# Amazon Bedrock Published articles for Amazon Bedrock. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Improving HCLS AI reasoning with open-source agent skills DevFeed: [Improving HCLS AI reasoning with open-source agent skills]() Original publisher: [Read original article]() Author: Michael Hsieh Published: 2026-09-16T19:00:00Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Agent Skills](), [Artificial Intelligence](), [foundation-models](), [Open Source](), [Bioinformatics](), [Medical imaging]() Tags: [agent-skills](), [agents](), [ai](), [ai-agents](), [amazon-bedrock](), [amazon-quick-suite](), [announcements](), [artificial-intelligence](), [foundation-models](), [generative-ai](), [healthcare](), [healthcare-and-life-sciences](), [intermediate-200](), [kiro](), [life-sciences](), [medical-imaging](), [open-source](), [strands-agents]() ### AI overview This post presents 38 open-source agent skills spanning 11 healthcare and life sciences domains. The skills encode domain decision procedures for AI agents, and the reported evaluation found a 70-86% head-to-head win rate over agents without the skills. ### Source excerpt AI agents on foundation models often misapply healthcare and life sciences decision frameworks, citing the right guideline but applying it incorrectly. This post shares 38 open-source agent skills across 11 HCLS domains that close this gap, with installation steps, three worked use cases, and a 410-prompt evaluation showing a 70-86% win rate. ## Optimizing agent system prompts with Amazon Bedrock AgentCore DevFeed: [Optimizing agent system prompts with Amazon Bedrock AgentCore]() Original publisher: [Read original article]() Author: Han Ding Published: 2026-09-16T15:47:39Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock AgentCore](), [Prompt optimization](), [Prompt Engineering](), [Traces](), [Benchmark](), [A/B Testing](), [Automated prompt engineering]() Tags: [a-b-testing](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [benchmark](), [best-practices](), [evaluation](), [intermediate-200](), [open-source](), [prompt-optimization](), [system-prompts](), [traces]() ### AI overview This technical companion explains how Amazon Bedrock AgentCore's system prompt optimizer uses production agent traces and reward signals to propose revised system prompts. It describes the reflector engine, recommendation explanations, offline batch evaluation, online A/B testing, and promotion workflow, and reports benchmark results for Single Agent Reflector and the experimental open source Sub-Agent Reflector. ### Source excerpt AgentCore optimization turns production traces into proposed configuration changes, then validates them before promotion. This technical companion to the launch post explains how the system prompt optimizer's reflector engine works and shares benchmark results for the Single Agent and Sub-Agent Reflectors. ## Build a serverless PII redaction pipeline with Amazon Bedrock Data Automation DevFeed: [Build a serverless PII redaction pipeline with Amazon Bedrock Data Automation]() Original publisher: [Read original article]() Author: Samantha Stuart Published: 2026-09-16T15:17:37Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock](), [pii](), [Serverless](), [AWS Lambda](), [AWS Step Functions](), [Artificial Intelligence](), [Amazon Web Services](), [Ansible]() Tags: [advanced-300](), [amazon-bedrock](), [amazon-bedrock-data-automation](), [aws](), [aws-lambda](), [aws-step-functions](), [how-to](), [pii-redaction](), [pipeline](), [precision](), [serverless](), [technical-how-to]() ### AI overview This tutorial presents a serverless AWS pipeline for detecting and redacting personally identifiable information in scanned documents and images. It uses Amazon Bedrock Data Automation with a custom blueprint, AWS Step Functions, and AWS Lambda, with a token-matching quality check to improve recall on degraded and handwritten documents. ### Source excerpt Learn how to automate end-to-end PII detection and redaction from scanned documents at scale using Amazon Bedrock Data Automation with a custom blueprint, AWS Step Functions, and AWS Lambda. A custom blueprint redacts sensitive fields with field-level precision, and a token matching quality check raises recall across degraded and handwritten documents. ## Optimizing cost and latency with Amazon Bedrock prompt caching DevFeed: [Optimizing cost and latency with Amazon Bedrock prompt