant with Amazon Nova 2 Sonic</title>
<link>https://news.jatlink.uk/9967</link>
<guid>https://news.jatlink.uk/9967</guid>
<description><![CDATA[ In this post, we explore what it takes to migrate a traditional text agent into a conversational voice assistant using Amazon Nova 2 Sonic. We compare text and voice agent requirements, highlight design priorities for different use cases, break down agent architecture, and address common concerns like tools and sub-agents for reuse and system prompt adaptation. This post helps you navigate the migration process and avoid common pitfalls. ]]></description>
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<pubDate>Tue, 28 Apr 2026 19:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Migrating, text, agent, voice, assistant, with, Amazon, Nova, Sonic</media:keywords>
</item>

<item>
<title>NVIDIA Nemotron 3 Nano Omni model now available on Amazon SageMaker JumpStart</title>
<link>https://news.jatlink.uk/9968</link>
<guid>https://news.jatlink.uk/9968</guid>
<description><![CDATA[ Today, we are excited to announce the day zero availability of NVIDIA Nemotron 3 Nano Omni on Amazon SageMaker JumpStart. In this post, we walk through the model architecture and key capabilities of Nemotron 3 Nano Omni, explore the enterprise use cases it unlocks, and show you how to deploy and run inference using Amazon SageMaker JumpStart. ]]></description>
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<pubDate>Tue, 28 Apr 2026 19:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Nemotron, Nano, Omni, model, now, available, Amazon, SageMaker, JumpStart</media:keywords>
</item>

<item>
<title>Build Strands Agents with SageMaker AI models and MLflow</title>
<link>https://news.jatlink.uk/9888</link>
<guid>https://news.jatlink.uk/9888</guid>
<description><![CDATA[ In this post, we demonstrate how to build AI agents using Strands Agents SDK with models deployed on SageMaker AI endpoints. You will learn how to deploy foundation models from SageMaker JumpStart, integrate them with Strands Agents, and establish production-grade observability using SageMaker Serverless MLflow for agent tracing. We also cover how to implement A/B testing across multiple model variants and evaluate agent performance using MLflow metrics and show how you can build, deploy, and continuously improve AI agents on infrastructure you control. ]]></description>
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<pubDate>Mon, 27 Apr 2026 19:00:13 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, Strands, Agents, with, SageMaker, models, and, MLflow</media:keywords>
</item>

<item>
<title>How Popsa used Amazon Nova to inspire customers with personalised title suggestions</title>
<link>https://news.jatlink.uk/9889</link>
<guid>https://news.jatlink.uk/9889</guid>
<description><![CDATA[ In this post, we share how we applied Amazon Bedrock and the Amazon Nova family of models to reimagine our Title Suggestion feature. By combining metadata, computer vision, and retrieval-augmented generative AI, we now automatically generate creative, brand-aligned titles and subtitles across 12 languages. Using the unified API of Amazon Bedrock, Anthropic’s Claude 3 Haiku, and Amazon Nova Lite and Pro, we improved quality, reduced cost, and cut response times. This resulted in higher customer satisfaction, measurable uplifts in engagement and purchase rates, and over 5.5 million personalised titles generated in 2025. ]]></description>
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<pubDate>Mon, 27 Apr 2026 19:00:13 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Popsa, used, Amazon, Nova, inspire, customers, with, personalised, title, suggestions</media:keywords>
</item>

<item>
<title>Automate repetitive tasks with Amazon Quick Flows</title>
<link>https://news.jatlink.uk/9886</link>
<guid>https://news.jatlink.uk/9886</guid>
<description><![CDATA[ This post shows you how to build your first AI-powered workflow, using Amazon Quick, starting with a financial analysis tool and progressing to an advanced employee onboarding automation. ]]></description>
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<pubDate>Mon, 27 Apr 2026 19:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Automate, repetitive, tasks, with, Amazon, Quick, Flows</media:keywords>
</item>

<item>
<title>Build and deploy an automatic sync solution for Amazon Bedrock Knowledge Bases</title>
<link>https://news.jatlink.uk/9887</link>
<guid>https://news.jatlink.uk/9887</guid>
<description><![CDATA[ In this post, we explore an automated solution that detects S3 events and triggers ingestion jobs while respecting service quotas and providing comprehensive monitoring. This serverless solution uses an event-driven architecture to keep your knowledge base current without overwhelming the Amazon Bedrock APIs. ]]></description>
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<pubDate>Mon, 27 Apr 2026 19:00:12 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, and, deploy, automatic, sync, solution, for, Amazon, Bedrock, Knowledge, Bases</media:keywords>
</item>

<item>
<title>Building Workforce AI Agents with Visier and Amazon Quick</title>
<link>https://news.jatlink.uk/9698</link>
<guid>https://news.jatlink.uk/9698</guid>
<description><![CDATA[ In this post, we show how connecting the Visier Workforce AI platform with Amazon Quick through Model Context Protocol (MCP) gives every knowledge worker a unified agentic workspace to ask questions in. Visier helps ground the workspace in live workforce data and the organizational context that surrounds it while letting your users act on the conversational results without switching tools. ]]></description>
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<pubDate>Fri, 24 Apr 2026 23:00:11 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, Workforce, Agents, with, Visier, and, Amazon, Quick</media:keywords>
</item>

<item>
<title>Amazon Quick for marketing: From scattered data to strategic action</title>
<link>https://news.jatlink.uk/9596</link>
<guid>https://news.jatlink.uk/9596</guid>
<description><![CDATA[ Amazon Quick changes how you work. You can set it up in minutes and by the end of the day, you will wonder how you ever worked without it. Quick connects with your applications, tools, and data, creating a personal knowledge graph that learns your priorities, preferences, and network. ]]></description>
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<pubDate>Thu, 23 Apr 2026 19:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Amazon, Quick, for, marketing:, From, scattered, data, strategic, action</media:keywords>
</item>

<item>
<title>Applying multimodal biological foundation models across therapeutics and patient care</title>
<link>https://news.jatlink.uk/9597</link>
<guid>https://news.jatlink.uk/9597</guid>
<description><![CDATA[ In this post, we&#039;ll explore how multimodal BioFMs work, showcase real-world applications in drug discovery and clinical development, and contextualize how AWS enables organizations to build and deploy multimodal BioFMs. ]]></description>
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<pubDate>Thu, 23 Apr 2026 19:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Applying, multimodal, biological, foundation, models, across, therapeutics, and, patient, care</media:keywords>
</item>

<item>
<title>Cost&amp;effective multilingual audio transcription at scale with Parakeet&amp;TDT and AWS Batch</title>
<link>https://news.jatlink.uk/9530</link>
<guid>https://news.jatlink.uk/9530</guid>
<description><![CDATA[ In this post, we walk through building a scalable, event-driven transcription pipeline that automatically processes audio files uploaded to Amazon Simple Storage Service (Amazon S3), and show you how to use Amazon EC2 Spot Instances and buffered streaming inference to further reduce costs. ]]></description>
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<pubDate>Wed, 22 Apr 2026 23:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Cost-effective, multilingual, audio, transcription, scale, with, Parakeet-TDT, and, AWS, Batch</media:keywords>
</item>

<item>
<title>Amazon SageMaker AI now supports optimized generative AI inference recommendations</title>
<link>https://news.jatlink.uk/9531</link>
<guid>https://news.jatlink.uk/9531</guid>
<description><![CDATA[ Today, Amazon SageMaker AI  supports optimized generative AI inference recommendations. By delivering validated, optimal deployment configurations with performance metrics, Amazon SageMaker AI keeps your model developers focused on building accurate models, not managing infrastructure. ]]></description>
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<pubDate>Wed, 22 Apr 2026 23:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Amazon, SageMaker, now, supports, optimized, generative, inference, recommendations</media:keywords>
</item>

<item>
<title>Get to your first working agent in minutes: Announcing new features in Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/9532</link>
<guid>https://news.jatlink.uk/9532</guid>
<description><![CDATA[ Today, we&#039;re introducing new capabilities that further streamline the agent building experience, removing the infrastructure barriers that slow teams down at every stage of agent development from the first prototype through production deployment. ]]></description>
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<pubDate>Wed, 22 Apr 2026 23:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Get, your, first, working, agent, minutes:, Announcing, new, features, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Company&amp;wise memory in Amazon Bedrock with Amazon Neptune and Mem0</title>
<link>https://news.jatlink.uk/9516</link>
<guid>https://news.jatlink.uk/9516</guid>
<description><![CDATA[ Company-wise memory in Amazon Bedrock, powered by Amazon Neptune and Mem0, provides AI agents with persistent, company-specific context—enabling them to learn, adapt, and respond intelligently across multiple interactions. TrendMicro, one of the largest antivirus software companies in the world, developed the Trend’s Companion chatbot, so their customers can explore information through natural, conversational interactions ]]></description>
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<pubDate>Wed, 22 Apr 2026 19:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Company-wise, memory, Amazon, Bedrock, with, Amazon, Neptune, and, Mem0</media:keywords>
</item>

<item>
<title>From developer desks to the whole organization: Running Claude Cowork in Amazon Bedrock</title>
<link>https://news.jatlink.uk/9447</link>
<guid>https://news.jatlink.uk/9447</guid>
<description><![CDATA[ Today, we&#039;re excited to announce Claude Cowork in Amazon Bedrock. You can now run Cowork and Claude Code Desktop through Amazon Bedrock, directly or using an LLM gateway. In this post, we walk through how Claude Cowork integrates with Amazon Bedrock and show an example of how knowledge workers use it in practice. ]]></description>
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<pubDate>Tue, 21 Apr 2026 22:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>From, developer, desks, the, whole, organization:, Running, Claude, Cowork, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>End&amp;to&amp;end lineage with DVC and Amazon SageMaker AI MLflow apps</title>
<link>https://news.jatlink.uk/9433</link>
<guid>https://news.jatlink.uk/9433</guid>
<description><![CDATA[ In this post, we show how to combine DVC (Data Version Control), Amazon SageMaker AI, and Amazon SageMaker AI MLflow Apps to build end-to-end ML model lineage. We walk through two deployable patterns — dataset-level lineage and record-level lineage — that you can run in your own AWS account using the companion notebooks. ]]></description>
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<pubDate>Tue, 21 Apr 2026 18:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>End-to-end, lineage, with, DVC, and, Amazon, SageMaker, MLflow, apps</media:keywords>
</item>

<item>
<title>Accelerate Generative AI Inference on Amazon SageMaker AI with G7e Instances</title>
<link>https://news.jatlink.uk/9362</link>
<guid>https://news.jatlink.uk/9362</guid>
<description><![CDATA[ Today, we are thrilled to announce the availability of G7e instances powered by NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs on Amazon SageMaker AI. You can provision nodes with 1, 2, 4, and 8 RTX PRO 6000 GPU instances, with each GPU providing 96 GB of GDDR7 memory. This launch provides the capability to use a single-node GPU, G7e.2xlarge instance to host powerful open source foundation models (FMs) like GPT-OSS-120B, Nemotron-3-Super-120B-A12B (NVFP4 variant), and Qwen3.5-35B-A3B, offering organizations a cost-effective and high-performing option. ]]></description>
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<pubDate>Mon, 20 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerate, Generative, Inference, Amazon, SageMaker, with, G7e, Instances</media:keywords>
</item>

<item>
<title>ToolSimulator: scalable tool testing for AI agents</title>
<link>https://news.jatlink.uk/9363</link>
<guid>https://news.jatlink.uk/9363</guid>
<description><![CDATA[ You can use ToolSimulator, an LLM-powered tool simulation framework within Strands Evals, to thoroughly and safely test AI agents that rely on external tools, at scale. Instead of risking live API calls that expose personally identifiable information (PII), trigger unintended actions, or settling for static mocks that break with multi-turn workflows, you can use ToolSimulator&#039;s large language model (LLM)-powered simulations to validate your agents. Available today as part of the Strands Evals Software Development Kit (SDK), ToolSimulator helps you catch integration bugs early, test edge cases comprehensively, and ship production-ready agents with confidence. ]]></description>
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<pubDate>Mon, 20 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>ToolSimulator:, scalable, tool, testing, for, agents</media:keywords>
</item>

<item>
<title>Omnichannel ordering with Amazon Bedrock AgentCore and Amazon Nova 2 Sonic</title>
<link>https://news.jatlink.uk/9348</link>
<guid>https://news.jatlink.uk/9348</guid>
<description><![CDATA[ In this post, we&#039;ll show you how to build a complete omnichannel ordering system using Amazon Bedrock AgentCore, an agentic platform, to build, deploy, and operate highly effective AI agents securely at scale using any framework and foundation model and Amazon Nova 2 Sonic. ]]></description>
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<pubDate>Mon, 20 Apr 2026 18:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Omnichannel, ordering, with, Amazon, Bedrock, AgentCore, and, Amazon, Nova, Sonic</media:keywords>
</item>

<item>
<title>Introducing granular cost attribution for Amazon Bedrock</title>
<link>https://news.jatlink.uk/9154</link>
<guid>https://news.jatlink.uk/9154</guid>
<description><![CDATA[ In this post, we share how Amazon Bedrock&#039;s granular cost attribution works and walk through example cost tracking scenarios. ]]></description>
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<pubDate>Sat, 18 Apr 2026 02:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, granular, cost, attribution, for, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>From hours to minutes: How Agentic AI gave marketers time back for what matters</title>
<link>https://news.jatlink.uk/9139</link>
<guid>https://news.jatlink.uk/9139</guid>
<description><![CDATA[ In this post, we share how AWS Marketing’s Technology, AI, and Analytics (TAA) team worked with Gradial to build an agentic AI solution on Amazon Bedrock for accelerating content publishing workflows. ]]></description>
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<pubDate>Fri, 17 Apr 2026 22:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>From, hours, minutes:, How, Agentic, gave, marketers, time, back, for, what, matters</media:keywords>
</item>

<item>
<title>Optimize video semantic search intent with Amazon Nova Model Distillation on Amazon Bedrock</title>
<link>https://news.jatlink.uk/9136</link>
<guid>https://news.jatlink.uk/9136</guid>
<description><![CDATA[ In this post, we show you how to use Model Distillation, a model customization technique on Amazon Bedrock, to transfer routing intelligence from a large teacher model (Amazon Nova Premier) into a much smaller student model (Amazon Nova Micro). This approach cuts inference cost by over 95% and reduces latency by 50% while maintaining the nuanced routing quality that the task demands. ]]></description>
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<pubDate>Fri, 17 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Optimize, video, semantic, search, intent, with, Amazon, Nova, Model, Distillation, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Power video semantic search with Amazon Nova Multimodal Embeddings</title>
<link>https://news.jatlink.uk/9137</link>
<guid>https://news.jatlink.uk/9137</guid>
<description><![CDATA[ In this post, we show you how to build a video semantic search solution on Amazon Bedrock using Nova Multimodal Embeddings that intelligently understands user intent and retrieves accurate video results across all signal types simultaneously. We also share a reference implementation you can deploy and explore with your own content. ]]></description>
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<pubDate>Fri, 17 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Power, video, semantic, search, with, Amazon, Nova, Multimodal, Embeddings</media:keywords>
</item>

<item>
<title>Nova Forge SDK series part 2: Practical guide to fine&amp;tune Nova models using data mixing capabilities</title>
<link>https://news.jatlink.uk/9138</link>
<guid>https://news.jatlink.uk/9138</guid>
<description><![CDATA[ This hands-on guide walks through every step of fine-tuning an Amazon Nova model with the Amazon Nova Forge SDK, from data preparation to training with data mixing to evaluation, giving you a repeatable playbook you can adapt to your own use case. This is the second part in our Nova Forge SDK series, building on the SDK introduction and first part, which covered kicking off customization experiments. ]]></description>
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<pubDate>Fri, 17 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Nova, Forge, SDK, series, part, Practical, guide, fine-tune, Nova, models, using, data, mixing, capabilities</media:keywords>
</item>

<item>
<title>Cost&amp;efficient custom text&amp;to&amp;SQL using Amazon Nova Micro and Amazon Bedrock on&amp;demand inference</title>
<link>https://news.jatlink.uk/9049</link>
<guid>https://news.jatlink.uk/9049</guid>
<description><![CDATA[ In this post, we demonstrate two approaches to fine-tune Amazon Nova Micro for custom SQL dialect generation to deliver both cost efficiency and production ready performance. ]]></description>
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<pubDate>Thu, 16 Apr 2026 22:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Cost-efficient, custom, text-to-SQL, using, Amazon, Nova, Micro, and, Amazon, Bedrock, on-demand, inference</media:keywords>
</item>

<item>
<title>Transform retail with AWS generative AI services</title>
<link>https://news.jatlink.uk/9050</link>
<guid>https://news.jatlink.uk/9050</guid>
<description><![CDATA[ Online retailers face a persistent challenge: shoppers struggle to determine the fit and look when ordering online, leading to increased returns and decreased purchase confidence. The cost? Lost revenue, operational overhead, and customer frustration. Meanwhile, consumers increasingly expect immersive, interactive shopping experiences that bridge the gap between online and in-store retail. Retailers implementing virtual try-on […] ]]></description>
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<pubDate>Thu, 16 Apr 2026 22:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Transform, retail, with, AWS, generative, services</media:keywords>
</item>

<item>
<title>How Automated Reasoning checks in Amazon Bedrock transform generative AI compliance</title>
<link>https://news.jatlink.uk/9051</link>
<guid>https://news.jatlink.uk/9051</guid>
<description><![CDATA[ In this post, you&#039;ll learn why probabilistic AI validation falls short in regulated industries and how Automated Reasoning checks use formal verification to deliver mathematically proven results. You&#039;ll also see how customers across six industries use this technology to produce formally verified, auditable AI outputs, and how to get started. ]]></description>
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<pubDate>Thu, 16 Apr 2026 22:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Automated, Reasoning, checks, Amazon, Bedrock, transform, generative, compliance</media:keywords>
</item>

<item>
<title>Create rich, custom tooltips in Amazon Quick Sight</title>
<link>https://news.jatlink.uk/8936</link>
<guid>https://news.jatlink.uk/8936</guid>
<description><![CDATA[ Today, we&#039;re announcing sheet tooltips in Amazon Quick Sight. Dashboard authors can now design custom tooltip layouts using free-form layout sheets. These layouts combine charts, key performance indicator (KPI) metrics, text, and other visuals into a single tooltip that renders dynamically when readers hover over data points. ]]></description>
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<pubDate>Wed, 15 Apr 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Create, rich, custom, tooltips, Amazon, Quick, Sight</media:keywords>
</item>

<item>
<title>Accelerating decode&amp;heavy LLM inference with speculative decoding on AWS Trainium and vLLM</title>
<link>https://news.jatlink.uk/8937</link>
<guid>https://news.jatlink.uk/8937</guid>
<description><![CDATA[ In this post, you will learn how speculative decoding works and why it helps reduce cost per generated token on AWS Trainium2. ]]></description>
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<pubDate>Wed, 15 Apr 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerating, decode-heavy, LLM, inference, with, speculative, decoding, AWS, Trainium, and, vLLM</media:keywords>
</item>

<item>
<title>Rede Mater Dei de Saúde: Monitoring AI agents in the revenue cycle with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/8938</link>
<guid>https://news.jatlink.uk/8938</guid>
<description><![CDATA[ This post is cowritten by Renata Salvador Grande, Gabriel Bueno and Paulo Laurentys at Rede Mater Dei de Saúde. The growing adoption of multi-agent AI systems is redefining critical operations in healthcare. In large hospital networks, where thousands of decisions directly impact cash flow, service delivery times, and the risk of claim denials, the ability […] ]]></description>
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<pubDate>Wed, 15 Apr 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Rede, Mater, Dei, Saúde:, Monitoring, agents, the, revenue, cycle, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Navigating the generative AI journey: The Path&amp;to&amp;Value framework from AWS</title>
<link>https://news.jatlink.uk/8864</link>
<guid>https://news.jatlink.uk/8864</guid>
<description><![CDATA[ In this post, we introduce the Generative AI Path-to-Value (P2V) framework, a structured approach to help you move generative AI initiatives from concept to production and sustained value creation. ]]></description>
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<pubDate>Tue, 14 Apr 2026 22:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Navigating, the, generative, journey:, The, Path-to-Value, framework, from, AWS</media:keywords>
</item>

<item>
<title>Use&amp;case based deployments on SageMaker JumpStart</title>
<link>https://news.jatlink.uk/8865</link>
<guid>https://news.jatlink.uk/8865</guid>
<description><![CDATA[ We&#039;re excited to announce the launch of Amazon SageMaker JumpStart optimized deployments. SageMaker JumpStart improved deployments address the need for rich and straightforward deployment customization on SageMaker JumpStart by offering pre-defined deployment configurations, designed for specific use cases. Customers maintain the same level of visibility into the details of their proposed deployments, but now deployments are optimized for their specific use case and performance constraint. ]]></description>
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<pubDate>Tue, 14 Apr 2026 22:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Use-case, based, deployments, SageMaker, JumpStart</media:keywords>
</item>

