aws-agentic-ai

# 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`](services/gateway/README.md) |
| **Runtime** | Deploying and scaling agents | [`services/runtime/README.md`](services/runtime/README.md) |
| **Memory** | Managing conversation state | [`services/memory/README.md`](services/memory/README.md