AWS Bedrock AgentCore
FreeDeploy and manage AI agents at scale with AWS.
Free · Opens the source repo
What AWS Bedrock AgentCore does
AWS Bedrock AgentCore is designed for developers and engineers who need to deploy and manage AI agents effectively using AWS infrastructure. This skill provides comprehensive guidance on utilizing the core services of AWS Bedrock AgentCore, including Gateway, Runtime, Memory, Identity, Code Interpreter, Browser, Observability, Agent Registry, and Evaluations. It is particularly useful for those looking to implement scalable AI solutions, as it covers deployment patterns, service selection, and integration workflows through the AWS CLI.
The skill includes detailed instructions for common workflows, such as deploying a Gateway target, managing credentials across services, and utilizing the Agent Registry for resource discovery. Each service comes with its own README documentation, ensuring that users can find specific guidance tailored to their needs. The cross-service resources section is particularly valuable for addressing common integration challenges, such as credential management and security practices.
To effectively use this skill, you will need to verify AWS-specific details using the MCP documentation tools provided. The skill emphasizes the importance of using the correct documentation sources for AgentCore-specific questions and general AWS queries, ensuring that you have accurate and up-to-date information at your fingertips. This focus on documentation helps mitigate potential issues that can arise from misconfigurations or misunderstandings of service capabilities.
Overall, AWS Bedrock AgentCore is an essential tool for any developer or engineer looking to leverage AWS for AI agent deployment and management. Its structured approach to service integration and deployment makes it a reliable resource for both novice and experienced users in the AI development space.
When to use it
Use this skill when you need to deploy and manage AI agents using AWS Bedrock AgentCore services.
When not to use it
This skill may not be suitable for users not working within the AWS ecosystem or those needing a non-AWS solution for AI agent deployment.
What you can build with it
Deploying an AI Agent
Use this skill to set up and deploy an AI agent using the Runtime service, ensuring proper integration with other AWS services.
Managing Agent Credentials
Implement secure credential management across various AWS services to avoid authentication issues when deploying AI agents.
Evaluating Agent Performance
Utilize the Evaluations service to assess the quality of your AI agents and ensure they meet performance standards.
How to install AWS Bedrock AgentCore
View source1. Install with the skills CLI
npx skills add sickn33/agentic-awesome-skills/aws-agentic-ai --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by sickn33AWS 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, providessearch_agentcore_docsandfetch_agentcore_docfor AgentCore documentation - General AWS docs (
mcp__aws-mcp__*ormcp__*awsdocs*__*) — loaded via theaws-mcp-setupdependency 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.
- Upload OpenAPI schema to S3
- (API Key auth only) Create credential provider and store API key
- Create gateway target linking schema (and credentials if using API key)
- 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.
- Use Identity service credential providers for all API keys
- Link providers to gateway targets via ARN references
- Rotate credentials quarterly through credential provider updates
- Monitor usage with CloudWatch metrics
Discovering Agents and Tools (Agent Registry)
Read services/registry/README.md first — the registry has governance workflows, MCP endpoint options, and sync modes that affect how records become discoverable.
- Create a registry to catalog your organization's AI resources
- Register resources (MCP servers, agents, skills, custom) with descriptive metadata
- Submit records for approval (auto-approve for dev, manual for production)
- Search and discover approved resources via CLI or MCP endpoint
Agent Registry is in Preview. Available in us-east-1, us-west-2, eu-west-1, ap-northeast-1, ap-southeast-2.
Evaluating Agent Quality
Read services/evaluations/README.md first — evaluators, scoring modes, and IAM setup vary between online monitoring and on-demand testing.
- Instrument the agent with OpenTelemetry (ADOT) for trace collection
- Create evaluators (use built-in like
Builtin.Helpfulnessor create custom) - Set up online evaluation with sampling rate and data source
- Monitor scores in CloudWatch dashboards; investigate low-scoring sessions
Monitoring Agents
Read services/observability/README.md for the full monitoring setup — observability configuration depends on your Runtime protocol and framework choice.
- Enable observability for agents
- Configure CloudWatch dashboards for metrics
- Set up alarms for error rates and latency
- Use X-Ray for distributed tracing
Deep-Dive References
Each service README (linked in the table above) contains sub-links to getting-started guides, troubleshooting, and advanced topics. Start with the service README and follow pointers from there.
Advanced Runtime & OAuth References
Deep-dive reference documentation for Runtime internals, deployment, OAuth integration, and communication protocols. Read these when building production Runtime deployments or configuring OAuth authentication:
- OAuth Integration:
references/agentcore-oauth-integration.md- Three-layer OAuth architecture (Inbound JWT, Outbound Credential Provider, Gateway OAuth), Cognito configuration, supported IdPs, end-to-end CDK examples - Runtime Core Mechanisms:
references/agentcore-runtime-core.md- Container contract, MicroVM Session model, Agent lifecycle (per-request vs per-session), tool integration (MCP/HTTP), startup flow - Runtime Deployment & Operations:
references/agentcore-runtime-deploy.md- CDK deployment (L1/L2 constructs), multi-Runtime architecture, security model, observability (OTel/CloudWatch), BedrockAgentCoreApp vs FastAPI comparison - Runtime Protocol Reference:
references/agentcore-runtime-protocols.md- HTTP, MCP, A2A, AG-UI protocol specifications with container contracts, endpoint specs, and selection guide
Runnable Script Templates
Production-ready templates in scripts/ for common deployment patterns:
| Script | Protocol | Description |
|---|---|---|
Dockerfile.runtime-template | — | ARM64 multi-stage Docker build for AgentCore Runtime |
runtime-fastapi-template.py | HTTP | FastAPI Runtime with SSE streaming and MCPClient |
mcp-server-template.py | MCP | MCP Server with Streamable HTTP transport |
a2a-server-template.py | A2A | A2A Server with Agent Card discovery |
agui-server-template.py | AG-UI | AG-UI Server with standard AG-UI event stream |
gateway-custom-resource-lambda.py | — | CDK Custom Resource Lambda for Gateway lifecycle |
Cross-Service Resources
For patterns and best practices that span multiple AgentCore services:
- Credential Management:
cross-service/credential-management.md- Unified credential patterns, security practices, rotation procedures - Registry Integration:
cross-service/registry-integration.md- Cross-service patterns with Gateway, Identity, Runtime - Security & Resource Policies:
cross-service/security-resource-policies.md- Resource-based policies, cross-account access, VPC/IP restrictions - Agent Deployment with S3 Files:
cross-service/agent-persistence-patterns.md- Deploy Strands Agents, OpenClaw, Claude Agent SDK on AgentCore with S3 Files and Session Storage
Additional Resources
- AWS Documentation: Amazon Bedrock AgentCore
- API Reference: Bedrock AgentCore Control Plane API
- AWS CLI Reference: bedrock-agentcore-control commands
Limitations
- Use this skill only when the task clearly matches its upstream source and local project context.
- Verify commands, generated code, dependencies, credentials, and external service behavior before applying changes.
- Do not treat examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.
Frequently asked questions about AWS Bedrock AgentCore
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