<item>
<title>Best practices to run inference on Amazon SageMaker HyperPod</title>
<link>https://news.jatlink.uk/8866</link>
<guid>https://news.jatlink.uk/8866</guid>
<description><![CDATA[ This post explores how Amazon SageMaker HyperPod provides a comprehensive solution for inference workloads. We walk you through the platform’s key capabilities for dynamic scaling, simplified deployment, and intelligent resource management. By the end of this post, you’ll understand how to use the HyperPod automated infrastructure, cost optimization features, and performance enhancements to reduce your total cost of ownership by up to 40% while accelerating your generative AI deployments from concept to production. ]]></description>
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<pubDate>Tue, 14 Apr 2026 22:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Best, practices, run, inference, Amazon, SageMaker, HyperPod</media:keywords>
</item>

<item>
<title>How Guidesly built AI&amp;generated trip reports for outdoor guides on AWS</title>
<link>https://news.jatlink.uk/8867</link>
<guid>https://news.jatlink.uk/8867</guid>
<description><![CDATA[ In this post, we walk through how Guidesly built Jack AI on AWS using AWS Lambda, AWS Step Functions, Amazon Simple Storage Service (Amazon S3), Amazon Relational Database Service (Amazon RDS), Amazon SageMaker AI, and Amazon Bedrock to ingest trip media, enrich it with context, apply computer vision and generative AI, and publish marketing-ready content across multiple channels—securely, reliably, and at scale. ]]></description>
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<pubDate>Tue, 14 Apr 2026 22:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Guidesly, built, AI-generated, trip, reports, for, outdoor, guides, AWS</media:keywords>
</item>

<item>
<title>Spring AI SDK for Amazon Bedrock AgentCore is now Generally Available</title>
<link>https://news.jatlink.uk/8827</link>
<guid>https://news.jatlink.uk/8827</guid>
<description><![CDATA[ With the new Spring AI AgentCore SDK, you can build production-ready AI agents and run them on the highly scalable AgentCore Runtime. The Spring AI AgentCore SDK is an open source library that brings Amazon Bedrock AgentCore capabilities into Spring AI. In this post, we build an AI agent starting with a chat endpoint, then adding streaming responses, conversation memory, and tools for web browsing and code execution. ]]></description>
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<pubDate>Tue, 14 Apr 2026 14:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Spring, SDK, for, Amazon, Bedrock, AgentCore, now, Generally, Available</media:keywords>
</item>

<item>
<title>How to build effective reward functions with AWS Lambda for Amazon Nova model customization</title>
<link>https://news.jatlink.uk/8762</link>
<guid>https://news.jatlink.uk/8762</guid>
<description><![CDATA[ This post demonstrates how Lambda enables scalable, cost-effective reward functions for Amazon Nova customization. You&#039;ll learn to choose between Reinforcement Learning via Verifiable Rewards (RLVR) for objectively verifiable tasks and Reinforcement Learning via AI Feedback (RLAIF) for subjective evaluation, design multi-dimensional reward systems that help you prevent reward hacking, optimize Lambda functions for training scale, and monitor reward distributions with Amazon CloudWatch. Working code examples and deployment guidance are included to help you start experimenting. ]]></description>
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<pubDate>Mon, 13 Apr 2026 18:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, build, effective, reward, functions, with, AWS, Lambda, for, Amazon, Nova, model, customization</media:keywords>
</item>

<item>
<title>Understanding Amazon Bedrock model lifecycle</title>
<link>https://news.jatlink.uk/8471</link>
<guid>https://news.jatlink.uk/8471</guid>
<description><![CDATA[ This post shows you how to manage FM transitions in Amazon Bedrock, so you can make sure your AI applications remain operational as models evolve. We discuss the three lifecycle states, how to plan migrations with the new extended access feature, and practical strategies to transition your applications to newer models without disruption. ]]></description>
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<pubDate>Thu, 09 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Understanding, Amazon, Bedrock, model, lifecycle</media:keywords>
</item>

<item>
<title>The future of managing agents at scale: AWS Agent Registry now in preview</title>
<link>https://news.jatlink.uk/8472</link>
<guid>https://news.jatlink.uk/8472</guid>
<description><![CDATA[ Today, we&#039;re announcing AWS Agent Registry (preview) in AgentCore, a single place to discover, share, and reuse AI agents, tools, and agent skills across your enterprise. ]]></description>
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<pubDate>Thu, 09 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>The, future, managing, agents, scale:, AWS, Agent, Registry, now, preview</media:keywords>
</item>

<item>
<title>Embed a live AI browser agent in your React app with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/8473</link>
<guid>https://news.jatlink.uk/8473</guid>
<description><![CDATA[ This post walks you through three steps: starting a session and generating the Live View URL, rendering the stream in your React application, and wiring up an AI agent that drives the browser while your users watch. At the end, you will have a working sample application you can clone and run. ]]></description>
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<pubDate>Thu, 09 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Embed, live, browser, agent, your, React, app, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Introducing stateful MCP client capabilities on Amazon Bedrock AgentCore Runtime</title>
<link>https://news.jatlink.uk/8453</link>
<guid>https://news.jatlink.uk/8453</guid>
<description><![CDATA[ In this post, you will learn how to build stateful MCP servers that request user input during execution, invoke LLM sampling for dynamic content generation, and stream progress updates for long-running tasks. You will see code examples for each capability and deploy a working stateful MCP server to Amazon Bedrock AgentCore Runtime. ]]></description>
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<pubDate>Thu, 09 Apr 2026 18:00:13 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, stateful, MCP, client, capabilities, Amazon, Bedrock, AgentCore, Runtime</media:keywords>
</item>

<item>
<title>Human&amp;in&amp;the&amp;loop constructs for agentic workflows in healthcare and life sciences</title>
<link>https://news.jatlink.uk/8374</link>
<guid>https://news.jatlink.uk/8374</guid>
<description><![CDATA[ In healthcare and life sciences, AI agents help organizations process clinical data, submit regulatory filings, automate medical coding, and accelerate drug development and commercialization. However, the sensitive nature of healthcare data and regulatory requirements like Good Practice (GxP) compliance require human oversight at key decision points. This is where human-in-the-loop (HITL) constructs become essential. In this post, you will learn four practical approaches to implementing human-in-the-loop constructs using AWS services. ]]></description>
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<pubDate>Wed, 08 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Human-in-the-loop, constructs, for, agentic, workflows, healthcare, and, life, sciences</media:keywords>
</item>

<item>
<title>Building intelligent audio search with Amazon Nova Embeddings: A deep dive into semantic audio understanding</title>
<link>https://news.jatlink.uk/8375</link>
<guid>https://news.jatlink.uk/8375</guid>
<description><![CDATA[ This post walks you through understanding audio embeddings, implementing Amazon Nova Multimodal Embeddings, and building a practical search system for your audio content. You&#039;ll learn how embeddings represent audio as vectors, explore the technical capabilities of Amazon Nova, and see hands-on code examples for indexing and querying your audio libraries. By the end, you&#039;ll have the knowledge to deploy production-ready audio search capabilities. ]]></description>
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<pubDate>Wed, 08 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, intelligent, audio, search, with, Amazon, Nova, Embeddings:, deep, dive, into, semantic, audio, understanding</media:keywords>
</item>

<item>
<title>Reinforcement fine&amp;tuning on Amazon Bedrock: Best practices</title>
<link>https://news.jatlink.uk/8376</link>
<guid>https://news.jatlink.uk/8376</guid>
<description><![CDATA[ In this post, we explore where RFT is most effective, using the GSM8K mathematical reasoning dataset as a concrete example. We then walk through best practices for dataset preparation and reward function design, show how to monitor training progress using Amazon Bedrock metrics, and conclude with practical hyperparameter tuning guidelines informed by experiments across multiple models and use cases. ]]></description>
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<pubDate>Wed, 08 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Reinforcement, fine-tuning, Amazon, Bedrock:, Best, practices</media:keywords>
</item>

<item>
<title>Customize Amazon Nova models with Amazon Bedrock fine&amp;tuning</title>
<link>https://news.jatlink.uk/8373</link>
<guid>https://news.jatlink.uk/8373</guid>
<description><![CDATA[ In this post, we&#039;ll walk you through a complete implementation of model fine-tuning in Amazon Bedrock using Amazon Nova models, demonstrating each step through an intent classifier example that achieves superior performance on a domain specific task. Throughout this guide, you&#039;ll learn to prepare high-quality training data that drives meaningful model improvements, configure hyperparameters to optimize learning without overfitting, and deploy your fine-tuned model for improved accuracy and reduced latency. We&#039;ll show you how to evaluate your results using training metrics and loss curves. ]]></description>
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<pubDate>Wed, 08 Apr 2026 22:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Customize, Amazon, Nova, models, with, Amazon, Bedrock, fine-tuning</media:keywords>
</item>

<item>
<title>Manage AI costs with Amazon Bedrock Projects</title>
<link>https://news.jatlink.uk/8297</link>
<guid>https://news.jatlink.uk/8297</guid>
<description><![CDATA[ With Amazon Bedrock Projects, you can attribute inference costs to specific workloads and analyze them in AWS Cost Explorer and AWS Data Exports. In this post, you will learn how to set up Projects end-to-end, from designing a tagging strategy to analyzing costs. ]]></description>
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<pubDate>Wed, 08 Apr 2026 02:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Manage, costs, with, Amazon, Bedrock, Projects</media:keywords>
</item>

<item>
<title>Building real&amp;time conversational podcasts with Amazon Nova 2 Sonic</title>
<link>https://news.jatlink.uk/8267</link>
<guid>https://news.jatlink.uk/8267</guid>
<description><![CDATA[ This post walks through building an automated podcast generator that creates engaging conversations between two AI hosts on any topic, demonstrating the streaming capabilities of Nova Sonic, stage-aware content filtering, and real-time audio generation. ]]></description>
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<pubDate>Tue, 07 Apr 2026 18:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, real-time, conversational, podcasts, with, Amazon, Nova, Sonic</media:keywords>
</item>

<item>
<title>Text&amp;to&amp;SQL solution powered by Amazon Bedrock</title>
<link>https://news.jatlink.uk/8268</link>
<guid>https://news.jatlink.uk/8268</guid>
<description><![CDATA[ In this post, we show you how to build a natural text-to-SQL solution using Amazon Bedrock that transforms business questions into database queries and returns actionable answers. ]]></description>
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<pubDate>Tue, 07 Apr 2026 18:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Text-to-SQL, solution, powered, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>From isolated alerts to contextual intelligence: Agentic maritime anomaly analysis with generative AI</title>
<link>https://news.jatlink.uk/8202</link>
<guid>https://news.jatlink.uk/8202</guid>
<description><![CDATA[ This blog post demonstrates how Windward helps enhance and accelerate alert investigation processes by combining geospatial intelligence with generative AI, enabling analysts to focus on decision-making rather than data collection. ]]></description>
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<pubDate>Mon, 06 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>From, isolated, alerts, contextual, intelligence:, Agentic, maritime, anomaly, analysis, with, generative</media:keywords>
</item>

<item>
<title>Build AI&amp;powered employee onboarding agents with Amazon Quick</title>
<link>https://news.jatlink.uk/8199</link>
<guid>https://news.jatlink.uk/8199</guid>
<description><![CDATA[ In this post, we walk through building a custom HR onboarding agent with Quick. We show how to configure an agent that understands your organization’s processes, connects to your HR systems, and automates common tasks, such as answering new-hire questions and tracking document completion. ]]></description>
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<pubDate>Mon, 06 Apr 2026 22:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, AI-powered, employee, onboarding, agents, with, Amazon, Quick</media:keywords>
</item>

<item>
<title>Accelerate agentic tool calling with serverless model customization in Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/8200</link>
<guid>https://news.jatlink.uk/8200</guid>
<description><![CDATA[ In this post, we walk through how we fine-tuned Qwen 2.5 7B Instruct for tool calling using RLVR. We cover dataset preparation across three distinct agent behaviors, reward function design with tiered scoring, training configuration and results interpretation, evaluation on held-out data with unseen tools, and deployment. ]]></description>
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<pubDate>Mon, 06 Apr 2026 22:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerate, agentic, tool, calling, with, serverless, model, customization, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Building Intelligent Search with Amazon Bedrock and Amazon OpenSearch for hybrid RAG solutions</title>
<link>https://news.jatlink.uk/8201</link>
<guid>https://news.jatlink.uk/8201</guid>
<description><![CDATA[ In this post, we show how to implement a generative AI agentic assistant that uses both semantic and text-based search using Amazon Bedrock, Amazon Bedrock AgentCore, Strands Agents and Amazon OpenSearch. ]]></description>
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<pubDate>Mon, 06 Apr 2026 22:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, Intelligent, Search, with, Amazon, Bedrock, and, Amazon, OpenSearch, for, hybrid, RAG, solutions</media:keywords>
</item>

<item>
<title>Connecting MCP servers to Amazon Bedrock AgentCore Gateway using Authorization Code flow</title>
<link>https://news.jatlink.uk/8185</link>
<guid>https://news.jatlink.uk/8185</guid>
<description><![CDATA[ Amazon Bedrock AgentCore Gateway provides a centralized layer for managing how AI agents connect to tools and MCP servers across your organization. In this post, we walk through how to configure AgentCore Gateway to connect to an OAuth-protected MCP server using the Authorization Code flow. ]]></description>
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<pubDate>Mon, 06 Apr 2026 18:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Connecting, MCP, servers, Amazon, Bedrock, AgentCore, Gateway, using, Authorization, Code, flow</media:keywords>
</item>

<item>
<title>Simulate realistic users to evaluate multi&amp;turn AI agents in Strands Evals</title>
<link>https://news.jatlink.uk/7929</link>
<guid>https://news.jatlink.uk/7929</guid>
<description><![CDATA[ In this post, we explore how ActorSimulator in Strands Evaluations SDK addresses the challenge with structured user simulation that integrates into your evaluation pipeline. ]]></description>
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<pubDate>Thu, 02 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Simulate, realistic, users, evaluate, multi-turn, agents, Strands, Evals</media:keywords>
</item>

<item>
<title>Control which domains your AI agents can access</title>
<link>https://news.jatlink.uk/7909</link>
<guid>https://news.jatlink.uk/7909</guid>
<description><![CDATA[ In this post, we show you how to configure AWS Network Firewall to restrict AgentCore resources to an allowlist of approved internet domains. This post focuses on domain-level filtering using SNI inspection — the first layer of a defense-in-depth approach. ]]></description>
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<pubDate>Thu, 02 Apr 2026 18:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Control, which, domains, your, agents, can, access</media:keywords>
</item>

<item>
<title>Scaling seismic foundation models on AWS: Distributed training with Amazon SageMaker HyperPod and expanding context windows</title>
<link>https://news.jatlink.uk/7908</link>
<guid>https://news.jatlink.uk/7908</guid>
<description><![CDATA[ This post describes how TGS achieved near-linear scaling for distributed training and expanded context windows for their Vision Transformer-based SFM using Amazon SageMaker HyperPod. This joint solution cut training time from 6 months to just 5 days while enabling analysis of seismic volumes larger than previously possible. ]]></description>
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<pubDate>Thu, 02 Apr 2026 18:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Scaling, seismic, foundation, models, AWS:, Distributed, training, with, Amazon, SageMaker, HyperPod, and, expanding, context, windows</media:keywords>
</item>

<item>
<title>Persist session state with filesystem configuration and execute shell commands</title>
<link>https://news.jatlink.uk/7890</link>
<guid>https://news.jatlink.uk/7890</guid>
<description><![CDATA[ In this post, we go through how to use managed session storage to persist your agent&#039;s filesystem state and how to execute shell commands directly in your agent&#039;s environment. ]]></description>
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<pubDate>Thu, 02 Apr 2026 14:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Persist, session, state, with, filesystem, configuration, and, execute, shell, commands</media:keywords>
</item>

<item>
<title>Rocket Close transforms mortgage document processing with Amazon Bedrock and Amazon Textract</title>
<link>https://news.jatlink.uk/7889</link>
<guid>https://news.jatlink.uk/7889</guid>
<description><![CDATA[ Through a strategic partnership with the AWS Generative AI Innovation Center (GenAIIC), Rocket Close developed an intelligent document processing solution that has significantly reduced processing time, making the process 15 times faster. The solution, which uses Amazon Textract for OCR processing and Amazon Bedrock for foundation models (FMs), achieves a strong 90% overall accuracy in document segmentation, classification, and field extraction. ]]></description>
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<pubDate>Thu, 02 Apr 2026 14:00:06 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Rocket, Close, transforms, mortgage, document, processing, with, Amazon, Bedrock, and, Amazon, Textract</media:keywords>
</item>

<item>
<title>Automating competitive price intelligence with Amazon Nova Act</title>
<link>https://news.jatlink.uk/7825</link>
<guid>https://news.jatlink.uk/7825</guid>
<description><![CDATA[ This post demonstrates how to build an automated competitive price intelligence system that streamlines manual workflows, supporting teams to make data-driven pricing decisions with real-time market insights. ]]></description>
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<pubDate>Wed, 01 Apr 2026 22:00:07 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Automating, competitive, price, intelligence, with, Amazon, Nova, Act</media:keywords>
</item>

<item>
<title>Build reliable AI agents with Amazon Bedrock AgentCore Evaluations</title>
<link>https://news.jatlink.uk/7744</link>
<guid>https://news.jatlink.uk/7744</guid>
<description><![CDATA[ In this post, we introduce Amazon Bedrock AgentCore Evaluations, a fully managed service for assessing AI agent performance across the development lifecycle. We walk through how the service measures agent accuracy across multiple quality dimensions. We explain the two evaluation approaches for development and production and share practical guidance for building agents you can deploy with confidence. ]]></description>
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<pubDate>Wed, 01 Apr 2026 02:00:10 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, reliable, agents, with, Amazon, Bedrock, AgentCore, Evaluations</media:keywords>
</item>

<item>
<title>Building an AI powered system for compliance evidence collection</title>
<link>https://news.jatlink.uk/7726</link>
<guid>https://news.jatlink.uk/7726</guid>
<description><![CDATA[ In this post, we show you how to build a similar system for your organization. You will learn the architecture decisions, implementation details, and deployment process that can help you automate your own compliance workflows. ]]></description>
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<pubDate>Tue, 31 Mar 2026 22:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, powered, system, for, compliance, evidence, collection</media:keywords>
</item>

<item>
<title>Build a FinOps agent using Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/7725</link>
<guid>https://news.jatlink.uk/7725</guid>
<description><![CDATA[ In this post, you learn how to build a FinOps agent using Amazon Bedrock AgentCore that helps your finance team manage AWS costs across multiple accounts. This conversational agent consolidates data from AWS Cost Explorer, AWS Budgets, and AWS Compute Optimizer into a single interface, so your team can ask questions like &quot;What are my top cost drivers this month?&quot; and receive immediate answers. ]]></description>
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<pubDate>Tue, 31 Mar 2026 22:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, FinOps, agent, using, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Accelerating software delivery with agentic QA automation using Amazon Nova Act</title>
<link>https://news.jatlink.uk/7727</link>
<guid>https://news.jatlink.uk/7727</guid>
<description><![CDATA[ In this post, we demonstrate how to implement agentic QA automation through QA Studio, a reference solution built with Amazon Nova Act. You will see how to define tests in natural language that adapt automatically to UI changes, explore the serverless architecture that executes tests reliably at scale, and get step-by-step deployment guidance for your AWS environment. ]]></description>
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<pubDate>Tue, 31 Mar 2026 22:00:05 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerating, software, delivery, with, agentic, automation, using, Amazon, Nova, Act</media:keywords>
</item>

<item>
<title>Can your governance keep pace with your AI ambitions? AI risk intelligence in the agentic era</title>
<link>https://news.jatlink.uk/7695</link>
<guid>https://news.jatlink.uk/7695</guid>
<description><![CDATA[ Traditional frameworks designed for static deployments cannot address the dynamic interactions that define agentic workloads. AI Risk Intelligence (AIRI), from AWS Generative AI Innovation Center, provides the automated rigor required to govern agents at enterprise scale—a fundamental reimagining of how security, operations, and governance work together systemically. ]]></description>
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<pubDate>Tue, 31 Mar 2026 18:00:09 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Can, your, governance, keep, pace, with, your, ambitions, risk, intelligence, the, agentic, era</media:keywords>
</item>

<item>
<title>AWS launches frontier agents for security testing and cloud operations</title>
<link>https://news.jatlink.uk/7694</link>
<guid>https://news.jatlink.uk/7694</guid>
<description><![CDATA[ I&#039;m excited to announce that AWS Security Agent on-demand penetration testing and AWS DevOps Agent are now generally available, representing a new class of AI capabilities we announced at re:Invent called frontier agents. These autonomous systems work independently to achieve goals, scale massively to tackle concurrent tasks, and run persistently for hours or days without constant human oversight. Together, these agents are changing the way we secure and operate software. In preview, customers and partners report that AWS Security Agent compresses penetration testing timelines from weeks to hours and the AWS DevOps Agent supports 3–5x faster incident resolution. ]]></description>
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<pubDate>Tue, 31 Mar 2026 18:00:08 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>AWS, launches, frontier, agents, for, security, testing, and, cloud, operations</media:keywords>
</item>

