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1. Building a secure auth code flow setup using AgentCore Gateway with MCP clients

   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.

2. Reference your own AWS Secrets Manager secrets in Amazon Bedrock AgentCore Identity

   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...

3. Transforming rare cancer research with Amazon Quick: Integrating biomedical databases for breakthrough discoveries

   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...

4. OpenAI models and Codex on Amazon Bedrock are now generally available

   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. 

5. Extending MCP support for Amazon Bedrock AgentCore Gateway

   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...

6. Secure AI agents with Policy and Lambda interceptors in Amazon Bedrock AgentCore gateway

   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.

7. Enable safe agentic payments with built-in guardrails using Amazon Bedrock AgentCore payments

   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.

8. AgentOps: Operationalize agentic AI at scale with Amazon Bedrock AgentCore

   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't just execute predetermined workflows. They reason, adapt, and make autonomous decisions, and DevOps practices need to be adapted. That's where AgentOps comes in, the...

9. Accelerate LLM model loading and increase context windows with GPUDirect on Amazon FSx for Lustre and TurboQuant

   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 […]

10. Amazon Quick integration with time-series databases for market intelligence using MCP

    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...