# Amazon Bedrock AWS platform for building generative AI applications and agents using foundation models. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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. ## Abnormal AI: Amazon Bedrock AgentCore for agentic email security at scale DevFeed: [Abnormal AI: Amazon Bedrock AgentCore for agentic email security at scale]() Original publisher: [Read original article]() Author: Aswin Vasudevan Published: 2026-09-14T21:22:45Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock AgentCore](), [threat detection](), [Amazon Bedrock](), [Security](), [Serverless](), [API](), [Amazon Web Services](), [AWS CloudTrail](), [Amazon CloudWatch](), [Amazon S3]() Tags: [advanced-300](), [agentic](), [agents](), [amazon-bedrock-agentcore](), [amazon-cloudwatch](), [amazon-s3](), [analysis](), [api](), [aws](), [aws-cloudtrail](), [code](), [customer-solutions](), [data](), [data-processing](), [security](), [serverless](), [threat-detection]() ### AI overview Abnormal AI uses Amazon Bedrock AgentCore Code Interpreter as an ephemeral, serverless compute scratch pad for real-time inline email threat detection. The article describes its sandbox isolation, networking and file-handling options, preloaded Python capabilities, observability integrations, and use at billion-message scale. ### Source excerpt Learn how Abnormal AI deployed Amazon Bedrock AgentCore Code Interpreter as an ephemeral compute scratch pad for the agents behind its real-time email threat detection at billion-message scale, plus the sandbox design decisions and practical lessons for builders deploying Code Interpreter in production. ## 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 [...] ## 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. ## 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. ## How Heurist Finance built an AI-native investment workbench on Amazon Bedrock AgentCore DevFeed: [How Heurist Finance built an AI-native investment workbench on Amazon Bedrock AgentCore]() Original publisher: [Read original article]() Author: JW Wang Published: 2026-09-09T18:11:12Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock](), [Claude](), [Orchestration](), [tracing]() Tags: [advanced-300](), [agent](), [ai](), [amazon-bedrock-agentcore](), [anthropic-claude](), [customer-solutions](), [identity](), [memory](), [observability](), [sandbox](), [tracing]() ### AI overview Heurist Finance uses Amazon Bedrock AgentCore to run a conversational investment workbench that accesses premium data per query and produces auditable, personalized research responses. ### Source excerpt Learn how Heurist built Heurist Finance, a conversational AI investment workbench, on Amazon Bedrock AgentCore. This customer story shows how AgentCore payments, Identity, Memory, Code Interpreter, and Observability let a small team buy premium market data per query, isolate analysis in a sandbox, and keep every action auditable. ## 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 [...] ## CoreBreak proves agent guardrails need to live outside the agent DevFeed: [CoreBreak proves agent guardrails need to live outside the agent]() Original publisher: [Read original article]() Author: Tyler Akidau Published: 2026-08-31T00:00:00Z Content type: opinion Language: en Sources: [Redpanda]() Topics: [Artificial Intelligence](), [AI Agent](), [Security](), [Securing AI](), [Amazon Web Services](), [vercel ai sdk](), [Amazon Bedrock](), [Google](), [Vercel]() Tags: [agent](), [agentic-ai](), [ai](), [ai-agent](), [architecture](), [aws](), [bedrock](), [cve](), [google](), [policy](), [security](), [thought-leadership](), [vercel]() ### AI overview The article argues that CoreBreak exposed a structural security flaw in major AI agent stacks and that agent guardrails should be enforced outside the agent's control. ### Source excerpt At Black Hat 2026, CoreBreak exposed the same structural flaw across AWS, Google, and Vercel. Here's why AI agent security enforcement should live beyond the agent's reach. ## Closing the AI agent trust gap with graduated autonomy DevFeed: [Closing the AI agent trust gap with graduated autonomy]() Original publisher: [Read original article]() Author: Dev Arora Published: 2026-08-26T17:33:03Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [Artificial Intelligence](), [AI Agent](), [Authorization](), [Amazon Bedrock](), [Amazon Web