# Amazon Bedrock AgentCore Amazon Bedrock AgentCore is a platform for building, connecting, securing, debugging, evaluating, and scaling production AI agents across frameworks and 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. ## 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. ## AWS agents will suggest your new flights. Code decides what gets booked. DevFeed: [AWS agents will suggest your new flights. Code decides what gets booked.]() Original publisher: [Read original article]() Author: Meredith Shubel Published: 2026-09-15T21:27:21Z Content type: news Language: en Sources: [The New Stack]() Topics: [AWS Step Functions](), [Amazon Bedrock AgentCore](), [Orchestration](), [Generative AI](), [Amazon Web Services]() Tags: [ai-agents](), [ai-engineering](), [airline](), [amazon-bedrock-agentcore](), [aws](), [aws-step-functions](), [cloud-services](), [generative-ai](), [orchestration]() ### AI overview AWS published a Step Functions pattern for airline rebooking in which Amazon Bedrock AgentCore agents propose itineraries and compensation messages, while deterministic workflow steps validate proposals before reservations change or payments are issued. ### Source excerpt AWS published a new Step Functions pattern this week that gives AI agents a role in airline rebooking while keeping The post AWS agents will suggest your new flights. Code decides what gets booked. appeared first on The New Stack. ## 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. ## 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. ## Validating multi-agent decisions with Step Functions and Bedrock AgentCore DevFeed: [Validating multi-agent decisions with Step Functions and Bedrock AgentCore]() Original publisher: [Read original article]() Author: Ben Freiberg Published: 2026-09-14T16:47:23Z Content type: article Language: en Sources: [AWS Compute Blog]() Topics: [Amazon Bedrock AgentCore](), [AWS Step Functions](), [Artificial Intelligence](), [Generative AI](), [Automation]() Tags: [ai-agents](), [amazon-bedrock-agentcore](), [audit-trail](), [automation](), [aws-step-functions](), [generative-ai](), [regulatory](), [technical-how-to](), [workflow]() ### AI overview The article presents a pattern for validating multi-agent airline rebooking decisions. Amazon Bedrock AgentCore agents propose options, while AWS Step Functions applies deterministic validation, supports large-scale parallel processing, enables human review, and maintains an execution history for auditing. ### Source excerpt Orchestrating specialized Amazon Bedrock AgentCore agents with AWS Step Functions gives you the reasoning power of generative AI with the guardrails of deterministic validation. Agents propose options, and deterministic code validates them before any action is taken, demonstrated here with an airline rebooking workflow. ## 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. ## Propagate user authorization context in AI agents with Amazon Bedrock AgentCore DevFeed: [Propagate user authorization context in AI agents with Amazon Bedrock AgentCore]() Original publisher: [Read original article]() Author: Anshu Bathla Published: 2026-08-19T17:24:15Z Content type: article Language: en Sources: [AWS Security Blog]() Topics: [Amazon Bedrock AgentCore](), [AI Agent](), [Authorization](), [AWS Identity and Access Management (IAM)](), [Amazon Bedrock Knowledge Bases](), [Amazon DynamoDB](), [Amazon S3](), [prompt injection](), [Amazon Web Services](), [Software as a service]() Tags: [access-control](), [advanced-300](), [ai-agents](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [amazon-bedrock-knowledge-bases](), [amazon-dynamodb](), [amazon-s3](), [artificial-intelligence](), [authorization](), [aws](), [prompt-injection](), [security-blog](), [security-identity-compliance](), [technical-how-to]() ### AI overview This article explains how to propagate user authorization context through AI agents built with Amazon Bedrock AgentCore. It presents a pattern for enforcing least-privilege access in downstream data services and infrastructure, so agents can access DynamoDB, Bedrock Knowledge Bases, S3-backed documents, and other sources only within the requesting user's permissions. ### Source excerpt Many teams now deploy AI agents that pull from Amazon DynamoDB tables, document repositories, software as a service (SaaS) platforms, and internal knowledge bases to answer questions and automate workflows. A key risk in these deployments is that the agent has no awareness of who's asking, so it might return data the user shouldn't see. [...] ## 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: 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 [...] ## When AI Agents Call AWS, Who Does AWS Think They Are? DevFeed: [When AI Agents Call AWS, Who Does AWS Think They Are?]