<item>
<title>Deliver hyper&amp;personalized viewer experiences with an agentic AI movie assistant using Amazon Bedrock AgentCore and Amazon Nova Sonic 2.0</title>
<link>https://news.jatlink.uk/7597</link>
<guid>https://news.jatlink.uk/7597</guid>
<description><![CDATA[ In this post, we walk through two use cases that help enhance the user viewing experience using agentic AI tools and frameworks including Strands Agents SDK, Amazon Bedrock AgentCore, and Amazon Nova Sonic 2.0. This agentic AI system uses a Model Context Protocol (MCP) to deliver a personal entertainment concierge that understands user preferences through natural dialogue. ]]></description>
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<pubDate>Mon, 30 Mar 2026 18:00:28 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Deliver, hyper-personalized, viewer, experiences, with, agentic, movie, assistant, using, Amazon, Bedrock, AgentCore, and, Amazon, Nova, Sonic, 2.0</media:keywords>
</item>

<item>
<title>How Ring scales global customer support with Amazon Bedrock Knowledge Bases</title>
<link>https://news.jatlink.uk/7594</link>
<guid>https://news.jatlink.uk/7594</guid>
<description><![CDATA[ In this post, you&#039;ll learn how Ring implemented metadata-driven filtering for Region-specific content, separated content management into ingestion, evaluation and promotion workflows, and achieved cost savings while scaling up. ]]></description>
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<pubDate>Mon, 30 Mar 2026 18:00:27 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Ring, scales, global, customer, support, with, Amazon, Bedrock, Knowledge, Bases</media:keywords>
</item>

<item>
<title>Build a solar flare detection system on SageMaker AI LSTM networks and ESA STIX data</title>
<link>https://news.jatlink.uk/7596</link>
<guid>https://news.jatlink.uk/7596</guid>
<description><![CDATA[ In this post, we show you how to use Amazon SageMaker AI to build and deploy a deep learning model for detecting solar flares using data from the European Space Agency&#039;s STIX instrument. ]]></description>
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<pubDate>Mon, 30 Mar 2026 18:00:27 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, solar, flare, detection, system, SageMaker, LSTM, networks, and, ESA, STIX, data</media:keywords>
</item>

<item>
<title>Reimagine marketing at Volkswagen Group with generative AI</title>
<link>https://news.jatlink.uk/7595</link>
<guid>https://news.jatlink.uk/7595</guid>
<description><![CDATA[ In this post, we explore the challenges that Volkswagen Group faced in producing brand-compliant marketing assets at scale. We walk through how we built a generative AI solution that generates photorealistic vehicle images, validates technical accuracy at the component level, and helps enforce brand guideline compliance alignment across the ten brands. ]]></description>
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<pubDate>Mon, 30 Mar 2026 18:00:27 +0100</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Reimagine, marketing, Volkswagen, Group, with, generative</media:keywords>
</item>

<item>
<title>Run Generative AI inference with Amazon Bedrock in Asia Pacific (New Zealand)</title>
<link>https://news.jatlink.uk/7274</link>
<guid>https://news.jatlink.uk/7274</guid>
<description><![CDATA[ Today, we’re excited to announce that Amazon Bedrock is now available in the Asia Pacific (New Zealand) Region (ap-southeast-6). Customers in New Zealand can now access Anthropic Claude models (Claude Opus 4.5, Opus 4.6, Sonnet 4.5, Sonnet 4.6, and Haiku 4.5) and Amazon (Nova 2 Lite) models directly in the Auckland Region with cross region inference. In this post, we explore how cross-Region inference works from the New Zealand Region, the models available through geographic and global routing, and how to get started with your first API call. We ]]></description>
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<pubDate>Fri, 27 Mar 2026 01:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Run, Generative, inference, with, Amazon, Bedrock, Asia, Pacific, New, Zealand</media:keywords>
</item>

<item>
<title>Accelerating LLM fine&amp;tuning with unstructured data using SageMaker Unified Studio and S3</title>
<link>https://news.jatlink.uk/7258</link>
<guid>https://news.jatlink.uk/7258</guid>
<description><![CDATA[ Last year, AWS announced an integration between Amazon SageMaker Unified Studio and Amazon S3 general purpose buckets. This integration makes it straightforward for teams to use unstructured data stored in Amazon Simple Storage Service (Amazon S3) for machine learning (ML) and data analytics use cases. In this post, we show how to integrate S3 general purpose buckets with Amazon SageMaker Catalog to fine-tune Llama 3.2 11B Vision Instruct for visual question answering (VQA) using Amazon SageMaker Unified Studio. ]]></description>
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<pubDate>Thu, 26 Mar 2026 21:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerating, LLM, fine-tuning, with, unstructured, data, using, SageMaker, Unified, Studio, and</media:keywords>
</item>

<item>
<title>Introducing Amazon Polly Bidirectional Streaming: Real&amp;time speech synthesis for conversational AI</title>
<link>https://news.jatlink.uk/7259</link>
<guid>https://news.jatlink.uk/7259</guid>
<description><![CDATA[ Today, we’re excited to announce the new Bidirectional Streaming API for Amazon Polly, enabling streamlined real-time text-to-speech (TTS) synthesis where you can start sending text and receiving audio simultaneously. This new API is built for conversational AI applications that generate text or audio incrementally, like responses from large language models (LLMs), where users must begin synthesizing audio before the full text is available. ]]></description>
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<pubDate>Thu, 26 Mar 2026 21:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Amazon, Polly, Bidirectional, Streaming:, Real-time, speech, synthesis, for, conversational</media:keywords>
</item>

<item>
<title>Building age&amp;responsive, context&amp;aware AI with Amazon Bedrock Guardrails</title>
<link>https://news.jatlink.uk/7257</link>
<guid>https://news.jatlink.uk/7257</guid>
<description><![CDATA[ In this post, we walk you through how to implement a fully automated, context-aware AI solution using a serverless architecture on AWS. This solution helps organizations looking to deploy responsible AI systems, align with compliance requirements for vulnerable populations, and help maintain appropriate and trustworthy AI responses across diverse user groups without compromising performance or governance. ]]></description>
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<pubDate>Thu, 26 Mar 2026 21:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, age-responsive, context-aware, with, Amazon, Bedrock, Guardrails</media:keywords>
</item>

<item>
<title>Unlocking video insights at scale with Amazon Bedrock multimodal models</title>
<link>https://news.jatlink.uk/7149</link>
<guid>https://news.jatlink.uk/7149</guid>
<description><![CDATA[ In this post, we explore how the multimodal foundation models (FMs) of Amazon Bedrock enable scalable video understanding through three distinct architectural approaches. Each approach is designed for different use cases and cost-performance trade-offs. ]]></description>
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<pubDate>Wed, 25 Mar 2026 21:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Unlocking, video, insights, scale, with, Amazon, Bedrock, multimodal, models</media:keywords>
</item>

<item>
<title>Deploy voice agents with Pipecat and Amazon Bedrock AgentCore Runtime – Part 1</title>
<link>https://news.jatlink.uk/7150</link>
<guid>https://news.jatlink.uk/7150</guid>
<description><![CDATA[ In this series of posts, you will learn how streaming architectures help address these challenges using Pipecat voice agents on Amazon Bedrock AgentCore Runtime. In Part 1, you will learn how to deploy Pipecat voice agents on AgentCore Runtime using different network transport approaches including WebSockets, WebRTC and telephony integration, with practical deployment guidance and code samples. ]]></description>
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<pubDate>Wed, 25 Mar 2026 21:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Deploy, voice, agents, with, Pipecat, and, Amazon, Bedrock, AgentCore, Runtime, –, Part</media:keywords>
</item>

<item>
<title>Reinforcement fine&amp;tuning on Amazon Bedrock with OpenAI&amp;Compatible APIs: a technical walkthrough</title>
<link>https://news.jatlink.uk/7151</link>
<guid>https://news.jatlink.uk/7151</guid>
<description><![CDATA[ In this post, we walk through the end-to-end workflow of using RFT on Amazon Bedrock with OpenAI-compatible APIs: from setting up authentication, to deploying a Lambda-based reward function, to kicking off a training job and running on-demand inference on your fine-tuned model. ]]></description>
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<pubDate>Wed, 25 Mar 2026 21:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Reinforcement, fine-tuning, Amazon, Bedrock, with, OpenAI-Compatible, APIs:, technical, walkthrough</media:keywords>
</item>

<item>
<title>Accelerating custom entity recognition with Claude tool use in Amazon Bedrock</title>
<link>https://news.jatlink.uk/7046</link>
<guid>https://news.jatlink.uk/7046</guid>
<description><![CDATA[ This post introduces Claude Tool use in Amazon Bedrock which uses the power of large language models (LLMs) to perform dynamic, adaptable entity recognition without extensive setup or training. ]]></description>
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<pubDate>Tue, 24 Mar 2026 21:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerating, custom, entity, recognition, with, Claude, tool, use, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Deploy SageMaker AI inference endpoints with set GPU capacity using training plans</title>
<link>https://news.jatlink.uk/7045</link>
<guid>https://news.jatlink.uk/7045</guid>
<description><![CDATA[ In this post, we walk through how to search for available p-family GPU capacity, create a training plan reservation for inference, and deploy a SageMaker AI inference endpoint on that reserved capacity. We follow a data scientist&#039;s journey as they reserve capacity for model evaluation and manage the endpoint throughout the reservation lifecycle. ]]></description>
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<pubDate>Tue, 24 Mar 2026 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Deploy, SageMaker, inference, endpoints, with, set, GPU, capacity, using, training, plans</media:keywords>
</item>

<item>
<title>Overcoming LLM hallucinations in regulated industries: Artificial Genius’s deterministic models on Amazon Nova</title>
<link>https://news.jatlink.uk/6915</link>
<guid>https://news.jatlink.uk/6915</guid>
<description><![CDATA[ In this post, we’re excited to showcase how AWS ISV Partner Artificial Genius is using Amazon SageMaker AI and Amazon Nova to deliver a solution that is probabilistic on input but deterministic on output, helping to enable safe, enterprise-grade adoption. ]]></description>
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<pubDate>Mon, 23 Mar 2026 17:00:08 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Overcoming, LLM, hallucinations, regulated, industries:, Artificial, Genius’s, deterministic, models, Amazon, Nova</media:keywords>
</item>

<item>
<title>How Reco transforms security alerts using Amazon Bedrock</title>
<link>https://news.jatlink.uk/6913</link>
<guid>https://news.jatlink.uk/6913</guid>
<description><![CDATA[ In this blog post, we show you how Reco implemented Amazon Bedrock to help transform security alerts and achieve significant improvements in incident response times. ]]></description>
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<pubDate>Mon, 23 Mar 2026 17:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Reco, transforms, security, alerts, using, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Integrating Amazon Bedrock AgentCore with Slack</title>
<link>https://news.jatlink.uk/6914</link>
<guid>https://news.jatlink.uk/6914</guid>
<description><![CDATA[ In this post, we demonstrate how to build a Slack integration using AWS Cloud Development Kit (AWS CDK). You will learn how to deploy the infrastructure with three specialized AWS Lambda functions, configure event subscriptions properly to handle Slack&#039;s security requirements, and implement conversation management patterns that work for many agent use cases. ]]></description>
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<pubDate>Mon, 23 Mar 2026 17:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Integrating, Amazon, Bedrock, AgentCore, with, Slack</media:keywords>
</item>

<item>
<title>Enforce data residency with Amazon Quick extensions for Microsoft Teams</title>
<link>https://news.jatlink.uk/6596</link>
<guid>https://news.jatlink.uk/6596</guid>
<description><![CDATA[ In this post, we will show you how to enforce data residency when deploying Amazon Quick Microsoft Teams extensions across multiple AWS Regions. You will learn how to configure multi-Region Amazon Quick extensions that automatically route users to AWS Region-appropriate resources, helping keep compliance with GDPR and other data sovereignty requirements. ]]></description>
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<pubDate>Thu, 19 Mar 2026 21:00:10 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Enforce, data, residency, with, Amazon, Quick, extensions, for, Microsoft, Teams</media:keywords>
</item>

<item>
<title>Introducing V&amp;RAG: revolutionizing AI&amp;powered video production with Retrieval Augmented Generation</title>
<link>https://news.jatlink.uk/6594</link>
<guid>https://news.jatlink.uk/6594</guid>
<description><![CDATA[ This post introduces Video Retrieval-Augmented Generation (V-RAG), an approach to help improve video content creation. By combining retrieval augmented generation with advanced video AI models, V-RAG offers an efficient, and reliable solution for generating AI videos. ]]></description>
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<pubDate>Thu, 19 Mar 2026 21:00:09 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, V-RAG:, revolutionizing, AI-powered, video, production, with, Retrieval, Augmented, Generation</media:keywords>
</item>

<item>
<title>Enhanced metrics for Amazon SageMaker AI endpoints: deeper visibility for better performance</title>
<link>https://news.jatlink.uk/6595</link>
<guid>https://news.jatlink.uk/6595</guid>
<description><![CDATA[ SageMaker AI endpoints now support enhanced metrics with configurable publishing frequency. This launch provides the granular visibility needed to monitor, troubleshoot, and improve your production endpoints. ]]></description>
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<pubDate>Thu, 19 Mar 2026 21:00:09 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Enhanced, metrics, for, Amazon, SageMaker, endpoints:, deeper, visibility, for, better, performance</media:keywords>
</item>

<item>
<title>Run NVIDIA Nemotron 3 Super on Amazon Bedrock</title>
<link>https://news.jatlink.uk/6592</link>
<guid>https://news.jatlink.uk/6592</guid>
<description><![CDATA[ This post explores the technical characteristics of the Nemotron 3 Super model and discusses potential application use cases. It also provides technical guidance to get started using this model for your generative AI applications within the Amazon Bedrock environment. ]]></description>
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<pubDate>Thu, 19 Mar 2026 21:00:08 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Run, NVIDIA, Nemotron, Super, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Use RAG for video generation using Amazon Bedrock and Amazon Nova Reel</title>
<link>https://news.jatlink.uk/6593</link>
<guid>https://news.jatlink.uk/6593</guid>
<description><![CDATA[ In this post, we explore our approach to video generation through VRAG, transforming natural language text prompts and images into grounded, high-quality videos. Through this fully automated solution, you can generate realistic, AI-powered video sequences from structured text and image inputs, streamlining the video creation process. ]]></description>
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<pubDate>Thu, 19 Mar 2026 21:00:08 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Use, RAG, for, video, generation, using, Amazon, Bedrock, and, Amazon, Nova, Reel</media:keywords>
</item>

<item>
<title>How Bark.com and AWS collaborated to build a scalable video generation solution</title>
<link>https://news.jatlink.uk/6465</link>
<guid>https://news.jatlink.uk/6465</guid>
<description><![CDATA[ Working with the AWS Generative AI Innovation Center, Bark developed an AI-powered content generation solution that demonstrated a substantial reduction in production time in experimental trials while improving content quality scores. In this post, we walk you through the technical architecture we built, the key design decisions that contributed to success, and the measurable results achieved, giving you a blueprint for implementing similar solutions. ]]></description>
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<pubDate>Wed, 18 Mar 2026 17:00:08 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Bark.com, and, AWS, collaborated, build, scalable, video, generation, solution</media:keywords>
</item>

<item>
<title>Migrate from Amazon Nova 1 to Amazon Nova 2 on Amazon Bedrock</title>
<link>https://news.jatlink.uk/6466</link>
<guid>https://news.jatlink.uk/6466</guid>
<description><![CDATA[ In this post, you will learn how to migrate from Nova 1 to Nova 2 on Amazon Bedrock. We cover model mapping, API changes, code examples using the Converse API, guidance on configuring new capabilities, and a summary of use cases. We conclude with a migration checklist to help you plan and execute your transition. ]]></description>
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<pubDate>Wed, 18 Mar 2026 17:00:08 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Migrate, from, Amazon, Nova, Amazon, Nova, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Evaluating AI agents for production: A practical guide to Strands Evals</title>
<link>https://news.jatlink.uk/6463</link>
<guid>https://news.jatlink.uk/6463</guid>
<description><![CDATA[ In this post, we show how to evaluate AI agents systematically using Strands Evals. We walk through the core concepts, built-in evaluators, multi-turn simulation capabilities and practical approaches and patterns for integration. ]]></description>
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<pubDate>Wed, 18 Mar 2026 17:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Evaluating, agents, for, production:, practical, guide, Strands, Evals</media:keywords>
</item>

<item>
<title>Introducing Nova Forge SDK, a seamless way to customize Nova models for enterprise AI</title>
<link>https://news.jatlink.uk/6462</link>
<guid>https://news.jatlink.uk/6462</guid>
<description><![CDATA[ Today, we are launching Nova Forge SDK that makes LLM customization accessible, empowering teams to harness the full potential of language models without the challenges of dependency management, image selection, and recipe configuration and eventually lowering the barrier of entry. ]]></description>
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<pubDate>Wed, 18 Mar 2026 17:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Nova, Forge, SDK, seamless, way, customize, Nova, models, for, enterprise</media:keywords>
</item>

<item>
<title>Build an AI&amp;Powered A/B testing engine using Amazon Bedrock</title>
<link>https://news.jatlink.uk/6464</link>
<guid>https://news.jatlink.uk/6464</guid>
<description><![CDATA[ This post shows you how to build an AI-powered A/B testing engine using Amazon Bedrock, Amazon Elastic Container Service, Amazon DynamoDB, and the Model Context Protocol (MCP). The system improves traditional A/B testing by analyzing user context  to make smarter variant assignment decisions during the experiment. ]]></description>
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<pubDate>Wed, 18 Mar 2026 17:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, AI-Powered, AB, testing, engine, using, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Kick off Nova customization experiments using Nova Forge SDK</title>
<link>https://news.jatlink.uk/6461</link>
<guid>https://news.jatlink.uk/6461</guid>
<description><![CDATA[ In this post, we walk you through the process of using the Nova Forge SDK to train an Amazon Nova model using Amazon SageMaker AI Training Jobs. ]]></description>
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<pubDate>Wed, 18 Mar 2026 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Kick, off, Nova, customization, experiments, using, Nova, Forge, SDK</media:keywords>
</item>

<item>
<title>AWS AI League: Atos fine&amp;tunes approach to AI education</title>
<link>https://news.jatlink.uk/6347</link>
<guid>https://news.jatlink.uk/6347</guid>
<description><![CDATA[ In this post, we’ll explore how Atos used the AWS AI League to help accelerate AI education across 400+ participants, highlight the tangible benefits of gamified, experiential learning, and share actionable insights you can apply to your own AI enablement programs. ]]></description>
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<pubDate>Tue, 17 Mar 2026 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>AWS, League:, Atos, fine-tunes, approach, education</media:keywords>
</item>

<item>
<title>AWS and NVIDIA deepen strategic collaboration to accelerate AI from pilot to production</title>
<link>https://news.jatlink.uk/6259</link>
<guid>https://news.jatlink.uk/6259</guid>
<description><![CDATA[ Today at NVIDIA GTC 2026, AWS and NVIDIA announced an expanded collaboration with new technology integrations to support growing AI compute demand and help you build and run AI solutions that are production-ready. ]]></description>
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<pubDate>Mon, 16 Mar 2026 21:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>AWS, and, NVIDIA, deepen, strategic, collaboration, accelerate, from, pilot, production</media:keywords>
</item>

<item>
<title>Agentic AI in the Enterprise Part 2: Guidance by Persona</title>
<link>https://news.jatlink.uk/6257</link>
<guid>https://news.jatlink.uk/6257</guid>
<description><![CDATA[ This is Part II of a two-part series from the AWS Generative AI Innovation Center. In Part II, we speak directly to the leaders who must turn that shared foundation into action. Each role carries a distinct set of responsibilities, risks, and leverage points. Whether you own a P&amp;L, run enterprise architecture, lead security, govern data, or manage compliance, this section is written in the language of your job—because that&#039;s where agentic AI either succeeds or quietly dies. ]]></description>
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<pubDate>Mon, 16 Mar 2026 20:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Agentic, the, Enterprise, Part, Guidance, Persona</media:keywords>
</item>

<item>
<title>Introducing Disaggregated Inference on AWS powered by llm&amp;d</title>
<link>https://news.jatlink.uk/6258</link>
<guid>https://news.jatlink.uk/6258</guid>
<description><![CDATA[ In this blog post, we introduce the concepts behind next-generation inference capabilities, including disaggregated serving, intelligent request scheduling, and expert parallelism. We discuss their benefits and walk through how you can implement them on Amazon SageMaker HyperPod EKS to achieve significant improvements in inference performance, resource utilization, and operational efficiency. ]]></description>
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<pubDate>Mon, 16 Mar 2026 20:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Disaggregated, Inference, AWS powered, llm-d</media:keywords>
</item>