Services](), [Large Language Model](), [tracing](), [configuration](), [Provisioning]() Tags: [agent](), [agents](), [ai](), [ai-agent](), [ai-agents](), [amazon](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [api](), [aws](), [compliance](), [evaluation](), [infrastructure](), [large-language-model](), [logs](), [model](), [policy](), [production](), [thought-leadership](), [tracing]() ### AI overview This article presents graduated autonomy as an architectural pattern for closing the trust gap in AI agents. Agents gain permissions through sustained reliability and lose them when performance degrades, using trust scoring, autonomy tiers, pre- and post-execution controls, policy enforcement, provenance, reversibility, and delivery gates. The design uses Amazon Bedrock AgentCore, Amazon DynamoDB, and AWS CodePipeline. ### Source excerpt Most teams give AI agents either full access or read-only, leaving value unused or risk unmanaged. This post describes graduated autonomy, an architectural pattern in which agents earn expanded permissions through sustained reliability and lose them when performance degrades, built on Amazon Bedrock AgentCore, Amazon DynamoDB, and AWS CodePipeline. ## Java Weekly, Issue 660 DevFeed: [Java Weekly, Issue 660]() Original publisher: [Read original article]() Author: baeldung Published: 2026-08-22T11:26:18Z Content type: article Language: en Sources: [Baeldung]() Topics: [API](), [JSON](), [Quarkus](), [Gradle](), [Retrieval Augmented Generation (RAG)](), [Amazon Bedrock](), [agent observability]() Tags: [amazon-bedrock](), [api](), [gradle](), [java](), [json](), [no-ads](), [no-after-post](), [no-before-post](), [no-optins](), [rag](), [release](), [weekly-review](), [weekly-review-no-ads-no-after-post-no-before-post-no-optins]() ### AI overview Java Weekly, Issue 660 is a roundup of Java ecosystem news, including a Hibernate second-level cache performance case study, a proposed small JSON API for JDK 28, Gradle and Renovate integration, AI and RAG resources, and recent Quarkus and other library releases. ### Source excerpt A clear Hibernate speedup and a new JSON API coming to Java. The post Java Weekly, Issue 660 first appeared on Baeldung. ## Run agentic software delivery inside the boundaries you already trust DevFeed: [Run agentic software delivery inside the boundaries you already trust]() Original publisher: [Read original article]() Author: Ozer Dondurmacioglu Published: 2026-08-20T00:00:00Z Content type: article Language: en Sources: [GitLab]() Topics: [GitHub](), [Amazon Bedrock](), [Cloud](), [Amazon Web Services](), [Inference](), [Software as a service](), [AI Development](), [Critical Infrastructure]() Tags: [agentic](), [ai](), [amazon-bedrock](), [aws](), [cloud](), [critical-infrastructure](), [inference](), [product](), [software](), [software-delivery](), [software-development]() ### AI overview GitLab introduces AI Gateway for GitLab Dedicated, enabling customers to run AI processing within their single-tenant environment and selected AWS region. The gateway supports Amazon Bedrock and other preferred model providers, while GitLab maintains the managed infrastructure, availability, and disaster recovery. GitLab Duo Agent Platform capabilities such as code review automation, failed-pipeline fixes, and security analysis can use customer-selected models. ### Source excerpt Many enterprises choose GitLab Dedicated for a clear reason: a single-tenant instance, managed by GitLab, in a cloud region they select. That isolation already covers source code, project data, and the software development lifecycle they run on GitLab. GitLab Dedicated customers can now deploy the AI Gateway for the GitLab Duo Agent Platform inside their single-tenant SaaS infrastructure, so AI-processed data stays in their environment and chosen region. GitLab Dedicated as the critical infrastructure for software delivery GitLab Dedicated is deployed in your preferred AWS region, and hosted and maintained by GitLab. You can bring your own encryption keys. No private cloud installation required. GitLab operates the instance, including high availability and disaster recovery, and maintains 99.9% monthly availability for core services. Updates to GitLab Dedicated follow a stability-first cadence, with instances on the previous minor version (N-1) relative to the current GitLab release. That is the infrastructure organizations already