() Original publisher: [Read original article]() Author: info@goteleport.com (Jeffrey Ellin) Published: 2026-07-08T00:00:00Z Content type: tutorial Language: en Sources: [Teleport]() Topics: [Amazon Bedrock AgentCore](), [Amazon Web Services](), [MCP](), [AI Agent](), [Model Context Protocol](), [JSON Web Tokens](), [AWS IAM](), [Amazon S3]() Tags: [agents](), [ai](), [ai-agents](), [amazon-bedrock-agentcore](), [aws](), [gateway](), [how-to](), [iam](), [jwt](), [mcp](), [s3]() ### AI overview This tutorial explains how to propagate a verified caller identity through an AI agent, Amazon Bedrock AgentCore, Lambda, and AWS API calls. It uses short-lived Teleport JWTs so downstream tools can associate requests with the authenticated user and improve accountability in CloudTrail. ### Source excerpt Learn how to use Teleport JWTs to give MCP tools a verified identity in Amazon Bedrock AgentCore. ## OpenAI and Amazon announce strategic partnership DevFeed: [OpenAI and Amazon announce strategic partnership]() Original publisher: [Read original article]() Published: 2026-02-27T05:30:00Z Content type: news Language: en Sources: [OpenAI News]() Topics: [amazon](), [Amazon Web Services (AWS)](), [OpenAI](), [AI Infrastructure](), [bedrock](), [Generative AI](), [Artificial Intelligence](), [Amazon Bedrock AgentCore]() Tags: [agents](), [ai-infrastructure](), [amazon](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [amazon-web-services-aws](), [company](), [generative-ai](), [infrastructure](), [openai](), [security]() ### AI overview OpenAI and Amazon announce a multi-year strategic partnership involving AWS distribution of OpenAI Frontier, a Stateful Runtime Environment on Amazon Bedrock, customized models, AI infrastructure, and a $50 billion Amazon investment in OpenAI. ### Source excerpt OpenAI and Amazon announce a strategic partnership bringing OpenAI's Frontier platform to AWS, expanding AI infrastructure, custom models, and enterprise AI agents. ## Seven Years of Firecracker DevFeed: [Seven Years of Firecracker]() Original publisher: [Read original article]() Author: Marc Brooker Published: 2025-09-18T00:00:00Z Content type: article Language: en Sources: [Marc Brooker's Blog](), [Marc Brooker's Blog]() Topics: [Firecracker](), [virtualization](), [Amazon Web Services](), [Security](), [Open Source](), [Amazon Bedrock AgentCore](), [Serverless](), [Artificial Intelligence](), [Multitenancy](), [Large Language Model](), [Python]() Tags: [ai](), [amazon-bedrock-agentcore](), [aws](), [aws-lambda](), [firecracker](), [llm](), [multitenancy](), [open-source](), [python](), [security](), [virtualization]() ### AI overview This article reflects on seven years of Firecracker, an open source technology for creating and managing small virtual machines. It describes Firecracker's role in AWS Lambda, its security and compatibility benefits compared with container and language-level isolation, its use across AWS, and its role in Amazon Bedrock AgentCore for running AI agents. ### Source excerpt Seven Years of Firecracker Time flies like an arrow. Fruit flies like a banana. Back at re:Invent 2018, we shared Firecracker with the world. Firecracker is open source software that makes it easy to create and manage small virtual machines. At the time, we talked about Firecracker as one of the key technologies behind AWS Lambda, including how it'd allowed us to make Lambda faster, more efficient, and more secure. A couple years later, we published Firecracker: Lightweight Virtualization for Serverless Applications (at NSDI'20). Here's me talking through the paper back then: The paper went into more detail into how we're using Firecracker in Lambda, how we think about the economics of multitenancy (more about that here), and how we chose virtualization over kernel-level isolation (containers) or language-level isolation for Lambda. Despite these challenges, virtualization provides many compelling benefits. From an isolation perspective, the most compelling benefit is that it moves the security-critical interface from the OS boundary to a boundary supported in hardware and comparatively simpler software. It removes the need to trade off between kernel features and security: the guest kernel can supply its full feature set with no change to the threat model. VMMs are much smaller than general-purpose OS kernels, exposing a small number of well-understood abstractions without compromising on software compatibility or requiring software to be modified. Firecracker has really taken off, in all three ways we hoped it would. First, we use it in many more places inside AWS, backing the infrastructure we offer to customers across multiple services. Second, folks use the open source version directly, building their own cool products and businesses on it. Third, it was the motivation for a wave of innovation in the VM space. In this post, I wanted to write a bit about two of the ways we're using Firecracker at AWS that weren't covered in the paper. Bedrock AgentCore Back in J