/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, Browser, Observability, Agent Registry, or Evaluations. Covers agent deployment, MCP...
risk: critical
source: https://github.com/zxkane/aws-skills/tree/main/plugins/aws-agentic-ai/skills/aws-agentic-ai
source_repo: zxkane/aws-skills
source_type: community
date_added: 2026-07-01
license: MIT
license_source: https://github.com/zxkane/aws-skills/blob/main/LICENSE

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`](https://github.com/zxkane/aws-skills/tree/main/plugins/aws-agentic-ai/skills/aws-agentic-ai/services/gateway/README.md) | | **Runtime** | Deploying and scaling agents | [`services/runtime/README.md`](https://github.com/zxkane/aws-skills/tree/main/plugins/aws-agentic-ai/skills/aws-agentic-ai/services/runtime/README.md) | | **Memory** | Managing conversation state | [`services/memory/README.md`](https://github.com/zxkane/aws-skills/tree/main/plugins/aws-agentic-ai/skills/aws-agentic-ai/services/memory/README.md) | | **Identity** | Credential and access management | [`services/identity/README.md`](https://github.com/zxkane/aws-skills/tree/main/plugins/aws-agentic-ai/skills/aws-agentic-ai/services/identity/README.md) | | **Code Interpreter** | Secure code execution in sandboxes | [`services/code-interpreter/README.md`](https://github.com/zxkane/aws-skills/tree/main/plugins/aws-agentic-ai/skills/aws-agentic-ai/services/code-interpreter/README.md) | | **Browser** | Web automation and scraping | [`services/browser/README.md`](https://github.com/zxkane/aws-skills/tree/main/plugins/aws-agentic-ai/skills/aws-agentic-ai/services/browser/README.md) | | **Observability** | Tracing and monitoring | [`services/observability/README.md`](https://github.com/zxkane/aws-skills/tree/main/plugins/aws-agentic-ai/skills/aws-agentic-ai/services/observability/README.md) | | **Agent Registry** | Catalog, discover, and govern agents/tools (Preview) | [`services/registry/README.md`](https://github.com/zxkane/aws-skills/tree/main/plugins/aws-agentic-ai/skills/aws-agentic-ai/services/registry/README.md) | | **Evaluations** | Automated agent quality assessment (LLM-as-a-Judge) | [`services/evaluations/README.md`](https://github.com/zxkane/aws-skills/tree/main/plugins/aws-agentic-ai/skills/aws-agentic-ai/services/evaluations/README.md) |

Common Workflows

Deploying a Gateway Target

Read [`services/gateway/README.md`](https://github.com/zxkane/aws-skills/tree/main/plugins/aws-agentic-ai/skills/aws-agentic-ai/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`](https://github.com/zxkane/aws-skills/tree/main/plugins/aws-agentic-ai/skills/aws-agentic-ai/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`](https://github.com/zxkane/aws-skills/tree/main/plugins/aws-agentic-ai/skills/aws-agentic-ai/services/registry/README.md) first — the registry has governance workflows, MCP endpoint options, and sync modes that affect how records become discoverable.

1. Create a registry to catalog your organization's AI resources 2. Register resources (MCP servers, agents, skills, custom) with descriptive metadata 3. Submit records for approval (auto-approve for dev, manual for production) 4. Search and discover approved resources via CLI or MCP endpoint

> Agent Registry