caching]() Original publisher: [Read original article]() Author: Daniel Abib Published: 2026-09-15T16:18:19Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock](), [Caching](), [Latency](), [API](), [foundation-models](), [LangChain](), [Multi-tenancy]() Tags: [advanced-300](), [amazon-bedrock](), [api](), [caching](), [foundation-models](), [integration](), [langchain](), [latency](), [pricing](), [technical-how-to](), [tokens]() ### AI overview This AWS post explains how Amazon Bedrock prompt caching can reduce repeated input-token costs by up to 90 percent and lower time to first token when requests reuse the same context. It presents six scenarios using the Converse API, including document, system prompt, tool definition, mixed TTL, tenant-isolated, and LangChain caching. ### Source excerpt Prompt caching in Amazon Bedrock can cut input token costs by up to 90% when you repeatedly send the same context to foundation models. This post walks through six practical prompt caching scenarios using the Converse API: message content, system prompt, tool definition, mixed TTL, tenant isolation, and LangChain integration. ## Manage end-user OAuth consent for AI agents with Amazon Bedrock AgentCore DevFeed: [Manage end-user OAuth consent for AI agents with Amazon Bedrock AgentCore]() Original publisher: [Read original article]() Author: Swara Gandhi Published: 2026-09-14T20:35:45Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock AgentCore](), [OAuth](), [Amazon Web Services](), [AWS CloudTrail](), [GitHub](), [Slack](), [Model Context Protocol](), [ide]() Tags: [amazon-bedrock](), [amazon-bedrock-agentcore](), [announcements](), [aws](), [aws-cloudtrail](), [claude-code](), [cursor](), [github](), [intermediate-200](), [kiro](), [model-context-protocol](), [oauth](), [slack](), [visual-studio-code]() ### AI overview This tutorial explains how Amazon Bedrock AgentCore Identity's Consent portal manages end-user OAuth consent and session binding for AI agents. It covers configuring GitHub and Slack targets through an AgentCore Gateway, storing user tokens, supporting IDE and MCP clients, and reviewing activity in AWS CloudTrail. ### Source excerpt Amazon Bedrock AgentCore Identity now offers a Consent portal, a managed web experience and session binding endpoint for AgentCore Gateway. This post walks through provisioning a portal, configuring GitHub and Slack 3LO targets, and the end-user consent flow, and shows how to review activity in AWS CloudTrail. ## Unified Knowledge Graph RAG on AWS: GraphRAG and LightRAG on one stack DevFeed: [Unified Knowledge Graph RAG on AWS: GraphRAG and LightRAG on one stack]() Original publisher: [Read original article]() Author: Jonas Kim Published: 2026-09-14T16:55:58Z Content type: article Language: en Sources: [AWS Open Source Blog]() Topics: [Retrieval Augmented Generation (RAG)](), [Graphs](), [Amazon Bedrock](), [Amazon OpenSearch Service](), [Amazon Web Services]() Tags: [amazon-bedrock](), [amazon-opensearch-service](), [aws](), [knowledge-graph](), [open-source](), [rag](), [retrieval](), [search](), [vector-search]() ### AI overview This article presents a unified knowledge-graph RAG stack that brings Microsoft GraphRAG and HKUDS LightRAG together on Amazon Bedrock, Amazon Neptune, and Amazon OpenSearch Service. The shared stack supports common ingestion, indexing, caching, and multilingual handling while allowing the retrieval methodology to be selected per query. ### Source excerpt Picture a compliance analyst staring at a few thousand contracts, amendments, and internal memos, trying to answer one question that sounds straightforward: "Which of our obligations are exposed if this one milestone slips?" The answer isn't written in any single document. It's stitched across three -- a master agreement that ties a payment to a [...] ## How Ninth Wave built AI-powered open finance onboarding on Amazon Bedrock DevFeed: [How Ninth Wave built AI-powered open finance onboarding on Amazon Bedrock]() Original publisher: [Read original article]() Author: Shawn Kelly Published: 2026-09-14T15:58:57Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock AgentCore](), [Artificial Intelligence](), [API](), [Architecture & Design](), [Security](), [Finance](), [Amazon Web Services](), [data]() Tags: [advanced-300](), [ai](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [apis](), [customer-solutions](), [fintech](), [onboarding](), [pci-dss](), [security](), [soc-2]() ### AI