aws-agentic-ai

name	aws-agentic-ai
description	AWS Bedrock AgentCore comprehensive expert for deploying and managing AI agents at scale. Use when working with any AgentCore service including Gateway, Runtime, Memory, Identity, Code Interpreter, Br
category	AI & Agents
source	antigravity
tags	["api","mcp","claude","ai","agent","llm","automation","workflow","template","document"]
url	https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/aws-agentic-ai

AWS Bedrock AgentCore

When to Use

Use this skill when you need aWS Bedrock AgentCore comprehensive expert for deploying and managing AI agents at scale. Use when working with any AgentCore service including Gateway, Runtime, Memory, Identity, Code Interpreter, Browser, Observability, Agent Registry, or Evaluations. Covers agent deployment, MCP...

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

How to use this skill: Identify the service(s) the user needs from the table below, then read the corresponding service README before responding. For cross-service patterns (credentials, security, registry integration), check the Cross-Service Resources section. Verify AWS-specific details using the MCP documentation tools.

AWS Documentation Requirement

Always verify AWS facts using MCP tools before answering. Two documentation sources are available:

• AgentCore-specific docs (mcp__acdocs__*) — bundled with this plugin, provides search_agentcore_docs and fetch_agentcore_doc for AgentCore documentation
• General AWS docs (mcp__aws-mcp__* or mcp__*awsdocs*__*) — loaded via the aws-mcp-setup dependency for broader AWS documentation

Prefer the AgentCore docs MCP for AgentCore-specific questions. If MCP tools are unavailable, guide the user through the aws-mcp-setup skill's setup flow.

Available Services

Service	Use For	Documentation
Gateway	Converting REST APIs to MCP tools	services/gateway/README.md
Runtime	Deploying and scaling agents	services/runtime/README.md
Memory	Managing conversation state	services/memory/README.md
Identity	Credential and access management	services/identity/README.md
Code Interpreter	Secure code execution in sandboxes	services/code-interpreter/README.md
Browser	Web automation and scraping	services/browser/README.md
Observability	Tracing and monitoring	services/observability/README.md
Agent Registry	Catalog, discover, and govern agents/tools (Preview)	services/registry/README.md
Evaluations	Automated agent quality assessment (LLM-as-a-Judge)	services/evaluations/README.md

Common Workflows

Deploying a Gateway Target

Read services/gateway/README.md before implementing — Gateway setup involves deployment strategies, IAM, and auth choices that vary significantly by use case.

1. Upload OpenAPI schema to S3
2. (API Key auth only) Create credential provider and store API key
3. Create gateway target linking schema (and credentials if using API key)
4. Verify target status and test connectivity

Credential provider is only needed for API key authentication. Lambda targets use IAM roles, and MCP servers use OAuth.

Managing Credentials

Read cross-service/credential-management.md first — credential patterns differ across services and getting them wrong causes hard-to-debug auth failures.

1. Use Identity service credential providers for all API keys
2. Link providers to gateway targets via ARN references
3. Rotate credentials quarterly through credential provider updates
4. Monitor usage with CloudWatch metrics

Discovering Agents and Tools (Agent Registry)

Read services/registry/README.md first — the registry has governance workflows,