<item>
<title>How Workhuman built multi&amp;tenant self&amp;service reporting using Amazon Quick Sight embedded dashboards</title>
<link>https://news.jatlink.uk/6238</link>
<guid>https://news.jatlink.uk/6238</guid>
<description><![CDATA[ This post explores how Workhuman transformed their analytics delivery model and the key lessons learned from their implementation. We go through their architecture approach, implementation strategy, and the business outcomes they achieved—providing you with a practical blueprint for adding embedded analytics to your own software as a service (SaaS) applications. ]]></description>
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<pubDate>Mon, 16 Mar 2026 16:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Workhuman, built, multi-tenant, self-service, reporting, using, Amazon, Quick, Sight, embedded, dashboards</media:keywords>
</item>

<item>
<title>Build an offline feature store using Amazon SageMaker Unified Studio and SageMaker Catalog</title>
<link>https://news.jatlink.uk/6239</link>
<guid>https://news.jatlink.uk/6239</guid>
<description><![CDATA[ This blog post provides step-by-step guidance on implementing an offline feature store using SageMaker Catalog within a SageMaker Unified Studio domain. By adopting a publish-subscribe pattern, data producers can use this solution to publish curated, versioned feature tables—while data consumers can securely discover, subscribe to, and reuse them for model development. ]]></description>
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<pubDate>Mon, 16 Mar 2026 16:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, offline, feature, store, using, Amazon, SageMaker, Unified, Studio, and, SageMaker, Catalog</media:keywords>
</item>

<item>
<title>P&amp;EAGLE: Faster LLM inference with Parallel Speculative Decoding in vLLM</title>
<link>https://news.jatlink.uk/6078</link>
<guid>https://news.jatlink.uk/6078</guid>
<description><![CDATA[ In this post, we explain how P-EAGLE works, how we integrated it into vLLM starting from v0.16.0 (PR#32887), and how to serve it with our pre-trained checkpoints. ]]></description>
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<pubDate>Fri, 13 Mar 2026 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>P-EAGLE:, Faster, LLM, inference, with, Parallel, Speculative, Decoding, vLLM</media:keywords>
</item>

<item>
<title>Improve operational visibility for inference workloads on Amazon Bedrock with new CloudWatch metrics for TTFT and Estimated Quota Consumption</title>
<link>https://news.jatlink.uk/6015</link>
<guid>https://news.jatlink.uk/6015</guid>
<description><![CDATA[ Today, we’re announcing two new Amazon CloudWatch metrics for Amazon Bedrock, TimeToFirstToken and EstimatedTPMQuotaUsage. In this post, we cover how these work and how to set alarms, establish baselines, and proactively manage capacity using them. ]]></description>
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<pubDate>Fri, 13 Mar 2026 00:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Improve, operational, visibility, for, inference, workloads, Amazon, Bedrock, with, new, CloudWatch, metrics, for, TTFT, and, Estimated, Quota, Consumption</media:keywords>
</item>

<item>
<title>Secure AI agents with Policy in Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/6016</link>
<guid>https://news.jatlink.uk/6016</guid>
<description><![CDATA[ In this post, you will understand how Policy in Amazon Bedrock AgentCore creates a deterministic enforcement layer that operates independently of the agent&#039;s own reasoning. You will learn how to turn natural language descriptions of your business rules into Cedar policies, then use those policies to enforce fine-grained, identity-aware controls so that agents only access the tools and data that their users are authorized to use. You will also see how to apply Policy through AgentCore Gateway, intercepting and evaluating every agent-to-tool request at runtime. ]]></description>
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<pubDate>Fri, 13 Mar 2026 00:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Secure, agents, with, Policy, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Fine&amp;tuning NVIDIA Nemotron Speech ASR on Amazon EC2 for domain adaptation</title>
<link>https://news.jatlink.uk/5982</link>
<guid>https://news.jatlink.uk/5982</guid>
<description><![CDATA[ In this post, we explore how to fine-tune a leaderboard-topping, NVIDIA Nemotron Speech Automatic Speech Recognition (ASR) model; Parakeet TDT 0.6B V2. Using synthetic speech data to achieve superior transcription results for specialised applications, we&#039;ll walk through an end-to-end workflow that combines AWS infrastructure with the following popular open-source frameworks. ]]></description>
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<pubDate>Thu, 12 Mar 2026 16:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Fine-tuning, NVIDIA, Nemotron, Speech, ASR, Amazon, EC2, for, domain, adaptation</media:keywords>
</item>

<item>
<title>Multimodal embeddings at scale: AI data lake for media and entertainment workloads</title>
<link>https://news.jatlink.uk/5981</link>
<guid>https://news.jatlink.uk/5981</guid>
<description><![CDATA[ This post shows you how to build a scalable multimodal video search system that enables natural language search across large video datasets using Amazon Nova models and Amazon OpenSearch Service. You will learn how to move beyond manual tagging and keyword-based searches to enable semantic search that captures the full richness of video content. ]]></description>
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<pubDate>Thu, 12 Mar 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Multimodal, embeddings, scale:, data, lake, for, media, and, entertainment, workloads</media:keywords>
</item>

<item>
<title>Operationalizing Agentic AI Part 1: A Stakeholder’s Guide</title>
<link>https://news.jatlink.uk/5928</link>
<guid>https://news.jatlink.uk/5928</guid>
<description><![CDATA[ The AWS Generative AI Innovation Center has helped 1,000+ customers move AI into production, delivering millions in documented productivity gains. In this post, we share guidance for leaders across the C-suite: CTOs, CISOs, CDOs, and Chief Data Science/AI officers, as well as business owners and compliance leads. ]]></description>
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<pubDate>Wed, 11 Mar 2026 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Operationalizing, Agentic, Part, Stakeholder’s, Guide</media:keywords>
</item>

<item>
<title>Accelerate custom LLM deployment: Fine&amp;tune with Oumi and deploy to Amazon Bedrock</title>
<link>https://news.jatlink.uk/5826</link>
<guid>https://news.jatlink.uk/5826</guid>
<description><![CDATA[ In this post, we show how to fine-tune a Llama model using Oumi on Amazon EC2 (with the option to create synthetic data using Oumi), store artifacts in Amazon S3, and deploy to Amazon Bedrock using Custom Model Import for managed inference. ]]></description>
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<pubDate>Tue, 10 Mar 2026 16:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerate, custom, LLM, deployment:, Fine-tune, with, Oumi, and, deploy, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Access Anthropic Claude models in India on Amazon Bedrock with Global cross&amp;Region inference</title>
<link>https://news.jatlink.uk/5765</link>
<guid>https://news.jatlink.uk/5765</guid>
<description><![CDATA[ In this post, you will discover how to use Amazon Bedrock&#039;s Global cross-Region Inference for Claude models in India. We will guide you through the capabilities of each Claude model variant and how to get started with a code example to help you start building generative AI applications immediately. ]]></description>
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<pubDate>Mon, 09 Mar 2026 21:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Access, Anthropic, Claude, models, India, Amazon, Bedrock, with, Global, cross-Region, inference</media:keywords>
</item>

<item>
<title>Run NVIDIA Nemotron 3 Nano as a fully managed serverless model on Amazon Bedrock</title>
<link>https://news.jatlink.uk/5764</link>
<guid>https://news.jatlink.uk/5764</guid>
<description><![CDATA[ We are excited to announce that NVIDIA’s Nemotron 3 Nano is now available as a fully managed and serverless model in Amazon Bedrock. This follows our earlier announcement at AWS re:Invent supporting NVIDIA Nemotron 2 Nano 9B and NVIDIA Nemotron 2 Nano VL 12B models. This post explores the technical characteristics of the NVIDIA Nemotron 3 Nano model and discusses potential application use cases. Additionally, it provides technical guidance to help you get started using this model for your generative AI applications within the Amazon Bedrock environment. ]]></description>
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<pubDate>Mon, 09 Mar 2026 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Run, NVIDIA, Nemotron, Nano, fully, managed, serverless, model, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Drive organizational growth with Amazon Lex multi&amp;developer CI/CD pipeline</title>
<link>https://news.jatlink.uk/5491</link>
<guid>https://news.jatlink.uk/5491</guid>
<description><![CDATA[ In this post, we walk through a multi-developer CI/CD pipeline for Amazon Lex that enables isolated development environments, automated testing, and streamlined deployments. We show you how to set up the solution and share real-world results from teams using this approach. ]]></description>
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<pubDate>Thu, 05 Mar 2026 17:00:10 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Drive, organizational, growth, with, Amazon, Lex, multi-developer, CICD, pipeline</media:keywords>
</item>

<item>
<title>Building custom model provider for Strands Agents with LLMs hosted on SageMaker AI endpoints</title>
<link>https://news.jatlink.uk/5492</link>
<guid>https://news.jatlink.uk/5492</guid>
<description><![CDATA[ This post demonstrates how to build custom model parsers for Strands agents when working with LLMs hosted on SageMaker that don&#039;t natively support the Bedrock Messages API format. We&#039;ll walk through deploying Llama 3.1 with SGLang on SageMaker using awslabs/ml-container-creator, then implementing a custom parser to integrate it with Strands agents. ]]></description>
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<pubDate>Thu, 05 Mar 2026 17:00:10 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, custom, model, provider, for, Strands, Agents, with, LLMs, hosted, SageMaker, endpoints</media:keywords>
</item>

<item>
<title>Embed Amazon Quick Suite chat agents in enterprise applications</title>
<link>https://news.jatlink.uk/5447</link>
<guid>https://news.jatlink.uk/5447</guid>
<description><![CDATA[ Organizations find it challenging to implement a secure embedded chat in their applications and can require weeks of development to build authentication, token validation, domain security, and global distribution infrastructure. In this post, we show you how to solve this with a one-click deployment solution to embed the chat agents using the Quick Suite Embedding SDK in enterprise portals. ]]></description>
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<pubDate>Thu, 05 Mar 2026 00:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Embed, Amazon, Quick, Suite, chat, agents, enterprise, applications</media:keywords>
</item>

<item>
<title>Unlock powerful call center analytics with Amazon Nova foundation models</title>
<link>https://news.jatlink.uk/5448</link>
<guid>https://news.jatlink.uk/5448</guid>
<description><![CDATA[ In this post, we discuss how Amazon Nova demonstrates capabilities in conversational analytics, call classification, and other use cases often relevant to contact center solutions. We examine these capabilities for both single-call and multi-call analytics use cases. ]]></description>
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<pubDate>Thu, 05 Mar 2026 00:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Unlock, powerful, call, center, analytics, with, Amazon, Nova, foundation, models</media:keywords>
</item>

<item>
<title>How Ricoh built a scalable intelligent document processing solution on AWS</title>
<link>https://news.jatlink.uk/5432</link>
<guid>https://news.jatlink.uk/5432</guid>
<description><![CDATA[ This post explores how Ricoh built a standardized, multi-tenant solution for automated document classification and extraction using the AWS GenAI IDP Accelerator as a foundation, transforming their document processing from a custom-engineering bottleneck into a scalable, repeatable service. ]]></description>
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<pubDate>Wed, 04 Mar 2026 21:00:12 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Ricoh, built, scalable, intelligent, document, processing, solution, AWS</media:keywords>
</item>

<item>
<title>Building a scalable virtual try&amp;on solution using Amazon Nova on AWS: part 1</title>
<link>https://news.jatlink.uk/5339</link>
<guid>https://news.jatlink.uk/5339</guid>
<description><![CDATA[ In this post, we explore the virtual try-on capability now available in Amazon Nova Canvas, including sample code to get started quickly and tips to help get the best outputs. ]]></description>
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<pubDate>Tue, 03 Mar 2026 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, scalable, virtual, try-on, solution, using, Amazon, Nova, AWS:, part</media:keywords>
</item>

<item>
<title>How Lendi revamped the refinance journey for its customers using agentic AI in 16 weeks using Amazon Bedrock</title>
<link>https://news.jatlink.uk/5340</link>
<guid>https://news.jatlink.uk/5340</guid>
<description><![CDATA[ This post details how Lendi Group built their AI-powered Home Loan Guardian using Amazon Bedrock, the challenges they faced, the architecture they implemented, and the significant business outcomes they’ve achieved. Their journey offers valuable insights for organizations that want to use generative AI to transform customer experiences while maintaining the human touch that builds trust and loyalty. ]]></description>
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<pubDate>Tue, 03 Mar 2026 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Lendi, revamped, the, refinance, journey, for, its, customers, using, agentic, weeks, using, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>How Tines enhances security analysis with Amazon Quick Suite</title>
<link>https://news.jatlink.uk/5341</link>
<guid>https://news.jatlink.uk/5341</guid>
<description><![CDATA[ In this post, we show you how to connect Quick Suite with Tines to securely retrieve, analyze, and visualize enterprise data from any security or IT system. We walk through an example that uses a MCP server in Tines to retrieve data from various tools, such as AWS CloudTrail, Okta, and VirusTotal, to remediate security events using Quick Suite. ]]></description>
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<pubDate>Tue, 03 Mar 2026 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Tines, enhances, security, analysis, with, Amazon, Quick, Suite</media:keywords>
</item>

<item>
<title>Build safe generative AI applications like a Pro: Best Practices with Amazon Bedrock Guardrails</title>
<link>https://news.jatlink.uk/5284</link>
<guid>https://news.jatlink.uk/5284</guid>
<description><![CDATA[ In this post, we will show you how to configure Amazon Bedrock Guardrails for efficient performance, implement best practices to protect your applications, and monitor your deployment effectively to maintain the right balance between safety and user experience. ]]></description>
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<pubDate>Mon, 02 Mar 2026 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, safe, generative, applications, like, Pro:, Best, Practices, with, Amazon, Bedrock, Guardrails</media:keywords>
</item>

<item>
<title>Build a serverless conversational AI agent using Claude with LangGraph and managed MLflow on Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/5283</link>
<guid>https://news.jatlink.uk/5283</guid>
<description><![CDATA[ This post explores how to build an intelligent conversational agent using Amazon Bedrock, LangGraph, and managed MLflow on Amazon SageMaker AI. ]]></description>
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<pubDate>Mon, 02 Mar 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, serverless, conversational, agent, using, Claude, with, LangGraph, and, managed, MLflow, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Building specialized AI without sacrificing intelligence: Nova Forge data mixing in action</title>
<link>https://news.jatlink.uk/5282</link>
<guid>https://news.jatlink.uk/5282</guid>
<description><![CDATA[ In this post, we share results from the AWS China Applied Science team&#039;s comprehensive evaluation of Nova Forge using a challenging Voice of Customer (VOC) classification task, benchmarked against open-source models. ]]></description>
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<pubDate>Mon, 02 Mar 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, specialized, without, sacrificing, intelligence: Nova, Forge, data, mixing, action</media:keywords>
</item>

<item>
<title>Learnings from COBOL modernization in the real world</title>
<link>https://news.jatlink.uk/5022</link>
<guid>https://news.jatlink.uk/5022</guid>
<description><![CDATA[ Delivering successful COBOL modernization requires a solution that can reverse engineer deterministically, produce validated and traceable specs, and help those specs flow into any AI-powered coding assistant for the forward engineering. A successful modernization requires both reverse engineering and forward engineering. Learn more about COBOL in this post. ]]></description>
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<pubDate>Thu, 26 Feb 2026 20:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Learnings, from, COBOL, modernization, the, real, world</media:keywords>
</item>

<item>
<title>Reinforcement fine&amp;tuning for Amazon Nova: Teaching AI through feedback</title>
<link>https://news.jatlink.uk/5023</link>
<guid>https://news.jatlink.uk/5023</guid>
<description><![CDATA[ In this post, we explore reinforcement fine-tuning (RFT) for Amazon Nova models, which can be a powerful customization technique that learns through evaluation rather than imitation. We&#039;ll cover how RFT works, when to use it versus supervised fine-tuning, real-world applications from code generation to customer service, and implementation options ranging from fully managed Amazon Bedrock to multi-turn agentic workflows with Nova Forge. You&#039;ll also learn practical guidance on data preparation, reward function design, and best practices for achieving optimal results. ]]></description>
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<pubDate>Thu, 26 Feb 2026 20:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Reinforcement, fine-tuning, for, Amazon, Nova:, Teaching, through, feedback</media:keywords>
</item>

<item>
<title>Large model inference container – latest capabilities and performance enhancements</title>
<link>https://news.jatlink.uk/5024</link>
<guid>https://news.jatlink.uk/5024</guid>
<description><![CDATA[ AWS recently released significant updates to the Large Model Inference (LMI) container, delivering comprehensive performance improvements, expanded model support, and streamlined deployment capabilities for customers hosting LLMs on AWS. These releases focus on reducing operational complexity while delivering measurable performance gains across popular model architectures. ]]></description>
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<pubDate>Thu, 26 Feb 2026 20:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Large, model, inference, container, –, latest, capabilities, and, performance, enhancements</media:keywords>
</item>

<item>
<title>Efficiently serve dozens of fine&amp;tuned models with vLLM on Amazon SageMaker AI and Amazon Bedrock</title>
<link>https://news.jatlink.uk/4942</link>
<guid>https://news.jatlink.uk/4942</guid>
<description><![CDATA[ In this post, we explain how we implemented multi-LoRA inference for Mixture of Experts (MoE) models in vLLM, describe the kernel-level optimizations we performed, and show you how you can benefit from this work. We use GPT-OSS 20B as our primary example throughout this post. ]]></description>
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<pubDate>Wed, 25 Feb 2026 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Efficiently, serve, dozens, fine-tuned, models, with, vLLM, Amazon, SageMaker, and, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Building intelligent event agents using Amazon Bedrock AgentCore and Amazon Bedrock Knowledge Bases</title>
<link>https://news.jatlink.uk/4941</link>
<guid>https://news.jatlink.uk/4941</guid>
<description><![CDATA[ This post demonstrates how to quickly deploy a production-ready event assistant using the components of Amazon Bedrock AgentCore. We&#039;ll build an intelligent companion that remembers attendee preferences and builds personalized experiences over time, while Amazon Bedrock AgentCore handles the heavy lifting of production deployment: Amazon Bedrock AgentCore Memory for maintaining both conversation context and long-term preferences without custom storage solutions, Amazon Bedrock AgentCore Identity for secure multi-IDP authentication, and Amazon Bedrock AgentCore Runtime for serverless scaling and session isolation. We will also use Amazon Bedrock Knowledge Bases for managed RAG and event data retrieval. ]]></description>
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<pubDate>Wed, 25 Feb 2026 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, intelligent, event, agents, using, Amazon, Bedrock, AgentCore, and, Amazon, Bedrock, Knowledge, Bases</media:keywords>
</item>

<item>
<title>Build an intelligent photo search using Amazon Rekognition, Amazon Neptune, and Amazon Bedrock</title>
<link>https://news.jatlink.uk/4864</link>
<guid>https://news.jatlink.uk/4864</guid>
<description><![CDATA[ In this post, we show you how to build a comprehensive photo search system using the AWS Cloud Development Kit (AWS CDK) that integrates Amazon Rekognition for face and object detection, Amazon Neptune for relationship mapping, and Amazon Bedrock for AI-powered captioning. ]]></description>
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<pubDate>Tue, 24 Feb 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, intelligent, photo, search, using, Amazon, Rekognition, Amazon, Neptune, and, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Introducing Amazon Bedrock global cross&amp;Region inference for Anthropic’s Claude models in the Middle East Regions (UAE and Bahrain)</title>
<link>https://news.jatlink.uk/4842</link>
<guid>https://news.jatlink.uk/4842</guid>
<description><![CDATA[ We’re excited to announce the availability of Anthropic’s Claude Opus 4.6, Claude Sonnet 4.6, Claude Opus 4.5, Claude Sonnet 4.5, and Claude Haiku 4.5 through Amazon Bedrock global cross-Region inference for customers operating in the Middle East. In this post, we guide you through the capabilities of each Anthropic Claude model variant, the key advantages of global cross-Region inference including improved resilience, real-world use cases you can implement, and a code example to help you start building generative AI applications immediately. ]]></description>
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<pubDate>Tue, 24 Feb 2026 16:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Amazon, Bedrock, global, cross-Region, inference, for, Anthropic’s, Claude, models, the, Middle, East, Regions, UAE, and, Bahrain</media:keywords>
</item>

<item>
<title>Generate structured output from LLMs with Dottxt Outlines in AWS</title>
<link>https://news.jatlink.uk/4840</link>
<guid>https://news.jatlink.uk/4840</guid>
<description><![CDATA[ This post explores the implementation of Dottxt’s Outlines framework as a practical approach to implementing structured outputs using AWS Marketplace in Amazon SageMaker. ]]></description>
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<pubDate>Tue, 24 Feb 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Generate, structured, output, from, LLMs, with, Dottxt, Outlines, AWS</media:keywords>
</item>

