aws-agentic-ai

name	aws-agentic-ai
aliases	["bedrock-agentcore","aws-agentic-ai"]
description	AWS Bedrock AgentCore comprehensive expert for deploying and managing all AgentCore services. Use when working with Gateway, Runtime, Memory, Identity, or any AgentCore component. Covers MCP target deployment, credential management, schema optimization, runtime configuration, memory management, and identity services.
context	fork
model	sonnet
skills	["aws-mcp-setup"]
allowed-tools	["mcp__aws-mcp__*","mcp__awsdocs__*","Bash(aws bedrock-agentcore-control *)","Bash(aws bedrock-agentcore-runtime *)","Bash(aws bedrock *)","Bash(aws s3 cp *)","Bash(aws s3 ls *)","Bash(aws secretsmanager *)","Bash(aws sts get-caller-identity)"]
hooks	{"PreToolUse":[{"matcher":"Bash(aws bedrock-agentcore-control create-*)","command":"aws sts get-caller-identity --query Account --output text","once":true}]}

AWS Bedrock AgentCore

AWS Bedrock AgentCore provides a complete platform for deploying and scaling AI agents with seven core services. This skill guides you through service selection, deployment patterns, and integration workflows using AWS CLI.

AWS Documentation Requirement

CRITICAL: This skill requires AWS MCP tools for accurate, up-to-date AWS information.

Before Answering AWS Questions

1. Always verify using AWS MCP tools (if available):

   • mcp__aws-mcp__aws___search_documentation or mcp__*awsdocs*__aws___search_documentation - Search AWS docs
   • mcp__aws-mcp__aws___read_documentation or mcp__*awsdocs*__aws___read_documentation - Read specific pages
   • mcp__aws-mcp__aws___get_regional_availability - Check service availability

2. If AWS MCP tools are unavailable:

   • Guide user to configure AWS MCP using the aws-mcp-setup skill (auto-loaded as dependency)
   • Help determine which option fits their environment:
     • Has uvx + AWS credentials → Full AWS MCP Server
     • No Python/credentials → AWS Documentation MCP (no auth)
   • If cannot determine → Ask user which option to use

When to Use This Skill

Use this skill when you need to:

• Deploy REST APIs as MCP tools for AI agents (Gateway)
• Execute agents in serverless runtime (Runtime)
• Add conversation memory to agents (Memory)
• Manage API credentials and authentication (Identity)
• Enable agents to execute code securely (Code Interpreter)
• Allow agents to interact with websites (Browser)
• Monitor and trace agent performance (Observability)

Available Services

Service	Use For	Documentation
Gateway	Converting REST APIs to MCP tools	services/gateway/README.md
Runtime	Deploying and scaling agents