Does this skill require specific AWS tools?

Yes, for optimal performance, this skill requires AWS MCP tools (like aws-mcp) to access real-time AWS documentation and verify service availability.

What is AWS Bedrock AgentCore?

AWS Bedrock AgentCore is a comprehensive platform for deploying, scaling, and managing AI agents, providing services for runtime, memory, identity, and tool integration.

How are credentials handled?

The skill utilizes the AgentCore Identity service for unified credential management, supporting API keys, OAuth, and IAM roles with best practices for rotation and security.

How does the Gateway service work in this skill?

The Gateway service allows you to convert standard REST APIs into Model Context Protocol (MCP) tools, enabling AI agents to interact with your existing backend services.

Can I manage agent memory with this skill?

Yes, the skill provides specific workflows for the AgentCore Memory service to manage conversation state and long-term context for your agents.

AWS Bedrock AgentCore Expert

Name: AWS Bedrock AgentCore Expert
Author: I-Onlabs

byI-Onlabs

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Infraestructura en la Nube

Deploys and manages AI agents using the full suite of AWS Bedrock AgentCore services for scalable agentic workflows.

This skill provides expert guidance for architecting and deploying AI agents using AWS Bedrock AgentCore. it covers the entire lifecycle of agent development, including converting REST APIs into MCP tools via the Gateway service, managing persistent conversation state with Memory, and executing secure sandboxed code. By leveraging AWS CLI and MCP integration, it helps developers implement robust identity management, serverless runtimes, and deep observability with CloudWatch and X-Ray, ensuring that agentic AI applications are production-ready and securely integrated with AWS infrastructure.

Características Principales

01Stateful conversation management using AgentCore Memory

02Gateway target deployment for REST-to-MCP tool conversion

03Full observability setup with tracing and performance monitoring

042 GitHub stars

05Secure credential and identity provider configuration

06Serverless agent runtime scaling and configuration

Casos de Uso

01Exposing internal business APIs to AI agents as standardized MCP tools

02Building stateful AI assistants that retain context across multiple user sessions

03Establishing secure, sandboxed environments for agent-led code execution

Características Principales

01Stateful conversation management using AgentCore Memory

02Gateway target deployment for REST-to-MCP tool conversion

03Full observability setup with tracing and performance monitoring

042 GitHub stars

05Secure credential and identity provider configuration

06Serverless agent runtime scaling and configuration

Casos de Uso

01Exposing internal business APIs to AI agents as standardized MCP tools

02Building stateful AI assistants that retain context across multiple user sessions

03Establishing secure, sandboxed environments for agent-led code execution