<item>
<title>Train CodeFu&amp;7B with veRL and Ray on Amazon SageMaker Training jobs</title>
<link>https://news.jatlink.uk/4839</link>
<guid>https://news.jatlink.uk/4839</guid>
<description><![CDATA[ In this post, we demonstrate how to train CodeFu-7B, a specialized 7-billion parameter model for competitive programming, using Group Relative Policy Optimization (GRPO) with veRL, a flexible and efficient training library for large language models (LLMs) that enables straightforward extension of diverse RL algorithms and seamless integration with existing LLM infrastructure, within a distributed Ray cluster managed by SageMaker training jobs. We walk through the complete implementation, covering data preparation, distributed training setup, and comprehensive observability, showcasing how this unified approach delivers both computational scale and developer experience for sophisticated RL training workloads. ]]></description>
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<pubDate>Tue, 24 Feb 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Train, CodeFu-7B, with, veRL, and, Ray, Amazon, SageMaker, Training, jobs</media:keywords>
</item>

<item>
<title>Global cross&amp;Region inference for latest Anthropic Claude Opus, Sonnet and Haiku models on Amazon Bedrock in Thailand, Malaysia, Singapore, Indonesia, and Taiwan</title>
<link>https://news.jatlink.uk/4841</link>
<guid>https://news.jatlink.uk/4841</guid>
<description><![CDATA[ In this post, we are exciting to announce availability of Global CRIS for customers in Thailand, Malaysia, Singapore, Indonesia, and Taiwan and give a walkthrough of technical implementation steps, and cover quota management best practices to maximize the value of your AI Inference deployments. We also provide guidance on best practices for production deployments. ]]></description>
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<pubDate>Tue, 24 Feb 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Global, cross-Region, inference, for, latest, Anthropic, Claude, Opus, Sonnet, and, Haiku, models, Amazon, Bedrock, Thailand, Malaysia, Singapore, Indonesia, and, Taiwan</media:keywords>
</item>

<item>
<title>Scaling data annotation using vision&amp;language models to power physical AI systems</title>
<link>https://news.jatlink.uk/4803</link>
<guid>https://news.jatlink.uk/4803</guid>
<description><![CDATA[ In this post, we examine how Bedrock Robotics tackles this challenge. By joining the AWS Physical AI Fellowship, the startup partnered with the AWS Generative AI Innovation Center to apply vision-language models that analyze construction video footage, extract operational details, and generate labeled training datasets at scale, to improve data preparation for autonomous construction equipment. ]]></description>
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<pubDate>Tue, 24 Feb 2026 00:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Scaling, data, annotation, using, vision-language, models, power, physical, systems</media:keywords>
</item>

<item>
<title>Accelerating AI model production at Hexagon with Amazon SageMaker HyperPod</title>
<link>https://news.jatlink.uk/4789</link>
<guid>https://news.jatlink.uk/4789</guid>
<description><![CDATA[ In this blog post, we demonstrate how Hexagon collaborated with Amazon Web Services to scale their AI model production by pretraining state-of-the-art segmentation models, using the model training infrastructure of Amazon SageMaker HyperPod. ]]></description>
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<pubDate>Mon, 23 Feb 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerating, model, production, Hexagon, with, Amazon, SageMaker, HyperPod</media:keywords>
</item>

<item>
<title>How Sonrai uses Amazon SageMaker AI to accelerate precision medicine trials</title>
<link>https://news.jatlink.uk/4788</link>
<guid>https://news.jatlink.uk/4788</guid>
<description><![CDATA[ In this post, we explore how Sonrai, a life sciences AI company, partnered with AWS to build a robust MLOps framework using Amazon SageMaker AI that addresses these challenges while maintaining the traceability and reproducibility required in regulated environments. ]]></description>
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<pubDate>Mon, 23 Feb 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Sonrai, uses, Amazon, SageMaker, accelerate, precision, medicine, trials</media:keywords>
</item>

<item>
<title>Agentic AI with multi&amp;model framework using Hugging Face smolagents on AWS</title>
<link>https://news.jatlink.uk/4763</link>
<guid>https://news.jatlink.uk/4763</guid>
<description><![CDATA[ Hugging Face smolagents is an open source Python library designed to make it straightforward to build and run agents using a few lines of code. We will show you how to build an agentic AI solution by integrating Hugging Face smolagents with Amazon Web Services (AWS) managed services. You&#039;ll learn how to deploy a healthcare AI agent that demonstrates multi-model deployment options, vector-enhanced knowledge retrieval, and clinical decision support capabilities. ]]></description>
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<pubDate>Mon, 23 Feb 2026 16:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Agentic, with, multi-model, framework, using, Hugging, Face, smolagents, AWS</media:keywords>
</item>

<item>
<title>Amazon SageMaker AI in 2025, a year in review part 2: Improved observability and enhanced features for SageMaker AI model customization and hosting</title>
<link>https://news.jatlink.uk/4621</link>
<guid>https://news.jatlink.uk/4621</guid>
<description><![CDATA[ In 2025, Amazon SageMaker AI made several improvements designed to help you train, tune, and host generative AI workloads. In Part 1 of this series, we discussed Flexible Training Plans and price performance improvements made to inference components. In this post, we discuss enhancements made to observability, model customization, and model hosting. These improvements facilitate a whole new class of customer use cases to be hosted on SageMaker AI. ]]></description>
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<pubDate>Fri, 20 Feb 2026 21:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Amazon, SageMaker, 2025, year, review, part, Improved, observability, and, enhanced, features, for, SageMaker, model, customization, and, hosting</media:keywords>
</item>

<item>
<title>Amazon SageMaker AI in 2025, a year in review part 1: Flexible Training Plans and improvements to price performance for inference workloads</title>
<link>https://news.jatlink.uk/4620</link>
<guid>https://news.jatlink.uk/4620</guid>
<description><![CDATA[ In 2025, Amazon SageMaker AI saw dramatic improvements to core infrastructure offerings along four dimensions: capacity, price performance, observability, and usability. In this series of posts, we discuss these various improvements and their benefits. In Part 1, we discuss capacity improvements with the launch of Flexible Training Plans. We also describe improvements to price performance for inference workloads. In Part 2, we discuss enhancements made to observability, model customization, and model hosting. ]]></description>
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<pubDate>Fri, 20 Feb 2026 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Amazon, SageMaker, 2025, year, review, part, Flexible, Training, Plans, and, improvements, price, performance, for, inference, workloads</media:keywords>
</item>

<item>
<title>Integrate external tools with Amazon Quick Agents using Model Context Protocol (MCP)</title>
<link>https://news.jatlink.uk/4598</link>
<guid>https://news.jatlink.uk/4598</guid>
<description><![CDATA[ In this post, you’ll use a six-step checklist to build a new MCP server or validate and adjust an existing MCP server for Amazon Quick integration. The Amazon Quick User Guide describes the MCP client behavior and constraints. This is a “How to” guide for detailed implementation required by 3P partners to integrate with Amazon Quick with MCP. ]]></description>
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<pubDate>Fri, 20 Feb 2026 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Integrate, external, tools, with, Amazon, Quick, Agents, using, Model, Context, Protocol, MCP</media:keywords>
</item>

<item>
<title>Amazon Quick now supports key pair authentication to Snowflake data source</title>
<link>https://news.jatlink.uk/4560</link>
<guid>https://news.jatlink.uk/4560</guid>
<description><![CDATA[ In this blog post, we will guide you through establishing data source connectivity between Amazon Quick Sight and Snowflake through secure key pair authentication. ]]></description>
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<pubDate>Fri, 20 Feb 2026 00:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Amazon, Quick, now, supports, key, pair, authentication, Snowflake, data, source</media:keywords>
</item>

<item>
<title>Amazon Quick Suite now supports key pair authentication to Snowflake data source</title>
<link>https://news.jatlink.uk/4526</link>
<guid>https://news.jatlink.uk/4526</guid>
<description><![CDATA[ In this blog post, we will guide you through establishing data source connectivity between Amazon Quick Sight and Snowflake through secure key pair authentication. ]]></description>
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<pubDate>Thu, 19 Feb 2026 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Amazon, Quick, Suite, now, supports, key, pair, authentication, Snowflake, data, source</media:keywords>
</item>

<item>
<title>Build AI workflows on Amazon EKS with Union.ai and Flyte</title>
<link>https://news.jatlink.uk/4525</link>
<guid>https://news.jatlink.uk/4525</guid>
<description><![CDATA[ In this post, we explain how you can use the Flyte Python SDK to orchestrate and scale AI/ML workflows. We explore how the Union.ai 2.0 system enables deployment of Flyte on Amazon Elastic Kubernetes Service (Amazon EKS), integrating seamlessly with AWS services like Amazon Simple Storage Service (Amazon S3), Amazon Aurora, AWS Identity and Access Management (IAM), and Amazon CloudWatch. We explore the solution through an AI workflow example, using the new Amazon S3 Vectors service. ]]></description>
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<pubDate>Thu, 19 Feb 2026 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, workflows, Amazon, EKS, with, Union.ai, and, Flyte</media:keywords>
</item>

<item>
<title>Build unified intelligence with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/4479</link>
<guid>https://news.jatlink.uk/4479</guid>
<description><![CDATA[ In this post, we demonstrate how to build unified intelligence systems using Amazon Bedrock AgentCore through our real-world implementation of the Customer Agent and Knowledge Engine (CAKE). ]]></description>
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<pubDate>Thu, 19 Feb 2026 00:00:15 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, unified, intelligence, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Evaluating AI agents: Real&amp;world lessons from building agentic systems at Amazon</title>
<link>https://news.jatlink.uk/4463</link>
<guid>https://news.jatlink.uk/4463</guid>
<description><![CDATA[ In this post, we present a comprehensive evaluation framework for Amazon agentic AI systems that addresses the complexity of agentic AI applications at Amazon through two core components: a generic evaluation workflow that standardizes assessment procedures across diverse agent implementations, and an agent evaluation library that provides systematic measurements and metrics in Amazon Bedrock AgentCore Evaluations, along with Amazon use case-specific evaluation approaches and metrics.  ]]></description>
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<pubDate>Wed, 18 Feb 2026 20:00:08 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Evaluating, agents:, Real-world, lessons, from, building, agentic, systems, Amazon</media:keywords>
</item>

<item>
<title>Customize AI agent browsing with proxies, profiles, and extensions in Amazon Bedrock AgentCore Browser</title>
<link>https://news.jatlink.uk/4172</link>
<guid>https://news.jatlink.uk/4172</guid>
<description><![CDATA[ Today, we are announcing three new capabilities that address these requirements: proxy configuration, browser profiles, and browser extensions. Together, these features give you fine-grained control over how your AI agents interact with the web. This post will walk through each capability with configuration examples and practical use cases to help you get started. ]]></description>
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<pubDate>Sat, 14 Feb 2026 00:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Customize, agent, browsing, with, proxies, profiles, and, extensions, Amazon, Bedrock, AgentCore, Browser</media:keywords>
</item>

<item>
<title>AI meets HR: Transforming talent acquisition with Amazon Bedrock</title>
<link>https://news.jatlink.uk/4078</link>
<guid>https://news.jatlink.uk/4078</guid>
<description><![CDATA[ In this post, we show how to create an AI-powered recruitment system using Amazon Bedrock, Amazon Bedrock Knowledge Bases, AWS Lambda, and other AWS services to enhance job description creation, candidate communication, and interview preparation while maintaining human oversight. ]]></description>
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<pubDate>Thu, 12 Feb 2026 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>meets, HR:, Transforming, talent, acquisition, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Build long&amp;running MCP servers on Amazon Bedrock AgentCore with Strands Agents integration</title>
<link>https://news.jatlink.uk/4079</link>
<guid>https://news.jatlink.uk/4079</guid>
<description><![CDATA[ In this post, we provide you with a comprehensive approach to achieve this. First, we introduce a context message strategy that maintains continuous communication between servers and clients during extended operations. Next, we develop an asynchronous task management framework that allows your AI agents to initiate long-running processes without blocking other operations. Finally, we demonstrate how to bring these strategies together with Amazon Bedrock AgentCore and Strands Agents to build production-ready AI agents that can handle complex, time-intensive operations reliably. ]]></description>
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<pubDate>Thu, 12 Feb 2026 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, long-running, MCP, servers, Amazon, Bedrock, AgentCore, with, Strands, Agents, integration</media:keywords>
</item>

<item>
<title>NVIDIA Nemotron 3 Nano 30B MoE model is now available in Amazon SageMaker JumpStart</title>
<link>https://news.jatlink.uk/4004</link>
<guid>https://news.jatlink.uk/4004</guid>
<description><![CDATA[ Today we’re excited to announce that the NVIDIA Nemotron 3 Nano 30B model with  3B active parameters is now generally available in the Amazon SageMaker JumpStart model catalog. You can accelerate innovation and deliver tangible business value with Nemotron 3 Nano on Amazon Web Services (AWS) without having to manage model deployment complexities. You can power your generative AI applications with Nemotron capabilities using the managed deployment capabilities offered by SageMaker JumpStart. ]]></description>
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<pubDate>Wed, 11 Feb 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>NVIDIA, Nemotron, Nano, 30B, MoE, model, now, available, Amazon, SageMaker, JumpStart</media:keywords>
</item>

<item>
<title>Mastering Amazon Bedrock throttling and service availability: A comprehensive guide</title>
<link>https://news.jatlink.uk/3981</link>
<guid>https://news.jatlink.uk/3981</guid>
<description><![CDATA[ This post shows you how to implement robust error handling strategies that can help improve application reliability and user experience when using Amazon Bedrock. We&#039;ll dive deep into strategies for optimizing performances for the application with these errors. Whether this is for a fairly new application or matured AI application, in this post you will be able to find the practical guidelines to operate with on these errors. ]]></description>
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<pubDate>Wed, 11 Feb 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Mastering, Amazon, Bedrock, throttling, and, service, availability:, comprehensive, guide</media:keywords>
</item>

<item>
<title>Swann provides Generative AI to millions of IoT Devices using Amazon Bedrock</title>
<link>https://news.jatlink.uk/3982</link>
<guid>https://news.jatlink.uk/3982</guid>
<description><![CDATA[ This post shows you how to implement intelligent notification filtering using Amazon Bedrock and its gen-AI capabilities. You&#039;ll learn model selection strategies, cost optimization techniques, and architectural patterns for deploying gen-AI at IoT scale, based on Swann Communications deployment across millions of devices. ]]></description>
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<pubDate>Wed, 11 Feb 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Swann, provides, Generative, millions, IoT, Devices, using, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>How LinqAlpha assesses investment theses using Devil’s Advocate on Amazon Bedrock</title>
<link>https://news.jatlink.uk/3983</link>
<guid>https://news.jatlink.uk/3983</guid>
<description><![CDATA[ LinqAlpha is a Boston-based multi-agent AI system built specifically for institutional investors. The system supports and streamlines agentic workflows across company screening, primer generation, stock price catalyst mapping, and now, pressure-testing investment ideas through a new AI agent called Devil’s Advocate. In this post, we share how LinqAlpha uses Amazon Bedrock to build and scale Devil’s Advocate. ]]></description>
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<pubDate>Wed, 11 Feb 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, LinqAlpha, assesses, investment, theses, using, Devil’s, Advocate, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>How Amazon uses Amazon Nova models to automate operational readiness testing for new fulfillment centers</title>
<link>https://news.jatlink.uk/3930</link>
<guid>https://news.jatlink.uk/3930</guid>
<description><![CDATA[ In this post, we discuss how Amazon Nova in Amazon Bedrock can be used to implement an AI-powered image recognition solution that automates the detection and validation of module components, significantly reducing manual verification efforts and improving accuracy. ]]></description>
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<pubDate>Tue, 10 Feb 2026 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Amazon, uses, Amazon, Nova, models, automate, operational, readiness, testing, for, new, fulfillment, centers</media:keywords>
</item>

<item>
<title>Iberdrola enhances IT operations using Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/3931</link>
<guid>https://news.jatlink.uk/3931</guid>
<description><![CDATA[ Iberdrola, one of the world’s largest utility companies, has embraced cutting-edge AI technology to revolutionize its IT operations in ServiceNow. Through its partnership with AWS, Iberdrola implemented different agentic architectures using Amazon Bedrock AgentCore, targeting three key areas: optimizing change request validation in the draft phase, enriching incident management with contextual intelligence, and simplifying change model selection using conversational AI. These innovations reduce bottlenecks, help teams accelerate ticket resolution, and deliver consistent and high-quality data handling throughout the organization. ]]></description>
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<pubDate>Tue, 10 Feb 2026 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Iberdrola, enhances, operations, using, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Building real&amp;time voice assistants with Amazon Nova Sonic compared to cascading architectures</title>
<link>https://news.jatlink.uk/3932</link>
<guid>https://news.jatlink.uk/3932</guid>
<description><![CDATA[ Amazon Nova Sonic delivers real-time, human-like voice conversations through the bidirectional streaming interface. In this post, you learn how Amazon Nova Sonic can solve some of the challenges faced by cascaded approaches, simplify building voice AI agents, and provide natural conversational capabilities. We also provide guidance on when to choose each approach to help you make informed decisions for your voice AI projects. ]]></description>
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<pubDate>Tue, 10 Feb 2026 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, real-time, voice, assistants, with, Amazon, Nova, Sonic, compared, cascading, architectures</media:keywords>
</item>

<item>
<title>Automated Reasoning checks rewriting chatbot reference implementation</title>
<link>https://news.jatlink.uk/3858</link>
<guid>https://news.jatlink.uk/3858</guid>
<description><![CDATA[ This blog post dives deeper into the implementation architecture for the Automated Reasoning checks rewriting chatbot. ]]></description>
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<pubDate>Mon, 09 Feb 2026 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Automated, Reasoning, checks, rewriting, chatbot, reference, implementation</media:keywords>
</item>

<item>
<title>Agent&amp;to&amp;agent collaboration: Using Amazon Nova 2 Lite and Amazon Nova Act for multi&amp;agent systems</title>
<link>https://news.jatlink.uk/3836</link>
<guid>https://news.jatlink.uk/3836</guid>
<description><![CDATA[ This post walks through how agent-to-agent collaboration on Amazon Bedrock works in practice, using Amazon Nova 2 Lite for planning and Amazon Nova Act for browser interaction, to turn a fragile single-agent setup into a predictable multi-agent system. ]]></description>
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<pubDate>Mon, 09 Feb 2026 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Agent-to-agent, collaboration:, Using, Amazon, Nova, Lite, and, Amazon, Nova, Act, for, multi-agent, systems</media:keywords>
</item>

<item>
<title>Scale LLM fine&amp;tuning with Hugging Face and Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/3833</link>
<guid>https://news.jatlink.uk/3833</guid>
<description><![CDATA[ In this post, we show how this integrated approach transforms enterprise LLM fine-tuning from a complex, resource-intensive challenge into a streamlined, scalable solution for achieving better model performance in domain-specific applications. ]]></description>
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<pubDate>Mon, 09 Feb 2026 17:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Scale, LLM, fine-tuning, with, Hugging, Face, and, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>New Relic transforms productivity with generative AI on AWS</title>
<link>https://news.jatlink.uk/3834</link>
<guid>https://news.jatlink.uk/3834</guid>
<description><![CDATA[ Working with the Generative AI Innovation Center, New Relic NOVA (New Relic Omnipresence Virtual Assistant) evolved from a knowledge assistant into a comprehensive productivity engine. We explore the technical architecture, development journey, and key lessons learned in building an enterprise-grade AI solution that delivers measurable productivity gains at scale. ]]></description>
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<pubDate>Mon, 09 Feb 2026 17:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>New, Relic, transforms, productivity, with, generative, AWS</media:keywords>
</item>

<item>
<title>Accelerate agentic application development with a full&amp;stack starter template for Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/3835</link>
<guid>https://news.jatlink.uk/3835</guid>
<description><![CDATA[ In this post, you will learn how to deploy Fullstack AgentCore Solution Template (FAST) to your Amazon Web Services (AWS) account, understand its architecture, and see how to extend it for your requirements. You will learn how to build your own agent while FAST handles authentication, infrastructure as code (IaC), deployment pipelines, and service integration. ]]></description>
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<pubDate>Mon, 09 Feb 2026 17:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerate, agentic, application, development, with, full-stack, starter, template, for, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Structured outputs on Amazon Bedrock: Schema&amp;compliant AI responses</title>
<link>https://news.jatlink.uk/3664</link>
<guid>https://news.jatlink.uk/3664</guid>
<description><![CDATA[ Today, we&#039;re announcing structured outputs on Amazon Bedrock—a capability that fundamentally transforms how you can obtain validated JSON responses from foundation models through constrained decoding for schema compliance. In this post, we explore the challenges of traditional JSON generation and how structured outputs solves them. We cover the two core mechanisms—JSON Schema output format and strict tool use—along with implementation details, best practices, and practical code examples. ]]></description>
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<pubDate>Fri, 06 Feb 2026 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Structured, outputs, Amazon, Bedrock:, Schema-compliant, responses</media:keywords>
</item>