trust when they scale software delivery under audit: guardrails you can take to a regulator, SaaS operations you do not staff, and an availability target that holds when the platform is in the critical path. Trust should travel with every agent Agentic workflows demand more from your software development lifecycle beyond the AI coding: increased pipeline volume, greater risk for vulnerabilities introduced, context that needs to travel with every agent and flow, among others. The isolation, residency, and service-level requirements that already apply to GitLab Dedicated should apply to that inference path too, especially with growing adoption of agentic software development. Adopt AI while staying compliant Here is what's new with the introduction of AI Gateway for GitLab Dedicated: Keep AI processing in the tenancy. The AI Gateway runs in the Dedicated single-tenant environment, aligned with data residency. Bring your own models. Connect ## How Temporal Adds Durability to AWS Agentic AI Workloads DevFeed: [How Temporal Adds Durability to AWS Agentic AI Workloads]() Original publisher: [Read original article]() Author: Rahman Syed Published: 2026-08-20T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Amazon Web Services](), [Artificial Intelligence](), [Agent Framework](), [Amazon Bedrock](), [Strands Agents](), [AWS Lambda]() Tags: [agent-framework](), [agentic](), [ai](), [amazon-bedrock](), [aws](), [aws-lambda](), [cloud](), [iam](), [temporal-voices]() ### AI overview This developer article explains how Temporal adds a durability layer to AWS-based agentic AI systems. It presents a loan-underwriting example and describes using Amazon Bedrock, Strands Agents, and AWS Lambda to build recoverable, scalable workloads. ### Source excerpt AWS gives developers the best-in-class tools for building agentic AI -- models, an agent framework, and compute to run it all on. As those systems grow more complex with more tools, more agents, and longer-running steps that wait on human-intervention, keeping the whole thing consistent and recoverable becomes its own design challenge. This is the layer Temporal adds. ## How Clario technology detects PHI/PII in DICOM images using Amazon Bedrock DevFeed: [How Clario technology detects PHI/PII in DICOM images using Amazon Bedrock]() Original publisher: [Read original article]() Author: Alex Boudreau Published: 2026-08-19T14:29:31Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [Amazon Bedrock](), [Medical imaging](), [Amazon Textract](), [Amazon Web Services](), [data]() Tags: [amazon](), [amazon-bedrock](), [amazon-textract](), [analysis](), [architecture](), [aws](), [customer-solutions](), [data](), [healthcare](), [images](), [medical-imaging](), [pii](), [technology](), [workflow]() ### AI overview Clario uses Amazon Bedrock and Amazon Textract to automate the detection of PHI and PII in DICOM image slices from clinical trials, including information stored in metadata and text embedded in image pixels. ### Source excerpt Clario, part of Thermo Fisher Scientific, uses Amazon Bedrock and Amazon Textract to automatically detect protected health information (PHI) and personally identifiable information (PII) across thousands of DICOM image slices in clinical trials, covering both metadata tags and text burned into the image pixels. ## Implement custom authentication for tools integration using request Lambda interceptor in AgentCore Gateway DevFeed: [Implement custom authentication for tools integration using request Lambda interceptor in AgentCore Gateway]() Original publisher: [Read original article]() Author: Nishant Mainro Published: 2026-08-18T20:46:26Z Content type: tutorial Language: en Sources: [AWS Security Blog]() Topics: [Amazon Bedrock AgentCore](), [Amazon Bedrock](), [AI Agent](), [Security](), [IAM](), [Model Context Protocol](), [OAuth 2.0](), [Architecture & Design](), [JSON Web Tokens]() Tags: [advanced-300](), [ai-agents](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [artificial-intelligence](), [aws-identity-and-access-management-iam](), [aws-lambda](), [model-context-protocol](), [oauth](), [prompt-injection](), [secrets](), [security](), [security-blog](), [security-identity-compliance](), [technical-how-to]() ### AI overview This tutorial explains how to use a request Lambda interceptor in Amazon Bedrock AgentCore Gateway to support legacy Basic Authentication for downstream tool