Category: Announcements

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.

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.

Today, we’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.

Today, we’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.

In this post, we share how Amazon Bedrock’s granular cost attribution works and walk through example cost tracking scenarios.

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.

Today, we’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.

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.

In this post, we go through how to use managed session storage to persist your agent’s filesystem state and how to execute shell commands directly in your agent’s environment.

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.