<item>
<title>Manage Amazon SageMaker HyperPod clusters using the HyperPod CLI and SDK</title>
<link>https://news.jatlink.uk/3663</link>
<guid>https://news.jatlink.uk/3663</guid>
<description><![CDATA[ In this post, we demonstrate how to use the CLI and the SDK to create and manage SageMaker HyperPod clusters in your AWS account. We walk through a practical example and dive deeper into the user workflow and parameter choices. ]]></description>
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<pubDate>Fri, 06 Feb 2026 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Manage, Amazon, SageMaker, HyperPod, clusters, using, the, HyperPod, CLI, and, SDK</media:keywords>
</item>

<item>
<title>Evaluate generative AI models with an Amazon Nova rubric&amp;based LLM judge on Amazon SageMaker AI (Part 2)</title>
<link>https://news.jatlink.uk/3641</link>
<guid>https://news.jatlink.uk/3641</guid>
<description><![CDATA[ In this post, we explore the Amazon Nova rubric-based judge feature: what a rubric-based judge is, how the judge is trained, what metrics to consider, and how to calibrate the judge. We chare notebook code of the Amazon Nova rubric-based LLM-as-a-judge methodology to evaluate and compare the outputs of two different LLMs using SageMaker training jobs. ]]></description>
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<pubDate>Fri, 06 Feb 2026 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Evaluate, generative, models, with, Amazon, Nova, rubric-based, LLM, judge, Amazon, SageMaker, Part</media:keywords>
</item>

<item>
<title>A practical guide to Amazon Nova Multimodal Embeddings</title>
<link>https://news.jatlink.uk/3583</link>
<guid>https://news.jatlink.uk/3583</guid>
<description><![CDATA[ In this post, you will learn how to configure and use Amazon Nova Multimodal Embeddings for media asset search systems, product discovery experiences, and document retrieval applications. ]]></description>
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<pubDate>Thu, 05 Feb 2026 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>practical, guide, Amazon, Nova, Multimodal, Embeddings</media:keywords>
</item>

<item>
<title>How Associa transforms document classification with the GenAI IDP Accelerator and Amazon Bedrock</title>
<link>https://news.jatlink.uk/3582</link>
<guid>https://news.jatlink.uk/3582</guid>
<description><![CDATA[ Associa collaborated with the AWS Generative AI Innovation Center to build a generative AI-powered document classification system aligning with Associa’s long-term vision of using generative AI to achieve operational efficiencies in document management. The solution automatically categorizes incoming documents with high accuracy, processes documents efficiently, and provides substantial cost savings while maintaining operational excellence. The document classification system, developed using the Generative AI Intelligent Document Processing (GenAI IDP) Accelerator, is designed to integrate seamlessly into existing workflows. It revolutionizes how employees interact with document management systems by reducing the time spent on manual classification tasks. ]]></description>
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<pubDate>Thu, 05 Feb 2026 21:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Associa, transforms, document, classification, with, the, GenAI, IDP, Accelerator, and, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Accelerating your marketing ideation with generative AI – Part 2: Generate custom marketing images from historical references</title>
<link>https://news.jatlink.uk/3428</link>
<guid>https://news.jatlink.uk/3428</guid>
<description><![CDATA[ Building upon our earlier work of marketing campaign image generation using Amazon Nova foundation models, in this post, we demonstrate how to enhance image generation by learning from previous marketing campaigns. We explore how to integrate Amazon Bedrock, AWS Lambda, and Amazon OpenSearch Serverless to create an advanced image generation system that uses reference campaigns to maintain brand guidelines, deliver consistent content, and enhance the effectiveness and efficiency of new campaign creation. ]]></description>
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<pubDate>Wed, 04 Feb 2026 16:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerating, your, marketing, ideation, with, generative, –, Part, Generate, custom, marketing, images, from, historical, references</media:keywords>
</item>

<item>
<title>Democratizing business intelligence: BGL’s journey with Claude Agent SDK and Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/3426</link>
<guid>https://news.jatlink.uk/3426</guid>
<description><![CDATA[ BGL is a leading provider of self-managed superannuation fund (SMSF) administration solutions that help individuals manage the complex compliance and reporting of their own or a client’s retirement savings, serving over 12,700 businesses across 15 countries. In this blog post, we explore how BGL built its production-ready AI agent using Claude Agent SDK and Amazon Bedrock AgentCore. ]]></description>
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<pubDate>Tue, 03 Feb 2026 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Democratizing, business, intelligence:, BGL’s, journey, with, Claude, Agent, SDK, and, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Use Amazon Quick Suite custom action connectors to upload text files to Google Drive using OpenAPI specification</title>
<link>https://news.jatlink.uk/3425</link>
<guid>https://news.jatlink.uk/3425</guid>
<description><![CDATA[ In this post, we demonstrate how to build a secure file upload solution by integrating Google Drive with Amazon Quick Suite custom connectors using Amazon API Gateway and AWS Lambda. ]]></description>
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<pubDate>Tue, 03 Feb 2026 20:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Use, Amazon, Quick, Suite, custom, action, connectors, upload, text, files, Google, Drive, using, OpenAPI, specification</media:keywords>
</item>

<item>
<title>AI agents in enterprises: Best practices with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/3424</link>
<guid>https://news.jatlink.uk/3424</guid>
<description><![CDATA[ This post explores nine essential best practices for building enterprise AI agents using Amazon Bedrock AgentCore. Amazon Bedrock AgentCore is an agentic platform that provides the services you need to create, deploy, and manage AI agents at scale. In this post, we cover everything from initial scoping to organizational scaling, with practical guidance that you can apply immediately. ]]></description>
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<pubDate>Tue, 03 Feb 2026 19:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>agents, enterprises:, Best, practices, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Agentic AI for healthcare data analysis with Amazon SageMaker Data Agent</title>
<link>https://news.jatlink.uk/3423</link>
<guid>https://news.jatlink.uk/3423</guid>
<description><![CDATA[ On November 21, 2025, Amazon SageMaker introduced a built-in data agent within Amazon SageMaker Unified Studio that transforms large-scale data analysis. In this post, we demonstrate, through a detailed case study of an epidemiologist conducting clinical cohort analysis, how SageMaker Data Agent can help reduce weeks of data preparation into days, and days of analysis development into hours—ultimately accelerating the path from clinical questions to research conclusions. ]]></description>
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<pubDate>Tue, 03 Feb 2026 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Agentic, for, healthcare, data, analysis, with, Amazon, SageMaker, Data, Agent</media:keywords>
</item>

<item>
<title>How Clarus Care uses Amazon Bedrock to deliver conversational contact center interactions</title>
<link>https://news.jatlink.uk/3397</link>
<guid>https://news.jatlink.uk/3397</guid>
<description><![CDATA[ In this post, we illustrate how Clarus Care, a healthcare contact center solutions provider, worked with the AWS Generative AI Innovation Center (GenAIIC) team to develop a generative AI-powered contact center prototype. This solution enables conversational interaction and multi-intent resolution through an automated voicebot and chat interface. It also incorporates a scalable service model to support growth, human transfer capabilities--when requested or for urgent cases--and an analytics pipeline for performance insights. ]]></description>
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<pubDate>Mon, 02 Feb 2026 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Clarus, Care, uses, Amazon, Bedrock, deliver, conversational, contact, center, interactions</media:keywords>
</item>

<item>
<title>Evaluating generative AI models with Amazon Nova LLM&amp;as&amp;a&amp;Judge on Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/3395</link>
<guid>https://news.jatlink.uk/3395</guid>
<description><![CDATA[ Evaluating the performance of large language models (LLMs) goes beyond statistical metrics like perplexity or bilingual evaluation understudy (BLEU) scores. For most real-world generative AI scenarios, it’s crucial to understand whether a model is producing better outputs than a baseline or an earlier iteration. This is especially important for applications such as summarization, content generation, […] ]]></description>
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<pubDate>Fri, 30 Jan 2026 22:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Evaluating, generative, models, with, Amazon, Nova, LLM-as-a-Judge, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Simplify ModelOps with Amazon SageMaker AI Projects using Amazon S3&amp;based templates</title>
<link>https://news.jatlink.uk/3393</link>
<guid>https://news.jatlink.uk/3393</guid>
<description><![CDATA[ This post explores how you can use Amazon S3-based templates to simplify ModelOps workflows, walk through the key benefits compared to using Service Catalog approaches, and demonstrates how to create a custom ModelOps solution that integrates with GitHub and GitHub Actions—giving your team one-click provisioning of a fully functional ML environment. ]]></description>
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<pubDate>Fri, 30 Jan 2026 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Simplify, ModelOps, with, Amazon, SageMaker, Projects, using, Amazon, S3-based, templates</media:keywords>
</item>

<item>
<title>Scale AI in South Africa using Amazon Bedrock global cross&amp;Region inference with Anthropic Claude 4.5 models</title>
<link>https://news.jatlink.uk/3394</link>
<guid>https://news.jatlink.uk/3394</guid>
<description><![CDATA[ In this post, we walk through how global cross-Region inference routes requests and where your data resides, then show you how to configure the required AWS Identity and Access Management (IAM) permissions and invoke Claude 4.5 models using the global inference profile Amazon Resource Name (ARN). We also cover how to request quota increases for your workload. By the end, you&#039;ll have a working implementation of global cross-Region inference in af-south-1. ]]></description>
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<pubDate>Fri, 30 Jan 2026 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Scale, South, Africa, using, Amazon, Bedrock, global, cross-Region, inference, with, Anthropic Claude 4.5, models</media:keywords>
</item>

<item>
<title>Scaling content review operations with multi&amp;agent workflow</title>
<link>https://news.jatlink.uk/3392</link>
<guid>https://news.jatlink.uk/3392</guid>
<description><![CDATA[ The agent-based approach we present is applicable to any type of enterprise content, from product documentation and knowledge bases to marketing materials and technical specifications. To demonstrate these concepts in action, we walk through a practical example of reviewing blog content for technical accuracy. These patterns and techniques can be directly adapted to various content review needs by adjusting the agent configurations, tools, and verification sources. ]]></description>
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<pubDate>Fri, 30 Jan 2026 00:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Scaling, content, review, operations, with, multi-agent, workflow</media:keywords>
</item>

<item>
<title>Build reliable Agentic AI solution with Amazon Bedrock: Learn from Pushpay’s journey on GenAI evaluation</title>
<link>https://news.jatlink.uk/3384</link>
<guid>https://news.jatlink.uk/3384</guid>
<description><![CDATA[ In this post, we walk you through Pushpay&#039;s journey in building this solution and explore how Pushpay used Amazon Bedrock to create a custom generative AI evaluation framework for continuous quality assurance and establishing rapid iteration feedback loops on AWS. ]]></description>
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<pubDate>Tue, 27 Jan 2026 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, reliable, Agentic, solution, with, Amazon, Bedrock:, Learn, from, Pushpay’s, journey, GenAI, evaluation</media:keywords>
</item>

<item>
<title>Build an intelligent contract management solution with Amazon Quick Suite and Bedrock AgentCore</title>
<link>https://news.jatlink.uk/3382</link>
<guid>https://news.jatlink.uk/3382</guid>
<description><![CDATA[ This blog post demonstrates how to build an intelligent contract management solution using Amazon Quick Suite as your primary contract management solution, augmented with Amazon Bedrock AgentCore for advanced multi-agent capabilities. ]]></description>
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<pubDate>Tue, 27 Jan 2026 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, intelligent, contract, management, solution, with, Amazon, Quick, Suite, and, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Build a serverless AI Gateway architecture with AWS AppSync Events</title>
<link>https://news.jatlink.uk/3379</link>
<guid>https://news.jatlink.uk/3379</guid>
<description><![CDATA[ In this post, we discuss how to use AppSync Events as the foundation of a capable, serverless, AI gateway architecture. We explore how it integrates with AWS services for comprehensive coverage of the capabilities offered in AI gateway architectures. Finally, we get you started on your journey with sample code you can launch in your account and begin building. ]]></description>
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<pubDate>Mon, 26 Jan 2026 18:00:39 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, serverless, Gateway, architecture, with, AWS, AppSync, Events</media:keywords>
</item>

<item>
<title>How Totogi automated change request processing with Totogi BSS Magic and Amazon Bedrock</title>
<link>https://news.jatlink.uk/3378</link>
<guid>https://news.jatlink.uk/3378</guid>
<description><![CDATA[ This blog post describes how Totogi automates change request processing by partnering with the AWS Generative AI Innovation Center and using the rapid innovation capabilities of Amazon Bedrock. ]]></description>
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<pubDate>Mon, 26 Jan 2026 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Totogi, automated, change, request, processing, with, Totogi, BSS, Magic, and, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>How the Amazon.com Catalog Team built self&amp;learning generative AI at scale with Amazon Bedrock</title>
<link>https://news.jatlink.uk/3375</link>
<guid>https://news.jatlink.uk/3375</guid>
<description><![CDATA[ In this post, we demonstrate how the Amazon Catalog Team built a self-learning system that continuously improves accuracy while reducing costs at scale using Amazon Bedrock. ]]></description>
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<pubDate>Fri, 23 Jan 2026 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, the, Amazon.com, Catalog, Team, built, self-learning, generative, scale, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Build AI agents with Amazon Bedrock AgentCore using AWS CloudFormation</title>
<link>https://news.jatlink.uk/3374</link>
<guid>https://news.jatlink.uk/3374</guid>
<description><![CDATA[ Amazon Bedrock AgentCore services are now being supported by various IaC frameworks such as AWS Cloud Development Kit (AWS CDK), Terraform and AWS CloudFormation Templates. This integration brings the power of IaC directly to AgentCore so developers can provision, configure, and manage their AI agent infrastructure. In this post, we use CloudFormation templates to build an end-to-end application for a weather activity planner. ]]></description>
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<pubDate>Fri, 23 Jan 2026 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, agents, with, Amazon, Bedrock, AgentCore, using, AWS, CloudFormation</media:keywords>
</item>

<item>
<title>How CLICKFORCE accelerates data&amp;driven advertising with Amazon Bedrock Agents</title>
<link>https://news.jatlink.uk/3372</link>
<guid>https://news.jatlink.uk/3372</guid>
<description><![CDATA[ In this post, we demonstrate how CLICKFORCE used AWS services to build Lumos and transform advertising industry analysis from weeks-long manual work into an automated, one-hour process. ]]></description>
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<pubDate>Thu, 22 Jan 2026 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, CLICKFORCE, accelerates, data-driven, advertising, with, Amazon, Bedrock, Agents</media:keywords>
</item>

<item>
<title>How PDI built an enterprise&amp;grade RAG system for AI applications with AWS</title>
<link>https://news.jatlink.uk/3371</link>
<guid>https://news.jatlink.uk/3371</guid>
<description><![CDATA[ PDI Technologies is a global leader in the convenience retail and petroleum wholesale industries. In this post, we walk through the PDI Intelligence Query (PDIQ) process flow and architecture, focusing on the implementation details and the business outcomes it has helped PDI achieve. ]]></description>
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<pubDate>Thu, 22 Jan 2026 18:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, PDI, built, enterprise-grade, RAG, system, for, applications, with, AWS</media:keywords>
</item>

<item>
<title>How Thomson Reuters built an Agentic Platform Engineering Hub with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/3369</link>
<guid>https://news.jatlink.uk/3369</guid>
<description><![CDATA[ This blog post explains how TR&#039;s Platform Engineering team, a geographically distributed unit overseeing TR&#039;s service availability, boosted its operational productivity by transitioning from manual to an automated agentic system using Amazon Bedrock AgentCore. ]]></description>
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<pubDate>Wed, 21 Jan 2026 22:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Thomson, Reuters, built, Agentic, Platform, Engineering, Hub, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Build agents to learn from experiences using Amazon Bedrock AgentCore episodic memory</title>
<link>https://news.jatlink.uk/3368</link>
<guid>https://news.jatlink.uk/3368</guid>
<description><![CDATA[ In this post, we walk you through the complete architecture to structure and store episodes, discuss the reflection module, and share compelling benchmarks that demonstrate significant improvements in agent task success rates. ]]></description>
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<pubDate>Wed, 21 Jan 2026 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, agents, learn, from, experiences, using, Amazon, Bedrock, AgentCore, episodic, memory</media:keywords>
</item>

<item>
<title>How bunq handles 97% of support with Amazon Bedrock</title>
<link>https://news.jatlink.uk/3365</link>
<guid>https://news.jatlink.uk/3365</guid>
<description><![CDATA[ In this post, we show how bunq upgraded Finn, its in-house generative AI assistant, using Amazon Bedrock to transform user support and banking operations to be seamless, in multiple languages and time zones. ]]></description>
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<pubDate>Wed, 21 Jan 2026 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, bunq, handles, 97, support, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Using Strands Agents to create a multi&amp;agent solution with Meta’s Llama 4 and Amazon Bedrock</title>
<link>https://news.jatlink.uk/3366</link>
<guid>https://news.jatlink.uk/3366</guid>
<description><![CDATA[ In this post, we explore how to build a multi-agent video processing workflow using Strands Agents, Meta&#039;s Llama 4 models, and Amazon Bedrock to automatically analyze and understand video content through specialized AI agents working in coordination. To showcase the solution, we will use Amazon SageMaker AI to walk you through the code. ]]></description>
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<pubDate>Wed, 21 Jan 2026 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Using, Strands, Agents, create, multi-agent, solution, with, Meta’s, Llama, and, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Introducing multimodal retrieval for Amazon Bedrock Knowledge Bases</title>
<link>https://news.jatlink.uk/3322</link>
<guid>https://news.jatlink.uk/3322</guid>
<description><![CDATA[ In this post, we&#039;ll guide you through building multimodal RAG applications. You&#039;ll learn how multimodal knowledge bases work, how to choose the right processing strategy based on your content type, and how to configure and implement multimodal retrieval using both the console and code examples. ]]></description>
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<pubDate>Tue, 20 Jan 2026 19:00:11 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, multimodal, retrieval, for, Amazon, Bedrock, Knowledge, Bases</media:keywords>
</item>

<item>
<title>Deploy AI agents on Amazon Bedrock AgentCore using GitHub Actions</title>
<link>https://news.jatlink.uk/3314</link>
<guid>https://news.jatlink.uk/3314</guid>
<description><![CDATA[ In this post, we demonstrate how to use a GitHub Actions workflow to automate the deployment of AI agents on AgentCore Runtime. This approach delivers a scalable solution with enterprise-level security controls, providing complete continuous integration and delivery (CI/CD) automation. ]]></description>
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<pubDate>Fri, 16 Jan 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Deploy, agents, Amazon, Bedrock, AgentCore, using, GitHub, Actions</media:keywords>
</item>

<item>
<title>How Palo Alto Networks enhanced device security infra log analysis with Amazon Bedrock</title>
<link>https://news.jatlink.uk/3312</link>
<guid>https://news.jatlink.uk/3312</guid>
<description><![CDATA[ Palo Alto Networks’ Device Security team wanted to detect early warning signs of potential production issues to provide more time to SMEs to react to these emerging problems. They partnered with the AWS Generative AI Innovation Center (GenAIIC) to develop an automated log classification pipeline powered by Amazon Bedrock. In this post, we discuss how Amazon Bedrock, through Anthropic’ s Claude Haiku model, and Amazon Titan Text Embeddings work together to automatically classify and analyze log data. We explore how this automated pipeline detects critical issues, examine the solution architecture, and share implementation insights that have delivered measurable operational improvements. ]]></description>
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<pubDate>Fri, 16 Jan 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Palo, Alto, Networks, enhanced, device, security, infra, log, analysis, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>From beginner to champion: A student’s journey through the AWS AI League ASEAN finals</title>
<link>https://news.jatlink.uk/3313</link>
<guid>https://news.jatlink.uk/3313</guid>
<description><![CDATA[ The AWS AI League, launched by Amazon Web Services (AWS), expanded its reach to the Association of Southeast Asian Nations (ASEAN) last year, welcoming student participants from Singapore, Indonesia, Malaysia, Thailand, Vietnam, and the Philippines. In this blog post, you’ll hear directly from the AWS AI League champion, Blix D. Foryasen, as he shares his reflection on the challenges, breakthroughs, and key lessons discovered throughout the competition. ]]></description>
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<pubDate>Fri, 16 Jan 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>From, beginner, champion:, student’s, journey, through, the, AWS, League, ASEAN, finals</media:keywords>
</item>