APIs. The interceptor retrieves service credentials from AWS Secrets Manager and constructs the authentication header while keeping credentials isolated from the AI agent. The article also describes the inbound MCP request flow and cautions that Basic Auth should be treated as an interim measure, with modernization toward OAuth 2.0, SAML, OpenID Connect, or IAM recommended. ### Source excerpt When deploying AI agents with Amazon Bedrock AgentCore, organizations benefit from built-in modern support for OAuth 2.0, AWS Identity and Access Management (IAM), and API key authentication through Amazon Bedrock AgentCore Gateway. However, some enterprise environments still use legacy authentication mechanisms such as HTTP Basic Authentication (Basic Auth) (RFC 7617). The extensible architecture of AgentCore [...] ## AWS Weekly Roundup: EC2 application status checks, IAM role manager, OpenAI Daybreak on Bedrock, and more (August 17, 2026) DevFeed: [AWS Weekly Roundup: EC2 application status checks, IAM role manager, OpenAI Daybreak on Bedrock, and more (August 17, 2026)]() Original publisher: [Read original article]() Author: Channy Yun (윤석찬) Published: 2026-08-17T16:02:36Z Content type: news Language: en Sources: [AWS News Blog]() Topics: [Amazon Web Services](), [Amazon EC2](), [AWS IAM](), [Amazon OpenSearch Service](), [Open Source](), [Amazon Bedrock](), [Linux](), [OpenAI](), [MCP]() Tags: [amazon-bedrock](), [amazon-ec2](), [amazon-elasticache](), [amazon-opensearch-service](), [amazon-sagemaker-jumpstart](), [aws](), [aws-certificate-manager](), [aws-client-vpn](), [aws-identity-and-access-management-iam](), [bedrock](), [iam](), [linux](), [mcp](), [news](), [open-source](), [openai](), [oracle-database-aws](), [week-in-review]() ### AI overview AWS Weekly Roundup highlights EC2 application status checks, automatic IAM role setup, OpenAI Daybreak on Amazon Bedrock, and updates involving OpenSearch, Valkey, and open source community events. ### Source excerpt Last week, AWS contributors joined the OpenSearch and Valkey communities at Open Source Summit Korea 2026 and MCP DevSummit Seoul 2026 to meet open source developers and contributors. At the four-day event, community leaders and users of these Linux Foundation open source projects gathered to share knowledge, collaborate on solutions, and push the projects forward. [...] ## Track generative AI costs with Amazon Bedrock inference profiles DevFeed: [Track generative AI costs with Amazon Bedrock inference profiles]() Original publisher: [Read original article]() Author: Erik Mack Published: 2026-08-13T15:59:39Z Content type: tutorial Language: en Sources: [AWS Architecture Blog]() Topics: [Amazon Bedrock](), [Generative AI](), [Inference](), [foundation-models](), [Amazon Web Services](), [AWS IAM]() Tags: [ai](), [amazon-bedrock](), [architecture](), [aws](), [bedrock](), [cost](), [generative](), [generative-ai](), [how-to](), [iam](), [inference](), [intermediate-200](), [technical-how-to]() ### AI overview This tutorial explains how to track generative AI costs by department when multiple teams share a foundation model through Amazon Bedrock. It uses tagged application inference profiles, AWS cost allocation tags, department-based request routing, and AWS Cost Explorer to produce separate cost breakdowns. ### Source excerpt Learn how to track generative AI costs by department using Amazon Bedrock application inference profiles and AWS cost allocation tags. Create tagged profiles for each team and view per-department cost breakdowns in AWS Cost Explorer. ## Daybreak models are now available on AWS DevFeed: [Daybreak models are now available on AWS]() Original publisher: [Read original article]() Published: 2026-08-11T10:00:00Z Content type: release Language: en Sources: [OpenAI News]() Topics: [Amazon Bedrock](), [Cybersecurity](), [Amazon Web Services](), [OpenAI](), [Frontier AI](), [Detection engineering](), [Incident response]() Tags: [ai](), [amazon-bedrock](), [aws](), [bedrock](), [cybersecurity](), [incident-response](), [openai](), [product](), [validation](), [workflows]() ### AI overview OpenAI and AWS are making Daybreak Blue and Daybreak Red available through Amazon Bedrock for authorized defensive security work, including vulnerability research, exploit validation, detection engineering, and incident response. ### Source excerpt OpenAI and AWS are making Daybreak cybersecurity capabilities