Is credential management secure?

Yes, the skill follows AWS best practices by utilizing the Identity service and credential providers for secure API key storage and rotation.

What is AWS Bedrock AgentCore?

It is a suite of services provided by AWS to deploy, scale, and manage AI agents, including specialized tools for memory, identity, and tool execution.

How does it handle agent memory?

The skill provides patterns for implementing the Memory service to maintain conversation state and context across different agent sessions.

Can I use this for REST API integration?

Absolutely. The Gateway service specifically helps convert REST APIs into MCP tools that agents can interact with via OpenAPI schemas.

Does this skill require extra tools?

Yes, it works best with AWS MCP (Model Context Protocol) tools to fetch live documentation and verify regional service availability.

AWS Bedrock AgentCore Assistant

Name: AWS Bedrock AgentCore Assistant
Author: zxkane

byzxkane

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Infraestructura en la Nube

Deploys and manages AI agents using the AWS Bedrock AgentCore suite of services for scaling, memory, and tool integration.

Acerca de

This skill provides comprehensive expertise for the AWS Bedrock AgentCore platform, enabling developers to build, deploy, and scale sophisticated AI agents. It guides users through the implementation of core services including the Gateway for converting REST APIs into Model Context Protocol (MCP) tools, Runtime for serverless agent execution, and specialized modules for conversation memory, identity management, and secure code execution. By leveraging AWS MCP tools, it ensures accurate configuration of infrastructure, IAM roles, and observability patterns for production-grade agentic workflows.

Características Principales

Serverless agent runtime configuration and scaling
Secure credential handling via Identity service
Observability setup with CloudWatch and X-Ray tracing
Gateway deployment for REST-to-MCP tool conversion
Implementation of conversation memory and state management
43 GitHub stars

Casos de Uso

Implementing secure web automation or sandboxed code execution within agent workflows
Exposing existing REST APIs as tools for AI agents using the Bedrock Gateway
Building scalable AI agents that require long-term memory and secure tool access

Acerca de

Características Principales

Serverless agent runtime configuration and scaling
Secure credential handling via Identity service
Observability setup with CloudWatch and X-Ray tracing
Gateway deployment for REST-to-MCP tool conversion
Implementation of conversation memory and state management
43 GitHub stars

Casos de Uso

Implementing secure web automation or sandboxed code execution within agent workflows
Exposing existing REST APIs as tools for AI agents using the Bedrock Gateway
Building scalable AI agents that require long-term memory and secure tool access