<item>
<title>Advanced fine&amp;tuning techniques for multi&amp;agent orchestration: Patterns from Amazon at scale</title>
<link>https://news.jatlink.uk/3311</link>
<guid>https://news.jatlink.uk/3311</guid>
<description><![CDATA[ In this post, we show you how fine-tuning enabled a 33% reduction in dangerous medication errors (Amazon Pharmacy), engineering 80% human effort reduction (Amazon Global Engineering Services), and content quality assessments improving 77% to 96% accuracy (Amazon A+). This post details the techniques behind these outcomes: from foundational methods like Supervised Fine-Tuning (SFT) (instruction tuning), and Proximal Policy Optimization (PPO), to Direct Preference Optimization (DPO) for human alignment, to cutting-edge reasoning optimizations such as Grouped-based Reinforcement Learning from Policy Optimization (GRPO), Direct Advantage Policy Optimization (DAPO), and Group Sequence Policy Optimization (GSPO) purpose-built for agentic systems. ]]></description>
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<pubDate>Fri, 16 Jan 2026 16:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Advanced, fine-tuning, techniques, for, multi-agent, orchestration:, Patterns, from, Amazon, scale</media:keywords>
</item>

<item>
<title>Safeguard generative AI applications with Amazon Bedrock Guardrails</title>
<link>https://news.jatlink.uk/3308</link>
<guid>https://news.jatlink.uk/3308</guid>
<description><![CDATA[ In this post, we demonstrate how you can address these challenges by adding centralized safeguards to a custom multi-provider generative AI gateway using Amazon Bedrock Guardrails. ]]></description>
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<pubDate>Thu, 15 Jan 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Safeguard, generative, applications, with, Amazon, Bedrock, Guardrails</media:keywords>
</item>

<item>
<title>Scale creative asset discovery with Amazon Nova Multimodal Embeddings unified vector search</title>
<link>https://news.jatlink.uk/3309</link>
<guid>https://news.jatlink.uk/3309</guid>
<description><![CDATA[ In this post, we describe how you can use Amazon Nova Multimodal Embeddings to retrieve specific video segments. We also review a real-world use case in which Nova Multimodal Embeddings achieved a recall success rate of 96.7% and a high-precision recall of 73.3% (returning the target content in the top two results) when tested against a library of 170 gaming creative assets. The model also demonstrates strong cross-language capabilities with minimal performance degradation across multiple languages. ]]></description>
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<pubDate>Thu, 15 Jan 2026 16:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Scale, creative, asset, discovery, with, Amazon, Nova, Multimodal, Embeddings, unified, vector, search</media:keywords>
</item>

<item>
<title>Build a generative AI&amp;powered business reporting solution with Amazon Bedrock</title>
<link>https://news.jatlink.uk/3307</link>
<guid>https://news.jatlink.uk/3307</guid>
<description><![CDATA[ This post introduces generative AI guided business reporting—with a focus on writing achievements &amp; challenges about your business—providing a smart, practical solution that helps simplify and accelerate internal communication and reporting. ]]></description>
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<pubDate>Thu, 15 Jan 2026 16:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, generative, AI-powered, business, reporting, solution, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>How the Amazon AMET Payments team accelerates test case generation with Strands Agents</title>
<link>https://news.jatlink.uk/3306</link>
<guid>https://news.jatlink.uk/3306</guid>
<description><![CDATA[ In this post, we explain how we overcame the limitations of single-agent AI systems through a human-centric approach, implemented structured outputs to significantly reduce hallucinations and built a scalable solution now positioned for expansion across the AMET QA team and later across other QA teams in International Emerging Stores and Payments (IESP) Org. ]]></description>
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<pubDate>Thu, 15 Jan 2026 16:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, the, Amazon, AMET, Payments, team, accelerates, test, case, generation, with, Strands, Agents</media:keywords>
</item>

<item>
<title>Transform AI development with new Amazon SageMaker AI model customization and large&amp;scale training capabilities</title>
<link>https://news.jatlink.uk/3304</link>
<guid>https://news.jatlink.uk/3304</guid>
<description><![CDATA[ This post explores how new serverless model customization capabilities, elastic training, checkpointless training, and serverless MLflow work together to accelerate your AI development from months to days. ]]></description>
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<pubDate>Wed, 14 Jan 2026 22:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Transform, development, with, new, Amazon, SageMaker, model, customization, and, large-scale, training, capabilities</media:keywords>
</item>

<item>
<title>How AutoScout24 built a Bot Factory to standardize AI agent development with Amazon Bedrock</title>
<link>https://news.jatlink.uk/3303</link>
<guid>https://news.jatlink.uk/3303</guid>
<description><![CDATA[ In this post, we explore the architecture that AutoScout24 used to build their standardized AI development framework, enabling rapid deployment of secure and scalable AI agents. ]]></description>
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<pubDate>Wed, 14 Jan 2026 22:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, AutoScout24, built, Bot, Factory, standardize, agent, development, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Securing Amazon Bedrock cross&amp;Region inference: Geographic and global</title>
<link>https://news.jatlink.uk/3300</link>
<guid>https://news.jatlink.uk/3300</guid>
<description><![CDATA[ In this post, we explore the security considerations and best practices for implementing Amazon Bedrock cross-Region inference profiles. Whether you&#039;re building a generative AI application or need to meet specific regional compliance requirements, this guide will help you understand the secure architecture of Amazon Bedrock CRIS and how to properly configure your implementation. ]]></description>
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<pubDate>Wed, 14 Jan 2026 00:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Securing, Amazon, Bedrock, cross-Region, inference:, Geographic, and, global</media:keywords>
</item>

<item>
<title>How Beekeeper by LumApps optimized user personalization with Amazon Bedrock</title>
<link>https://news.jatlink.uk/3299</link>
<guid>https://news.jatlink.uk/3299</guid>
<description><![CDATA[ Beekeeper’s automated leaderboard approach and human feedback loop system for dynamic LLM and prompt pair selection addresses the key challenges organizations face in navigating the rapidly evolving landscape of language models. ]]></description>
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<pubDate>Mon, 12 Jan 2026 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Beekeeper, LumApps, optimized, user, personalization, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>How Omada Health scaled patient care by fine&amp;tuning Llama models on Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/3298</link>
<guid>https://news.jatlink.uk/3298</guid>
<description><![CDATA[ This post is co-written with Sunaina Kavi, AI/ML Product Manager at Omada Health. Omada Health, a longtime innovator in virtual healthcare delivery, launched a new nutrition experience in 2025, featuring OmadaSpark, an AI agent trained with robust clinical input that delivers real-time motivational interviewing and nutrition education. It was built on AWS. OmadaSpark was designed […] ]]></description>
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<pubDate>Mon, 12 Jan 2026 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Omada, Health, scaled, patient, care, fine-tuning, Llama, models, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Crossmodal search with Amazon Nova Multimodal Embeddings</title>
<link>https://news.jatlink.uk/3297</link>
<guid>https://news.jatlink.uk/3297</guid>
<description><![CDATA[ In this post, we explore how Amazon Nova Multimodal Embeddings addresses the challenges of crossmodal search through a practical ecommerce use case. We examine the technical limitations of traditional approaches and demonstrate how Amazon Nova Multimodal Embeddings enables retrieval across text, images, and other modalities. You learn how to implement a crossmodal search system by generating embeddings, handling queries, and measuring performance. We provide working code examples and share how to add these capabilities to your applications. ]]></description>
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<pubDate>Sat, 10 Jan 2026 01:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Crossmodal, search, with, Amazon, Nova, Multimodal, Embeddings</media:keywords>
</item>

<item>
<title>Accelerating LLM inference with post&amp;training weight and activation using AWQ and GPTQ on Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/3295</link>
<guid>https://news.jatlink.uk/3295</guid>
<description><![CDATA[ Quantized models can be seamlessly deployed on Amazon SageMaker AI using a few lines of code. In this post, we explore why quantization matters—how it enables lower-cost inference, supports deployment on resource-constrained hardware, and reduces both the financial and environmental impact of modern LLMs, while preserving most of their original performance. We also take a deep dive into the principles behind PTQ and demonstrate how to quantize the model of your choice and deploy it on Amazon SageMaker. ]]></description>
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<pubDate>Fri, 09 Jan 2026 19:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerating, LLM, inference, with, post-training, weight, and, activation, using, AWQ, and, GPTQ, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Sentiment Analysis with Text and Audio Using AWS Generative AI Services: Approaches, Challenges, and Solutions</title>
<link>https://news.jatlink.uk/3293</link>
<guid>https://news.jatlink.uk/3293</guid>
<description><![CDATA[ This post, developed through a strategic scientific partnership between AWS and the Instituto de Ciência e Tecnologia Itaú (ICTi), P&amp;D hub maintained by Itaú Unibanco, the largest private bank in Latin America, explores the technical aspects of sentiment analysis for both text and audio. We present experiments comparing multiple machine learning (ML) models and services, discuss the trade-offs and pitfalls of each approach, and highlight how AWS services can be orchestrated to build robust, end-to-end solutions. We also offer insights into potential future directions, including more advanced prompt engineering for large language models (LLMs) and expanding the scope of audio-based analysis to capture emotional cues that text data alone might miss. ]]></description>
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<pubDate>Fri, 09 Jan 2026 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Sentiment, Analysis, with, Text, and, Audio, Using, AWS, Generative, Services:, Approaches, Challenges, and, Solutions</media:keywords>
</item>

<item>
<title>Architecting TrueLook’s AI&amp;powered construction safety system on Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/3294</link>
<guid>https://news.jatlink.uk/3294</guid>
<description><![CDATA[ This post provides a detailed architectural overview of how TrueLook built its AI-powered safety monitoring system using SageMaker AI, highlighting key technical decisions, pipeline design patterns, and MLOps best practices. You will gain valuable insights into designing scalable computer vision solutions on AWS, particularly around model training workflows, automated pipeline creation, and production deployment strategies for real-time inference. ]]></description>
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<pubDate>Fri, 09 Jan 2026 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Architecting, TrueLook’s, AI-powered, construction, safety, system, Amazon, SageMaker AI</media:keywords>
</item>

<item>
<title>How Beekeeper optimized user personalization with Amazon Bedrock</title>
<link>https://news.jatlink.uk/3292</link>
<guid>https://news.jatlink.uk/3292</guid>
<description><![CDATA[ Beekeeper’s automated leaderboard approach and human feedback loop system for dynamic LLM and prompt pair selection addresses the key challenges organizations face in navigating the rapidly evolving landscape of language models. ]]></description>
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<pubDate>Fri, 09 Jan 2026 17:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Beekeeper, optimized, user, personalization, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Scaling medical content review at Flo Health using Amazon Bedrock (Part 1)</title>
<link>https://news.jatlink.uk/3288</link>
<guid>https://news.jatlink.uk/3288</guid>
<description><![CDATA[ This two-part series explores Flo Health&#039;s journey with generative AI for medical content verification. Part 1 examines our proof of concept (PoC), including the initial solution, capabilities, and early results. Part 2 covers focusing on scaling challenges and real-world implementation. Each article stands alone while collectively showing how AI transforms medical content management at scale. ]]></description>
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<pubDate>Thu, 08 Jan 2026 19:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Scaling, medical, content, review, Flo, Health, using, Amazon, Bedrock, Part</media:keywords>
</item>

<item>
<title>Detect and redact personally identifiable information using Amazon Bedrock Data Automation and Guardrails</title>
<link>https://news.jatlink.uk/3286</link>
<guid>https://news.jatlink.uk/3286</guid>
<description><![CDATA[ This post shows an automated PII detection and redaction solution using Amazon Bedrock Data Automation and Amazon Bedrock Guardrails through a use case of processing text and image content in high volumes of incoming emails and attachments. The solution features a complete email processing workflow with a React-based user interface for authorized personnel to more securely manage and review redacted email communications and attachments. We walk through the step-by-step solution implementation procedures used to deploy this solution. Finally, we discuss the solution benefits, including operational efficiency, scalability, security and compliance, and adaptability. ]]></description>
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<pubDate>Thu, 08 Jan 2026 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Detect, and, redact, personally, identifiable, information, using, Amazon, Bedrock, Data, Automation, and, Guardrails</media:keywords>
</item>

<item>
<title>Speed meets scale: Load testing SageMakerAI endpoints with Observe.AI’s testing tool</title>
<link>https://news.jatlink.uk/3287</link>
<guid>https://news.jatlink.uk/3287</guid>
<description><![CDATA[ Observe.ai developed the One Load Audit Framework (OLAF), which integrates with SageMaker to identify bottlenecks and performance issues in ML services, offering latency and throughput measurements under both static and dynamic data loads. In this blog post, you will learn how to use the OLAF utility to test and validate your SageMaker endpoint. ]]></description>
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<pubDate>Thu, 08 Jan 2026 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Speed, meets, scale:, Load, testing, SageMakerAI, endpoints, with, Observe.AI’s, testing, tool</media:keywords>
</item>

<item>
<title>Migrate MLflow tracking servers to Amazon SageMaker AI with serverless MLflow</title>
<link>https://news.jatlink.uk/3282</link>
<guid>https://news.jatlink.uk/3282</guid>
<description><![CDATA[ This post shows you how to migrate your self-managed MLflow tracking server to a MLflow App – a serverless tracking server on SageMaker AI that automatically scales resources based on demand while removing server patching and storage management tasks at no cost. Learn how to use the MLflow Export Import tool to transfer your experiments, runs, models, and other MLflow resources, including instructions to validate your migration&#039;s success. ]]></description>
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<pubDate>Mon, 29 Dec 2025 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Migrate, MLflow, tracking, servers, Amazon, SageMaker, with, serverless, MLflow</media:keywords>
</item>

<item>
<title>Build an AI&amp;powered website assistant with Amazon Bedrock</title>
<link>https://news.jatlink.uk/3281</link>
<guid>https://news.jatlink.uk/3281</guid>
<description><![CDATA[ This post demonstrates how to solve this challenge by building an AI-powered website assistant using Amazon Bedrock and Amazon Bedrock Knowledge Bases. ]]></description>
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<pubDate>Mon, 29 Dec 2025 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, AI-powered, website, assistant, with, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Agentic QA automation using Amazon Bedrock AgentCore Browser and Amazon Nova Act</title>
<link>https://news.jatlink.uk/3279</link>
<guid>https://news.jatlink.uk/3279</guid>
<description><![CDATA[ In this post, we explore how agentic QA automation addresses these challenges and walk through a practical example using Amazon Bedrock AgentCore Browser and Amazon Nova Act to automate testing for a sample retail application. ]]></description>
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<pubDate>Wed, 24 Dec 2025 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Agentic, automation, using, Amazon, Bedrock, AgentCore, Browser, and, Amazon, Nova, Act</media:keywords>
</item>

<item>
<title>Optimizing LLM inference on Amazon SageMaker AI with BentoML’s LLM&amp; Optimizer</title>
<link>https://news.jatlink.uk/3280</link>
<guid>https://news.jatlink.uk/3280</guid>
<description><![CDATA[ In this post, we demonstrate how to optimize large language model (LLM) inference on Amazon SageMaker AI using BentoML&#039;s LLM-Optimizer to systematically identify the best serving configurations for your workload. ]]></description>
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<pubDate>Wed, 24 Dec 2025 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Optimizing, LLM, inference, Amazon, SageMaker, with, BentoML’s, LLM-, Optimizer</media:keywords>
</item>

<item>
<title>AI agent&amp;driven browser automation for enterprise workflow management</title>
<link>https://news.jatlink.uk/3278</link>
<guid>https://news.jatlink.uk/3278</guid>
<description><![CDATA[ Enterprise organizations increasingly rely on web-based applications for critical business processes, yet many workflows remain manually intensive, creating operational inefficiencies and compliance risks. Despite significant technology investments, knowledge workers routinely navigate between eight to twelve different web applications during standard workflows, constantly switching contexts and manually transferring information between systems. Data entry and validation tasks […] ]]></description>
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<pubDate>Wed, 24 Dec 2025 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>agent-driven, browser, automation, for, enterprise, workflow, management</media:keywords>
</item>

<item>
<title>Programmatically creating an IDP solution with Amazon Bedrock Data Automation</title>
<link>https://news.jatlink.uk/3277</link>
<guid>https://news.jatlink.uk/3277</guid>
<description><![CDATA[ In this post, we explore how to programmatically create an IDP solution that uses Strands SDK, Amazon Bedrock AgentCore, Amazon Bedrock Knowledge Base, and Bedrock Data Automation (BDA). This solution is provided through a Jupyter notebook that enables users to upload multi-modal business documents and extract insights using BDA as a parser to retrieve relevant chunks and augment a prompt to a foundational model (FM). ]]></description>
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<pubDate>Wed, 24 Dec 2025 18:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Programmatically, creating, IDP, solution, with, Amazon, Bedrock, Data, Automation</media:keywords>
</item>

<item>
<title>Exploring the zero operator access design of Mantle</title>
<link>https://news.jatlink.uk/3276</link>
<guid>https://news.jatlink.uk/3276</guid>
<description><![CDATA[ In this post, we explore how Mantle, Amazon&#039;s next-generation inference engine for Amazon Bedrock, implements a zero operator access (ZOA) design that eliminates any technical means for AWS operators to access customer data. ]]></description>
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<pubDate>Tue, 23 Dec 2025 23:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Exploring, the, zero, operator, access, design, Mantle</media:keywords>
</item>

<item>
<title>How dLocal automated compliance reviews using Amazon Quick Automate</title>
<link>https://news.jatlink.uk/3273</link>
<guid>https://news.jatlink.uk/3273</guid>
<description><![CDATA[ In this post, we share how dLocal worked closely with the AWS team to help shape the product roadmap, reinforce its role as an industry innovator, and set new benchmarks for operational excellence in the global fintech landscape. ]]></description>
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<pubDate>Tue, 23 Dec 2025 18:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, dLocal, automated, compliance, reviews, using, Amazon, Quick, Automate</media:keywords>
</item>

<item>
<title>Advancing ADHD diagnosis: How Qbtech built a mobile AI assessment Model Using Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/3274</link>
<guid>https://news.jatlink.uk/3274</guid>
<description><![CDATA[ In this post, we explore how Qbtech streamlined their machine learning (ML) workflow using Amazon SageMaker AI, a fully managed service to build, train and deploy ML models, and AWS Glue, a serverless service that makes data integration simpler, faster, and more cost effective. This new solution reduced their feature engineering time from weeks to hours, while maintaining the high clinical standards required by healthcare providers. ]]></description>
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<pubDate>Tue, 23 Dec 2025 18:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Advancing, ADHD, diagnosis:, How, Qbtech, built, mobile, assessment, Model, Using, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Accelerating your marketing ideation with generative AI – Part 1: From idea to generation with the Amazon Nova foundation models</title>
<link>https://news.jatlink.uk/3275</link>
<guid>https://news.jatlink.uk/3275</guid>
<description><![CDATA[ In this post, the first of a series of three, we focus on how you can use Amazon Nova to streamline, simplify, and accelerate marketing campaign creation through generative AI. We show how Bancolombia, one of Colombia’s largest banks, is experimenting with the Amazon Nova models to generate visuals for their marketing campaigns. ]]></description>
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<pubDate>Tue, 23 Dec 2025 18:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerating, your, marketing, ideation, with, generative, –, Part, From, idea, generation, with, the, Amazon, Nova, foundation, models</media:keywords>
</item>

<item>
<title>AWS AI League: Model customization and agentic showdown</title>
<link>https://news.jatlink.uk/3271</link>
<guid>https://news.jatlink.uk/3271</guid>
<description><![CDATA[ In this post, we explore the new AWS AI League challenges and how they are transforming how organizations approach AI development. The grand finale at AWS re:Invent 2025 was an exciting showcase of their ingenuity and skills. ]]></description>
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<pubDate>Tue, 23 Dec 2025 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>AWS, League:, Model, customization, and, agentic, showdown</media:keywords>
</item>

<item>
<title>Accelerate Enterprise AI Development using Weights &amp;amp; Biases and Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/3272</link>
<guid>https://news.jatlink.uk/3272</guid>
<description><![CDATA[ In this post, we demonstrate how to use Foundation Models (FMs) from Amazon Bedrock and the newly launched Amazon Bedrock AgentCore alongside W&amp;B Weave to help build, evaluate, and monitor enterprise AI solutions. We cover the complete development lifecycle from tracking individual FM calls to monitoring complex agent workflows in production. ]]></description>
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<pubDate>Tue, 23 Dec 2025 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Accelerate, Enterprise, Development, using, Weights, Biases, and, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Introducing Visa Intelligent Commerce on AWS: Enabling agentic commerce with Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/3270</link>
<guid>https://news.jatlink.uk/3270</guid>
<description><![CDATA[ In this post, we explore how AWS and Visa are partnering to enable agentic commerce through Visa Intelligent Commerce using Amazon Bedrock AgentCore. We demonstrate how autonomous AI agents can transform fragmented shopping and travel experiences into seamless, end-to-end workflows—from discovery and comparison to secure payment authorization—all driven by natural language. ]]></description>
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<pubDate>Tue, 23 Dec 2025 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, Visa, Intelligent, Commerce, AWS:, Enabling, agentic, commerce, with, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Deploy Mistral AI’s Voxtral on Amazon SageMaker AI</title>
<link>https://news.jatlink.uk/3267</link>
<guid>https://news.jatlink.uk/3267</guid>
<description><![CDATA[ In this post, we demonstrate hosting Voxtral models on Amazon SageMaker AI endpoints using vLLM and the Bring Your Own Container (BYOC) approach. vLLM is a high-performance library for serving large language models (LLMs) that features paged attention for improved memory management and tensor parallelism for distributing models across multiple GPUs. ]]></description>
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<pubDate>Mon, 22 Dec 2025 19:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Deploy, Mistral, AI’s, Voxtral, Amazon, SageMaker</media:keywords>
</item>

