How does it handle agent memory?

It provides workflows for the Memory service, which manages conversation state and history, allowing agents to maintain context across multiple sessions.

How does the Gateway service work in this skill?

The Gateway service converts your existing REST APIs into MCP (Model Context Protocol) tools, allowing AI agents to interact with your internal systems via OpenAPI schemas.

What is AWS Bedrock AgentCore?

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

Can I use this skill without an existing AWS setup?

The skill includes an 'aws-mcp-setup' dependency to help you configure the necessary Model Context Protocol connections if they aren't already present in your environment.

Does this skill require specific AWS permissions?

Yes, this skill uses the AWS CLI and requires appropriate IAM permissions for Bedrock AgentCore, S3, Secrets Manager, and STS to execute deployment and management tasks.

AWS Bedrock AgentCore Manager

Name: AWS Bedrock AgentCore Manager
Author: zxkane

byzxkane

•

Infraestructura en la Nube

Deploys, manages, and scales AI agents using the comprehensive AWS Bedrock AgentCore service ecosystem.

Acerca de

This skill provides specialized expertise for the AWS Bedrock AgentCore platform, enabling developers to build production-grade AI agents with integrated memory, identity, and tool-calling capabilities. It streamlines the deployment of REST APIs as MCP tools via the Gateway service, manages serverless agent execution through Runtime, and handles persistent conversation state with Memory services. By leveraging AWS CLI tools and the Model Context Protocol (MCP), it automates complex workflows like credential rotation, schema optimization, and multi-service observability, ensuring your agentic workflows are secure, scalable, and well-monitored within the AWS environment.

Características Principales

Unified credential management and rotation patterns using AWS Identity services
Automated conversion of REST APIs into MCP tools for agentic use
Advanced observability integration for tracing and monitoring agent performance
Comprehensive management of all seven AgentCore services including Gateway and Runtime
Setup and configuration of secure sandboxed environments for code execution
97 GitHub stars

Casos de Uso

Scaling serverless AI agents with automated credential management and identity services
Deploying enterprise AI agents with long-term memory and secure API integration
Implementing secure web automation and code execution capabilities for LLM-based workflows

Acerca de

Características Principales

Unified credential management and rotation patterns using AWS Identity services
Automated conversion of REST APIs into MCP tools for agentic use
Advanced observability integration for tracing and monitoring agent performance
Comprehensive management of all seven AgentCore services including Gateway and Runtime
Setup and configuration of secure sandboxed environments for code execution
97 GitHub stars

Casos de Uso

Scaling serverless AI agents with automated credential management and identity services
Deploying enterprise AI agents with long-term memory and secure API integration
Implementing secure web automation and code execution capabilities for LLM-based workflows