available through Amazon Bedrock to support enterprise security workflows. ## AWS Weekly Roundup: AWS Heroes Summit, Web Search on Amazon Bedrock, Dogwood, Kiro Crew, and more (August 10, 2026) DevFeed: [AWS Weekly Roundup: AWS Heroes Summit, Web Search on Amazon Bedrock, Dogwood, Kiro Crew, and more (August 10, 2026)]() Original publisher: [Read original article]() Author: Channy Yun (윤석찬) Published: 2026-08-10T15:45:03Z Content type: article Language: en Sources: [AWS News Blog]() Topics: [Amazon Bedrock](), [Amazon Web Services](), [Artificial Intelligence](), [Amazon Bedrock AgentCore](), [Amazon DynamoDB](), [AWS Transform](), [Embeddings](), [Containers](), [Serverless](), [Mainframe](), [OpenAI]() Tags: [ai](), [amazon](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [amazon-dynamodb](), [applications](), [aws](), [aws-lambda](), [aws-transform](), [bedrock](), [containers](), [embeddings](), [kiro](), [modernization](), [news](), [open-source](), [product](), [serverless](), [week-in-review]() ### AI overview This AWS Weekly Roundup covers the AWS Heroes Summit and selected launches, including web search for OpenAI models in Amazon Bedrock, dedicated runtime instances for agents in Amazon Bedrock AgentCore, vector search in Amazon DynamoDB, and general availability of AWS Transform continuous modernization. ### Source excerpt Last week, we brought together AWS Heroes from around the world to connect, collaborate, and celebrate the builders who go above and beyond for the AWS community. The AWS Heroes Summit, an invite-only annual gathering, brings global experts specializing in fields like AI, serverless, and containers together for direct collaboration, technical deep-dives, and feedback sessions [...] ## Agentic SDLC in Practice: Insights from Engineering Leaders DevFeed: [Agentic SDLC in Practice: Insights from Engineering Leaders]() Original publisher: [Read original article]() Author: Matar Peles Published: 2026-08-10T11:42:34Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [sdlc](), [Artificial Intelligence](), [AI Agent](), [Platform Engineering](), [Model Context Protocol](), [MCP Server](), [incident](), [Data Quality](), [Amazon Bedrock](), [Slack](), [cursor](), [Pull Request]() Tags: [agent](), [agentic](), [agents](), [ai](), [ai-agent](), [bedrock](), [coding](), [cursor](), [data-quality](), [engineering](), [incident](), [mcp](), [mcp-server](), [platform-engineering](), [python](), [review](), [sdlc](), [server](), [slack](), [workflow]() ### AI overview Engineering leaders share practical examples of an agentic SDLC, including autonomous Jira ticket resolution through pull request creation and AI-powered incident triage. The article highlights adoption challenges such as siloed agents, missing governance, and the need for a shared registry of agents, skills, and MCPs. ### Source excerpt Engineering leaders share how agentic SDLC works in practice: what scales adoption, where teams get stuck, and how to maximize AI ROI. ## Runtime instances: persistent compute for production AI agents on Amazon Bedrock AgentCore DevFeed: [Runtime instances: persistent compute for production AI agents on Amazon Bedrock AgentCore]() Original publisher: [Read original article]() Author: Sébastien Stormacq Published: 2026-08-06T22:58:00Z Content type: release Language: en Sources: [AWS News Blog]() Topics: [Amazon Bedrock](), [Artificial Intelligence](), [Amazon EC2](), [Amazon Web Services](), [GPU](), [observability]() Tags: [agents](), [ai](), [ai-agents](), [amazon](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [apis](), [aws](), [bedrock](), [compute](), [developers](), [framework](), [infrastructure](), [launch](), [memory](), [model](), [monitoring](), [news](), [os](), [production](), [storage](), [tools]() ### AI overview Amazon Bedrock AgentCore runtime instances provide persistent, AWS-managed EC2 infrastructure for production AI agents. The feature supports multi-agent collaboration, shared sessions lasting up to 14 days, GPU acceleration, containerized deployments, session stop/restart, and integration with EBS, AgentCore Memory, existing APIs, identity controls, and observability. ### Source excerpt Announcing runtime instances in Amazon Bedrock AgentCore--persistent, managed EC2 infrastructure for production AI agents with multi-agent collaboration, GPU support, and sessions lasting up to 14 days. [Next page]()