<item>
<title>Enhance document analytics with Strands AI Agents for the GenAI IDP Accelerator</title>
<link>https://news.jatlink.uk/3268</link>
<guid>https://news.jatlink.uk/3268</guid>
<description><![CDATA[ To address the need for businesses to quickly analyze information and unlock actionable insights, we are announcing Analytics Agent, a new feature that is seamlessly integrated into the GenAI IDP Accelerator. With this feature, users can perform advanced searches and complex analyses using natural language queries without SQL or data analysis expertise. In this post, we discuss how non-technical users can use this tool to analyze and understand the documents they have processed at scale with natural language. ]]></description>
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<pubDate>Mon, 22 Dec 2025 19:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Enhance, document, analytics, with, Strands, Agents, for, the, GenAI, IDP, Accelerator</media:keywords>
</item>

<item>
<title>Build a multimodal generative AI assistant for root cause diagnosis in predictive maintenance using Amazon Bedrock</title>
<link>https://news.jatlink.uk/3269</link>
<guid>https://news.jatlink.uk/3269</guid>
<description><![CDATA[ In this post, we demonstrate how to implement a predictive maintenance solution using Foundation Models (FMs) on Amazon Bedrock, with a case study of Amazon&#039;s manufacturing equipment within their fulfillment centers. The solution is highly adaptable and can be customized for other industries, including oil and gas, logistics, manufacturing, and healthcare. ]]></description>
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<pubDate>Mon, 22 Dec 2025 19:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, multimodal, generative, assistant, for, root, cause, diagnosis, predictive, maintenance, using, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Move Beyond Chain&amp;of&amp;Thought with Chain&amp;of&amp;Draft on Amazon Bedrock</title>
<link>https://news.jatlink.uk/3266</link>
<guid>https://news.jatlink.uk/3266</guid>
<description><![CDATA[ This post explores Chain-of-Draft (CoD), an innovative prompting technique introduced in a Zoom AI Research paper Chain of Draft: Thinking Faster by Writing Less, that revolutionizes how models approach reasoning tasks. While Chain-of-Thought (CoT) prompting has been the go-to method for enhancing model reasoning, CoD offers a more efficient alternative that mirrors human problem-solving patterns—using concise, high-signal thinking steps rather than verbose explanations. ]]></description>
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<pubDate>Mon, 22 Dec 2025 19:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Move, Beyond, Chain-of-Thought, with, Chain-of-Draft, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Introducing SOCI indexing for Amazon SageMaker Studio: Faster container startup times for AI/ML workloads</title>
<link>https://news.jatlink.uk/3263</link>
<guid>https://news.jatlink.uk/3263</guid>
<description><![CDATA[ Today, we are excited to introduce a new feature for SageMaker Studio: SOCI (Seekable Open Container Initiative) indexing. SOCI supports lazy loading of container images, where only the necessary parts of an image are downloaded initially rather than the entire container. ]]></description>
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<pubDate>Fri, 19 Dec 2025 19:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Introducing, SOCI, indexing, for, Amazon, SageMaker, Studio:, Faster, container, startup, times, for, AIML, workloads</media:keywords>
</item>

<item>
<title>Build and deploy scalable AI agents with NVIDIA NeMo, Amazon Bedrock AgentCore, and Strands Agents</title>
<link>https://news.jatlink.uk/3255</link>
<guid>https://news.jatlink.uk/3255</guid>
<description><![CDATA[ This post demonstrates how to use the powerful combination of Strands Agents, Amazon Bedrock AgentCore, and NVIDIA NeMo Agent Toolkit to build, evaluate, optimize, and deploy AI agents on Amazon Web Services (AWS) from initial development through production deployment. ]]></description>
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<pubDate>Thu, 18 Dec 2025 18:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Build, and, deploy, scalable, agents, with, NVIDIA, NeMo, Amazon, Bedrock, AgentCore, and, Strands, Agents</media:keywords>
</item>

<item>
<title>Bi&amp;directional streaming for real&amp;time agent interactions now available in Amazon Bedrock AgentCore Runtime</title>
<link>https://news.jatlink.uk/3256</link>
<guid>https://news.jatlink.uk/3256</guid>
<description><![CDATA[ In this post, you will learn about bi-directional streaming on AgentCore Runtime and the prerequisites to create a WebSocket implementation. You will also learn how to use Strands Agents to implement a bi-directional streaming solution for voice agents. ]]></description>
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<pubDate>Thu, 18 Dec 2025 18:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Bi-directional, streaming, for, real-time, agent, interactions, now, available, Amazon, Bedrock, AgentCore, Runtime</media:keywords>
</item>

<item>
<title>Track machine learning experiments with MLflow on Amazon SageMaker using Snowflake integration</title>
<link>https://news.jatlink.uk/3253</link>
<guid>https://news.jatlink.uk/3253</guid>
<description><![CDATA[ In this post, we demonstrate how to integrate Amazon SageMaker managed MLflow as a central repository to log these experiments and provide a unified system for monitoring their progress. ]]></description>
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<pubDate>Wed, 17 Dec 2025 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Track, machine, learning, experiments, with, MLflow, Amazon, SageMaker, using, Snowflake, integration</media:keywords>
</item>

<item>
<title>Tracking and managing assets used in AI development with Amazon SageMaker AI </title>
<link>https://news.jatlink.uk/3252</link>
<guid>https://news.jatlink.uk/3252</guid>
<description><![CDATA[ In this post, we&#039;ll explore the new capabilities and core concepts that help organizations track and manage models development and deployment lifecycles. We will show you how the features are configured to train models with automatic end-to-end lineage, from dataset upload and versioning to model fine-tuning, evaluation, and seamless endpoint deployment. ]]></description>
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<pubDate>Wed, 17 Dec 2025 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Tracking, and, managing, assets, used, development, with Amazon, SageMaker, AI </media:keywords>
</item>

<item>
<title>Governance by design: The essential guide for successful AI scaling</title>
<link>https://news.jatlink.uk/3250</link>
<guid>https://news.jatlink.uk/3250</guid>
<description><![CDATA[ Picture this: Your enterprise has just deployed its first generative AI application. The initial results are promising, but as you plan to scale across departments, critical questions emerge. How will you enforce consistent security, prevent model bias, and maintain control as AI applications multiply? ]]></description>
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<pubDate>Tue, 16 Dec 2025 22:00:04 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Governance, design:, The, essential, guide, for, successful, scaling</media:keywords>
</item>

<item>
<title>How Tata Power CoE built a scalable AI&amp;powered solar panel inspection solution with Amazon SageMaker AI and Amazon Bedrock</title>
<link>https://news.jatlink.uk/3248</link>
<guid>https://news.jatlink.uk/3248</guid>
<description><![CDATA[ In this post, we explore how Tata Power CoE and Oneture Technologies use AWS services to automate the inspection process end-to-end. ]]></description>
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<pubDate>Tue, 16 Dec 2025 19:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Tata, Power, CoE, built, scalable, AI-powered, solar, panel, inspection, solution, with, Amazon, SageMaker, and, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Unlocking video understanding with TwelveLabs Marengo on Amazon Bedrock</title>
<link>https://news.jatlink.uk/3249</link>
<guid>https://news.jatlink.uk/3249</guid>
<description><![CDATA[ In this post, we&#039;ll show how the TwelveLabs Marengo embedding model, available on Amazon Bedrock, enhances video understanding through multimodal AI. We&#039;ll build a video semantic search and analysis solution using embeddings from the Marengo model with Amazon OpenSearch Serverless as the vector database, for semantic search capabilities that go beyond simple metadata matching to deliver intelligent content discovery. ]]></description>
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<pubDate>Tue, 16 Dec 2025 19:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Unlocking, video, understanding, with, TwelveLabs, Marengo, Amazon, Bedrock</media:keywords>
</item>

<item>
<title>Checkpointless training on Amazon SageMaker HyperPod: Production&amp;scale training with faster fault recovery</title>
<link>https://news.jatlink.uk/3244</link>
<guid>https://news.jatlink.uk/3244</guid>
<description><![CDATA[ In this post, we introduce checkpointless training on Amazon SageMaker HyperPod, a paradigm shift in model training that reduces the need for traditional checkpointing by enabling peer-to-peer state recovery. Results from production-scale validation show 80–93% reduction in recovery time (from 15–30 minutes or more to under 2 minutes) and enables up to 95% training goodput on cluster sizes with thousands of AI accelerators. ]]></description>
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<pubDate>Mon, 15 Dec 2025 20:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Checkpointless, training, Amazon, SageMaker, HyperPod:, Production-scale, training, with, faster, fault, recovery</media:keywords>
</item>

<item>
<title>Adaptive infrastructure for foundation model training with elastic training on SageMaker HyperPod</title>
<link>https://news.jatlink.uk/3243</link>
<guid>https://news.jatlink.uk/3243</guid>
<description><![CDATA[ Amazon SageMaker HyperPod now supports elastic training, enabling your machine learning (ML) workloads to automatically scale based on resource availability. In this post, we demonstrate how elastic training helps you maximize GPU utilization, reduce costs, and accelerate model development through dynamic resource adaptation, while maintain training quality and minimizing manual intervention. ]]></description>
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<pubDate>Mon, 15 Dec 2025 19:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Adaptive, infrastructure, for, foundation, model, training, with, elastic, training, SageMaker, HyperPod</media:keywords>
</item>

<item>
<title>Applying data loading best practices for ML training with Amazon S3 clients</title>
<link>https://news.jatlink.uk/3242</link>
<guid>https://news.jatlink.uk/3242</guid>
<description><![CDATA[ In this post, we present practical techniques and recommendations for optimizing throughput in ML training workloads that read data directly from Amazon S3 general purpose buckets. ]]></description>
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<pubDate>Mon, 15 Dec 2025 18:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Applying, data, loading, best, practices, for, training, with, Amazon, clients</media:keywords>
</item>

<item>
<title>Customize agent workflows with advanced orchestration techniques using Strands Agents</title>
<link>https://news.jatlink.uk/3240</link>
<guid>https://news.jatlink.uk/3240</guid>
<description><![CDATA[ In this post, we explore two powerful orchestration patterns implemented with Strands Agents. Using a common set of travel planning tools, we demonstrate how different orchestration strategies can solve the same problem through distinct reasoning approaches, ]]></description>
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<pubDate>Mon, 15 Dec 2025 18:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Customize, agent, workflows, with, advanced, orchestration, techniques, using, Strands, Agents</media:keywords>
</item>

<item>
<title>Operationalize generative AI workloads and scale to hundreds of use cases with Amazon Bedrock – Part 1: GenAIOps</title>
<link>https://news.jatlink.uk/3241</link>
<guid>https://news.jatlink.uk/3241</guid>
<description><![CDATA[ In this first part of our two-part series, you&#039;ll learn how to evolve your existing DevOps architecture for generative AI workloads and implement GenAIOps practices. We&#039;ll showcase practical implementation strategies for different generative AI adoption levels, focusing on consuming foundation models. ]]></description>
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<pubDate>Mon, 15 Dec 2025 18:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Operationalize, generative, workloads, and, scale, hundreds, use, cases, with, Amazon, Bedrock, –, Part, GenAIOps</media:keywords>
</item>

<item>
<title>Building a voice&amp;driven AWS assistant with Amazon Nova Sonic</title>
<link>https://news.jatlink.uk/3239</link>
<guid>https://news.jatlink.uk/3239</guid>
<description><![CDATA[ In this post, we explore how to build a sophisticated voice-powered AWS operations assistant using Amazon Nova Sonic for speech processing and Strands Agents for multi-agent orchestration. This solution demonstrates how natural language voice interactions can transform cloud operations, making AWS services more accessible and operations more efficient. ]]></description>
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<pubDate>Fri, 12 Dec 2025 19:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Building, voice-driven, AWS, assistant, with, Amazon, Nova, Sonic</media:keywords>
</item>

<item>
<title>Amazon Bedrock AgentCore Observability with Langfuse</title>
<link>https://news.jatlink.uk/3234</link>
<guid>https://news.jatlink.uk/3234</guid>
<description><![CDATA[ In this post, we explain how to integrate Langfuse observability with Amazon Bedrock AgentCore to gain deep visibility into an AI agent&#039;s performance, debug issues faster, and optimize costs. We walk through a complete implementation using Strands agents deployed on AgentCore Runtime followed by step-by-step code examples. ]]></description>
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<pubDate>Thu, 11 Dec 2025 19:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Amazon, Bedrock, AgentCore, Observability, with, Langfuse</media:keywords>
</item>

<item>
<title>How Harmonic Security improved their data&amp;leakage detection system with low&amp;latency fine&amp;tuned models using Amazon SageMaker, Amazon Bedrock, and Amazon Nova Pro</title>
<link>https://news.jatlink.uk/3231</link>
<guid>https://news.jatlink.uk/3231</guid>
<description><![CDATA[ This post walks through how Harmonic Security used Amazon SageMaker AI, Amazon Bedrock, and Amazon Nova Pro to fine-tune a ModernBERT model, achieving low-latency, accurate, and scalable data leakage detection. ]]></description>
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<pubDate>Thu, 11 Dec 2025 19:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Harmonic, Security, improved, their, data-leakage, detection, system, with, low-latency, fine-tuned, models, using, Amazon, SageMaker, Amazon, Bedrock, and, Amazon, Nova, Pro</media:keywords>
</item>

<item>
<title>How Swisscom builds enterprise agentic AI for customer support and sales using Amazon Bedrock AgentCore</title>
<link>https://news.jatlink.uk/3232</link>
<guid>https://news.jatlink.uk/3232</guid>
<description><![CDATA[ In this post, we&#039;ll show how Swisscom implemented Amazon Bedrock AgentCore to build and scale their enterprise AI agents for customer support and sales operations. As an early adopter of Amazon Bedrock in the AWS Europe Region (Zurich), Swisscom leads in enterprise AI implementation with their Chatbot Builder system and various AI initiatives. Their successful deployments include Conversational AI powered by Rasa and fine-tuned LLMs on Amazon SageMaker, and the Swisscom Swisscom myAI assistant, built to meet Swiss data protection standards. ]]></description>
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<pubDate>Thu, 11 Dec 2025 19:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, Swisscom, builds, enterprise, agentic, for, customer, support, and, sales, using, Amazon, Bedrock, AgentCore</media:keywords>
</item>

<item>
<title>Scaling MLflow for enterprise AI: What’s New in SageMaker AI with MLflow</title>
<link>https://news.jatlink.uk/3233</link>
<guid>https://news.jatlink.uk/3233</guid>
<description><![CDATA[ Today we’re announcing Amazon SageMaker AI with MLflow, now including a serverless capability that dynamically manages infrastructure provisioning, scaling, and operations for artificial intelligence and machine learning (AI/ML) development tasks. In this post, we explore how these new capabilities help you run large MLflow workloads—from generative AI agents to large language model (LLM) experimentation—with improved performance, automation, and security using SageMaker AI with MLflow. ]]></description>
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<pubDate>Thu, 11 Dec 2025 19:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Scaling, MLflow, for, enterprise, AI:, What’s, New, SageMaker, with, MLflow</media:keywords>
</item>

<item>
<title>Implement automated smoke testing using Amazon Nova Act headless mode</title>
<link>https://news.jatlink.uk/3224</link>
<guid>https://news.jatlink.uk/3224</guid>
<description><![CDATA[ This post shows how to implement automated smoke testing using Amazon Nova Act headless mode in CI/CD pipelines. We use SauceDemo, a sample ecommerce application, as our target for demonstration. We demonstrate setting up Amazon Nova Act for headless browser automation in CI/CD environments and creating smoke tests that validate key user workflows. We then show how to implement parallel execution to maximize testing efficiency, configure GitLab CI/CD for automatic test execution on every deployment, and apply best practices for maintainable and scalable test automation. ]]></description>
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<pubDate>Wed, 10 Dec 2025 20:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Implement, automated, smoke, testing, using, Amazon, Nova, Act, headless, mode</media:keywords>
</item>

<item>
<title>Real&amp;world reasoning: How Amazon Nova Lite 2.0 handles complex customer support scenarios</title>
<link>https://news.jatlink.uk/3221</link>
<guid>https://news.jatlink.uk/3221</guid>
<description><![CDATA[ This post evaluates the reasoning capabilities of our latest offering in the Nova family, Amazon Nova Lite 2.0, using practical scenarios that test these critical dimensions. We compare its performance against other models in the Nova family—Lite 1.0, Micro, Pro 1.0, and Premier—to elucidate how the latest version advances reasoning quality and consistency. ]]></description>
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<pubDate>Tue, 09 Dec 2025 21:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Real-world, reasoning:, How, Amazon, Nova, Lite, 2.0, handles, complex, customer, support, scenarios</media:keywords>
</item>

<item>
<title>Create AI&amp;powered chat assistants for your enterprise with Amazon Quick Suite</title>
<link>https://news.jatlink.uk/3219</link>
<guid>https://news.jatlink.uk/3219</guid>
<description><![CDATA[ In this post, we show how to build chat agents in Amazon Quick Suite. We walk through a three-layer framework—identity, instructions, and knowledge—that transforms Quick Suite chat agents into intelligent enterprise AI assistants. In our example, we demonstrate how our chat agent guides feature discovery, use enterprise data to inform recommendations, and tailors solutions based on potential to impact and your team’s adoption readiness. ]]></description>
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<pubDate>Tue, 09 Dec 2025 18:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Create, AI-powered, chat, assistants, for, your, enterprise, with, Amazon, Quick, Suite</media:keywords>
</item>

<item>
<title>How AWS delivers generative AI to the public sector in weeks, not years</title>
<link>https://news.jatlink.uk/3214</link>
<guid>https://news.jatlink.uk/3214</guid>
<description><![CDATA[ Experts at the Generative AI Innovation Center share several strategies to help organizations excel with generative AI. ]]></description>
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<pubDate>Mon, 08 Dec 2025 18:00:07 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>How, AWS, delivers, generative, the, public, sector, weeks, not, years</media:keywords>
</item>

<item>
<title>Streamline AI agent tool interactions: Connect API Gateway to AgentCore Gateway with MCP</title>
<link>https://news.jatlink.uk/3212</link>
<guid>https://news.jatlink.uk/3212</guid>
<description><![CDATA[ AgentCore Gateway now supports API GatewayAs organizations explore the possibilities of agentic applications, they continue to navigate challenges of using enterprise data as context in invocation requests to large language models (LLMs) in a manner that is secure and aligned with enterprise policies. This post covers these new capabilities and shows how to implement them. ]]></description>
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<pubDate>Mon, 08 Dec 2025 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Streamline, agent, tool, interactions:, Connect, API, Gateway, AgentCore, Gateway, with, MCP</media:keywords>
</item>

<item>
<title>Create an intelligent insurance underwriter agent powered by Amazon Nova 2 Lite and Amazon Quick Suite</title>
<link>https://news.jatlink.uk/3213</link>
<guid>https://news.jatlink.uk/3213</guid>
<description><![CDATA[ In this post, we demonstrate how to build an intelligent insurance underwriting agent that addresses three critical challenges: unifying siloed data across CRM systems and databases, providing explainable and auditable AI decisions for regulatory compliance, and enabling automated fraud detection with consistent underwriting rules. The solution combines Amazon Nova 2 Lite for transparent risk assessment, Amazon Bedrock AgentCore for managed MCP server infrastructure, and Amazon Quick Suite for natural language interactions—delivering a production-ready system that underwriters can deploy in under 30 minutes . ]]></description>
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<pubDate>Mon, 08 Dec 2025 17:00:06 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>Create, intelligent, insurance, underwriter, agent, powered, Amazon, Nova, Lite, and, Amazon, Quick, Suite</media:keywords>
</item>

<item>
<title>S&amp;amp;P Global Data integration expands Amazon Quick Research capabilities</title>
<link>https://news.jatlink.uk/3211</link>
<guid>https://news.jatlink.uk/3211</guid>
<description><![CDATA[ Today, we are pleased to announce a new integration between Amazon Quick Research and S&amp;P Global. This integration brings both S&amp;P Global Energy news, research, and insights and S&amp;P Global Market Intelligence data to Quick Research customers in one deep research agent. In this post, we explore S&amp;P Global’s data sets and the solution architecture of the integration with Quick Research. ]]></description>
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<pubDate>Mon, 08 Dec 2025 17:00:05 +0000</pubDate>
<dc:creator>Jat AI</dc:creator>
<media:keywords>S&amp;P, Global, Data, integration, expands, Amazon, Quick, Research, capabilities</media:keywords>
</item>

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