overview The article describes how Ninth Wave built Compass, a multi-agent AI onboarding assistant powered by Amazon Bedrock AgentCore. Compass helps financial institutions validate bank APIs, map fields to Financial Data Exchange standards, and score production readiness while supporting secure, compliant open finance connectivity. ### Source excerpt Learn how Ninth Wave built Compass, a multi-agent AI onboarding assistant on Amazon Bedrock AgentCore that validates bank APIs against Financial Data Exchange (FDX) standards, scores compliance, and compresses open finance onboarding from weeks to minutes while meeting SOC 2 and PCI DSS requirements. ## AWS Weekly Roundup: OpenAI GPT-6 Astra on Amazon Bedrock, Amazon Quick desktop GA, Kiro for students, and more (September 14, 2026) DevFeed: [AWS Weekly Roundup: OpenAI GPT-6 Astra on Amazon Bedrock, Amazon Quick desktop GA, Kiro for students, and more (September 14, 2026)]() Original publisher: [Read original article]() Author: Micah Walter Published: 2026-09-14T15:56:33Z Content type: news Language: en Sources: [AWS News Blog]() Topics: [Amazon Bedrock](), [Frontier Model](), [OpenAI](), [context window](), [browser](), [ChatGPT](), [codex](), [macOS](), [Windows](), [Mobile]() Tags: [agents](), [ai](), [ai-agents](), [amazon](), [amazon-bedrock](), [amazon-elastic-block-store-amazon-ebs](), [amazon-opensearch-service](), [amazon-quick-suite](), [aws](), [aws-lambda](), [aws-outposts](), [aws-transform](), [bedrock](), [browser](), [chatgpt](), [codex](), [context-window](), [frontier-model](), [google-play](), [gpt](), [kiro](), [launch](), [macos](), [mobile](), [openai](), [week-in-review](), [windows]() ### AI overview This AWS Weekly Roundup highlights the general availability of OpenAI GPT-6 Astra on Amazon Bedrock, describing its reasoning, writing, design, computer-use, browser-use, and million-token context-window capabilities. It also covers the Amazon Quick desktop app for macOS and Windows, including synchronized conversations and agents across desktop and mobile, plus other AWS launches and updates. ### Source excerpt There's a particular energy to mid-September in New York. Pumpkin spice lattes are flowing, temperatures are dropping, and it's nearly sweater weather. The city is back at full speed, and so is the AWS launch calendar. This week that energy showed up in a new frontier model on Amazon Bedrock, a desktop app for Amazon [...] ## The generative AI customization spectrum: From prompt engineering to custom models on AWS DevFeed: [The generative AI customization spectrum: From prompt engineering to custom models on AWS]() Original publisher: [Read original article]() Author: Bhavya Sruthi Sode Published: 2026-09-14T15:47:12Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Generative AI](), [Amazon Web Services](), [Prompt Engineering](), [Fine-tuning](), [Retrieval Augmented Generation (RAG)](), [Amazon Bedrock](), [foundation-models](), [Anthropic Claude](), [Nova](), [llama]() Tags: [advanced-300](), [ai](), [amazon-bedrock](), [anthropic-claude](), [aws](), [best-practices](), [chain-of-thought](), [fine-tuning](), [foundation-models](), [generative-ai](), [llama](), [nova](), [optimization](), [prompt-engineering](), [rag]() ### AI overview This AWS article presents an eight-step decision framework for customizing generative AI workloads. It compares progressively more involved approaches, including prompt engineering, Retrieval Augmented Generation (RAG), fine-tuning, continued pre-training, and custom models such as Amazon Nova Forge, emphasizing that teams should start with the simplest approach and escalate when greater control or domain specificity is required. ### Source excerpt Pick the right generative AI customization approach on AWS with an 8-step decision framework, from prompt engineering and RAG to fine-tuning, continued pre-training, and Amazon Nova Forge. Start simple and escalate only when you must. ## Monitoring production agent lifecycle with AWS DevOps Agent and AgentCore Evaluations DevFeed: [Monitoring production agent lifecycle with AWS DevOps Agent and AgentCore Evaluations]() Original publisher: [Read original article]() Author: Meghana Ashok Published: 2026-09-11T18:26:38Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [ai observability](), [AWS IAM](), [incident](), [Orchestration](), [telemetry]() Tags: [advanced-300](), [agent](), [agents](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [aws](), [aws-identity-and-access-management-iam](), [best-practices](), [devops](), [incident](), [infrastructure-monitoring](), [logs](), [metrics](), [monitoring](), [orchestration](), [production](), [tracing]() ### AI overview The article describes monitoring production multi-agent systems with Amazon Bedrock AgentCore Evaluations for continuous quality assessment and AWS DevOps Agent for autonomous infrastructure incident investigation. ### Source excerpt Multi-agent systems fail in ways traditional monitoring misses. This post presents a dual-layer approach to monitoring production agents: Amazon Bedrock AgentCore Evaluations for continuous quality scoring and AWS DevOps Agent for autonomous infrastructure investigation, shown on a four-agent airline reservation system. ## Beyond the price per token: Choosing the right OpenAI model on Amazon Bedrock for your workload DevFeed: [Beyond the price per token: Choosing the right OpenAI model on Amazon Bedrock for your workload]() Original publisher: [Read original article]() Author: Nick McCarthy Published: 2026-09-11T18:24:38Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [AI Chat](), [AI Bots](), [ai observability]() Tags: [advanced-300](), [agent](), [agentic](), [amazon-bedrock](), [api](), [benchmarking](), [cost](), [evaluation](), [generative-ai](), [open-source](), [openai](), [technical-how-to]() ### AI overview The article presents an open-source benchmark for comparing OpenAI models on Amazon Bedrock with OpenAI API baselines by cost per correct answer, multi-turn agent trajectory cost, and deliverable quality. ### Source excerpt Comparing models on dollars per million tokens misses what production workloads actually pay for: outcomes. This post shares an open-source benchmarking harness that measures cost per correct answer, agent trajectory cost, and rubric-graded deliverable quality across OpenAI models on Amazon Bedrock. ## From zero-shot forecast to purchase order with Amazon Bedrock AgentCore DevFeed: [From zero-shot forecast to purchase order with Amazon Bedrock AgentCore]() Original publisher: [Read original article]() Author: Hyunsoo Kim, Ph.D. Published: 2026-09-11T14:08:01Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [AI Bots](), [Training AI Models](), [SDKs]() Tags: [advanced-300](), [agents](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [automation](), [best-practices](), [orchestration](), [strands-agents](), [time-series](), [training](), [zero-shot]() ### AI overview An architecture article on using Amazon Chronos2 zero-shot forecasting and Bedrock AgentCore multi-agent orchestration to turn demand forecasts into validated purchase orders without per-product model training. ### Source excerpt Combine zero-shot forecasting with Amazon Chronos2 and multi-agent orchestration on Amazon Bedrock AgentCore to turn demand forecasts into validated purchase orders. No per-product model training, with business rules, auditability, and cost that scales to zero. ## Video and image search in Amazon Bedrock Knowledge Base using Marengo 3.0 DevFeed: [Video and image search in Amazon Bedrock Knowledge Base using Marengo 3.0]() Original publisher: [Read original article]() Author: Eric Kim Published: 2026-09-10T21:15:39Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [AI search](), [Retrieval Augmented Generation (RAG)](), [Amazon S3](), [AWS IAM]() Tags: [amazon-bedrock](), [amazon-bedrock-knowledge-bases](), [announcements](), [audio](), [embedding](), [how-to](), [images](), [intermediate-200](), [multimodal](), [rag](), [s3](), [search](), [video]() ### AI overview A walkthrough for building an Amazon Bedrock Knowledge Base with TwelveLabs Marengo Embed 3.0 to perform natural-language semantic search across video, images, and audio. ### Source excerpt TwelveLabs Marengo Embed 3.0 is now generally available as an embedding model in Amazon Bedrock Knowledge Bases, bringing fully managed natural language search to video, image, and audio content. This walkthrough shows how to build a knowledge base powered by Marengo 3.0 and run semantic queries against your media. ## Model-agnostic PII detection with LLMs DevFeed: [Model-agnostic PII detection with LLMs]() Original publisher: [Read original article]() Author: Christophe Dupuy Published: 2026-09-10T16:02:16Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [AI Chat]() Tags: [advanced-300](), [amazon-bedrock](), [benchmark](), [fine-tuning](), [inference](), [large-language-models-llms](), [llms](), [pii](), [security](), [technical-how-to](), [vpc]() ### AI overview A tutorial on building a configurable, model-agnostic PII detector with large language models. It explains prompt-based entity configuration, implementation, benchmarking, and running the detector on custom data. ### Source excerpt A configurable, model-agnostic detector that turns any large language model on Amazon Bedrock into a PII detector. Because the entities to detect live in a prompt rather than in code, one detector adapts to new entity types without retraining, and it outperforms an off-the-shelf tool across five public corpora and nine LLM-based detectors. ## How AvioBook builds turnaround insights from operational data with Amazon Bedrock AgentCore DevFeed: [How AvioBook builds turnaround insights from operational data with Amazon Bedrock AgentCore]() Original publisher: [Read original article]() Author: Petra Lafond Published: 2026-09-10T15:53:05Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [data](), [Software]() Tags: [agentic](), [agents](), [airline](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [analytics](), [apis](), [customer-solutions]() ### AI overview AvioBook prototyped Connected Analytics on Amazon Bedrock AgentCore to turn flight and ground-operations data into evidence-based answers about airline turnaround delays. ### Source excerpt AvioBook, a Thales Group Company, prototyped Connected Analytics on Amazon Bedrock AgentCore to turn AvioBook Connect's operational data into plain-language, evidence-based answers for airline managers and dispatchers, helping them find and act on the causes of flight turnaround delays. ## ICYMI: What landed for AI builders in August 2026 DevFeed: [ICYMI: What landed for AI builders in August 2026]() Original publisher: [Read original article]() Author: Tanvi Girinath Published: 2026-09-09T20:01:03Z Content type: news Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock](), [Inference](), [Caching](), [SDKs](), [Deployment]() Tags: [agents](), [ai](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [announcements](), [caching](), [deployment](), [govcloud](), [inference](), [intermediate-200](), [latency](), [open](), [robots](), [updates]() ### AI overview A recap of August 2026 Amazon Bedrock, AgentCore, and Strands updates for AI builders, including expanded context windows, inference, long-running agents, GovCloud availability, and robot deployment. ### Source excerpt A recap of August 2026 launches for AI builders across Amazon Bedrock, Amazon Bedrock AgentCore, and Strands: million-token context for OpenAI models, cross-Region inference, agents that run for up to 14 days on dedicated compute, expanded AWS GovCloud availability, and Strands Robots for physical deployment. ## Take on your most ambitious work with GPT-6 Astra on Amazon Bedrock DevFeed: [Take on your most ambitious work with GPT-6 Astra on Amazon Bedrock]() Original publisher: [Read original article]() Author: Tanvi Girinath Published: 2026-09-08T22:06:58Z Content type: release Language: en Sources: [Artificial Intelligence]() Topics: [AI, ML & Data Engineering](), [AI-assisted coding](), [AI Chat]() Tags: [agents](), [amazon-bedrock](), [announcements](), [api](), [aws](), [cache](), [chatgpt](), [codex](), [gpt](), [intermediate-200](), [openai](), [scalability](), [security]() ### AI overview GPT-6 Astra is generally available on Amazon Bedrock. The article describes its reasoning, codebase work, browser use, prompt caching, and enterprise controls for production workloads. ### Source excerpt GPT-6 Astra from OpenAI is now generally available on Amazon Bedrock. It brings deeper reasoning and sharper judgment to your most demanding tasks, running on the Amazon Bedrock inference engine built for high performance, security, and scale. ## Build full-stack AWS applications in minutes with AI-powered scaffolding DevFeed: [Build full-stack AWS applications in minutes with AI-powered scaffolding]() Original publisher: [Read original article]() Author: Jack Stevenson Published: 2026-09-08T21:24:59Z Content type: article Language: en Sources: [AWS Open Source Blog]() Topics: [App](), [cloud-infrastructure](), [Amazon Bedrock](), [Command-line interface](), [Kiro](), [Terraform](), [Bun]() Tags: [agentic](), [ai](), [ai-coding](), [amazon-bedrock](), [announcements](), [applications](), [aws](), [kiro](), [observability](), [security](), [technical-how-to](), [terraform]() ### AI overview The article introduces version 1.0 of the Nx Plugin for AWS, an open-source set of deterministic Nx generators for scaffolding deployable AWS application components and their infrastructure. It describes using AI assistants or a CLI to generate applications with security, observability, and type-safety practices included, and begins a quick start for an agentic application using Amazon Bedrock. ### Source excerpt AI assistants can stand up an app or website that runs on AWS in minutes. Getting to a production-ready version you would put in front of real customers is still the hard part. Security, observability, type-safety, and resilience are non-negotiable for production. An assistant rarely gets all of that right in one pass, and hardening [...] ## Qualcomm and Amazon Sign Multi-Generation Deal for Custom AI Inference Silicon and 1.6T Optical Interconnects DevFeed: [Qualcomm and Amazon Sign Multi-Generation Deal for Custom AI Inference Silicon and 1.6T Optical Interconnects]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-08T17:17:46Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Inference](), [Inference Performance](), [AI Infrastructure](), [Amazon Web Services](), [data centers](), [Chip design](), [networking](), [Amazon Bedrock](), [amazon]() Tags: [ai](), [ai-data-centers](), [ai-inference](), [ai-infrastructure](), [amazon-bedrock](), [aws](), [chip-design](), [cost](), [data-centers](), [dsp](), [efficiency](), [enterprise](), [networking](), [partnership](), [qualcomm]() ### AI overview Qualcomm Technologies and Amazon are collaborating across multiple generations to develop custom silicon for AWS AI data centers, primarily targeting AI inference. The agreement also covers 1.6T optical connectivity for data center networks and Qualcomm's use of AWS infrastructure, including Amazon Bedrock, for electronic design automation workloads. ### Source excerpt Qualcomm Technologies and Amazon have entered into a multi-generation collaboration to deliver customized silicon at scale for AWS's AI data centers, with AI inference as the primary target. The agreement pairs Qualcomm's power-efficient processing, silicon design, and system-level integration with Amazon's AI infrastructure, and is aimed at the compute, memory bandwidth, networking, and energy constraints The post Qualcomm and Amazon Sign Multi-Generation Deal for Custom AI Inference Silicon and 1.6T Optical Interconnects appeared first on StorageReview.com. ## AWS Weekly Roundup: Claude Fable 5.1 on AWS, Amazon Linux 2027 preview, AWS Certified AI Business Strategist, and more (September 7, 2026) DevFeed: [AWS Weekly Roundup: Claude Fable 5.1 on AWS, Amazon Linux 2027 preview, AWS Certified AI Business Strategist, and more (September 7, 2026)]() Original publisher: [Read original article]() Author: Channy Yun (윤석찬) Published: 2026-09-07T14:24:08Z Content type: news Language: en Sources: [AWS News Blog]() Topics: [Claude](), [Amazon Bedrock](), [anthropic](), [Amazon EC2](), [Operating system]() Tags: [amazon-bedrock](), [amazon-ec2](), [amazon-redshift](), [anthropic](), [aws](), [aws-lambda](), [aws-training-and-certification](), [claude](), [linux](), [news](), [security-identity-compliance](), [week-in-review]() ### AI overview AWS's weekly roundup announces Claude Fable 5.1 availability through Amazon Bedrock and Claude Platform on AWS, including retention and safety-review details for Covered Models. It also highlights the Amazon Linux 2027 public preview and EC2 R9g/R9gd memory-optimized instances. ### Source excerpt Last week, Claude Fable 5.1 became available on AWS. According to Anthropic, Claude Fable 5.1 delivers frontier intelligence for ambitious tasks across coding, scientific research, and enterprise workflows. Claude Fable 5.1 is built for long-running, high-stakes work that runs for hours and spans many applications. It can own more of a software project on its [...] ## Incident response guide for AWS CloudTrail investigations - Part 2 DevFeed: [Incident response guide for AWS CloudTrail investigations - Part 2]() Original publisher: [Read original article]() Author: Oscar Diaz Published: 2026-09-03T21:15:53Z Content type: article Language: en Sources: [AWS Security Blog]() Topics: [Incident response](), [AWS CloudTrail](), [AWS IAM](), [web applications]() Tags: [advanced-300](), [amazon-bedrock](), [aws](), [aws-cloudtrail](), [aws-identity-and-access-management-iam](), [best-practices](), [incident-response](), [security-blog](), [security-identity-compliance]() ### AI overview An incident-response guide for investigating AWS CloudTrail events through a multi-stage attack scenario. It traces an SSRF vulnerability on an EC2-hosted web application to IMDSv1 credential harvesting, IAM activity, and unauthorized Amazon Bedrock access across Regions. ### Source excerpt In Part 1 of this guide, we examined two common incident scenarios: cross-account Amazon Simple Storage Service (Amazon S3) data deletion with ransomware implications, and cryptocurrency mining deployed through AWS CloudFormation using exposed AWS Management Console credentials. We also introduced key incident response terminology and investigative frameworks for analyzing AWS CloudTrail events. In this second [...] ## MCP went stateless: Is your AWS MCP server deployment well-architected? DevFeed: [MCP went stateless: Is your AWS MCP server deployment well-architected?]() Original publisher: [Read original article]() Author: Anand Komandooru Published: 2026-09-01T13:09:19Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [Model Context Protocol](), [Deployment](), [schema-evolution](), [Amazon Bedrock]() Tags: [amazon-bedrock](), [announcements](), [aws](), [best-practices](), [deployment](), [mcp](), [mcp-server](), [observability]() ### AI overview The article explains MCP's move to a stateless core and how it simplifies AWS MCP server deployments by removing the need for sticky sessions and shared session stores. It maps the MCP 2026-07-28 specification to AWS Well-Architected Agentic AI Lens practices and recommends migration. ### Source excerpt On July 28, 2026, MCP made its protocol core stateless, removing the initialize handshake and session header. This post maps the MCP 2026-07-28 specification to the AWS Well-Architected Agentic AI Lens, pillar by pillar, and shows why the stateless design lets you delete the sticky sessions and session stores your MCP servers needed on AWS. ## Extend Amazon Bedrock Guardrails to Tool Interactions Using the Strands Agents SDK DevFeed: [Extend Amazon Bedrock Guardrails to Tool Interactions Using the Strands Agents SDK]() Original publisher: [Read original article]() Author: Stephan Traub Published: 2026-08-27T16:20:05Z Content type: tutorial Language: en Sources: [AWS Security Blog]() Topics: [AI Bots](), [AWS IAM]() Tags: [ai-agents](), [amazon-bedrock](), [amazon-bedrock-guardrails](), [artificial-intelligence](), [aws-identity-and-access-management-iam](), [expert-400](), [generative-ai](), [model-context-protocol](), [security-blog](), [security-identity-compliance](), [strands-agents](), [tools](), [validation]() ### AI overview A tutorial on extending Amazon Bedrock Guardrails beyond model prompts and responses to validate AI-agent tool interactions with Strands Agents SDK lifecycle hooks. ### Source excerpt If you're running AI agents in production, Amazon Bedrock Guardrails protects the model boundary. But your agents also invoke tools, fetch external data, and communicate with other systems. That data flows outside the model boundary, where model-level guardrails can't reach. You can extend guardrail coverage to those interactions using three validation checkpoints built with the [...] ## ICYMI: July 2026 @AWS Security DevFeed: [ICYMI: July 2026 @AWS Security]() Original publisher: [Read original article]() Author: Rodolfo Brenes Published: 2026-08-26T19:32:55Z Content type: news Language: en Sources: [AWS Security Blog]() Topics: [Application Security](), [AI-assisted coding](), [AWS IAM]() Tags: [ai-agent](), [ai-coding](), [ai-security](), [amazon-bedrock](), [announcements](), [aws](), [compliance](), [ddos](), [firewall](), [foundational-100](), [iam](), [secrets](), [security](), [security-blog](), [security-identity-compliance]() ### AI overview A July 2026 AWS Security roundup covering AI-agent security, data protection, supply-chain protection, firewall automation, DDoS mitigation, and compliance readiness. ### Source excerpt If you found time for a bit of vacation this summer, you might be in catch-up mode. Here's a list to help: all the expert blog posts, new service capabilities, code samples, and workshops, in case you missed it, from July 2026. AWS Security Blog post This month's AWS Security Blog posts covered AI agent [...] [Next page]()