Can I use this skill to manage API keys?

Yes, the skill includes specialized workflows for the Identity service to manage credential providers and link them to Gateway targets securely.

What is AWS Bedrock AgentCore?

AWS Bedrock AgentCore is a suite of seven services (Gateway, Runtime, Memory, Identity, Code Interpreter, Browser, and Observability) designed to deploy and scale AI agents effectively.

Is it compatible with serverless architectures?

Absolutely. It specializes in configuring the AgentCore Runtime for serverless agent execution and scaling without managing underlying servers.

Does this skill require specific AWS permissions?

Yes, it is designed to work with AWS MCP tools which require appropriate IAM credentials to search documentation and manage regional service availability.

How does it help with agent memory?

It provides implementation patterns for the Memory service, allowing you to add and manage conversation state so agents can remember past interactions.

AWS Bedrock AgentCore Manager

Name: AWS Bedrock AgentCore Manager
Author: rafaelcalleja

byrafaelcalleja

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Cloud Infrastructure

Deploys and manages AWS Bedrock AgentCore services to build, scale, and monitor sophisticated AI agents.

About

The AWS Bedrock AgentCore skill provides specialized expertise for architecting and deploying AI agent infrastructure on AWS. It guides developers through the entire AgentCore ecosystem, including converting REST APIs into MCP tools with Gateway, managing serverless agent execution with Runtime, and maintaining persistent state with Memory. The skill emphasizes security through the Identity service and performance through Observability, while leveraging AWS MCP tools to ensure all configurations and documentation references are up-to-date and accurate for production environments.

Key Features

Configuration of serverless runtimes for scaling agent execution
Enhanced monitoring with CloudWatch and X-Ray observability
1 GitHub stars
Automated deployment of REST APIs as MCP tools via Gateway
Secure credential rotation and identity management integration
Persistent conversation state management with Memory services

Use Cases

Deploying secure, sandboxed code execution environments for agentic workflows
Building stateful AI assistants with long-term memory on AWS infrastructure
Transforming legacy backend services into discoverable tools for AI agents

About

Key Features

Configuration of serverless runtimes for scaling agent execution
Enhanced monitoring with CloudWatch and X-Ray observability
1 GitHub stars
Automated deployment of REST APIs as MCP tools via Gateway
Secure credential rotation and identity management integration
Persistent conversation state management with Memory services

Use Cases

Deploying secure, sandboxed code execution environments for agentic workflows
Building stateful AI assistants with long-term memory on AWS infrastructure
Transforming legacy backend services into discoverable tools for AI agents