aws-agentic-ai – skill DevOps & Hạ tầng cho agent AI

Chuyên gia toàn diện về AWS Bedrock AgentCore để triển khai và quản lý AI agent ở quy mô lớn. Dùng khi làm việc với bất kỳ dịch vụ AgentCore nào gồm Gateway, Runtime, Memory, Identity, Code Interpreter, Browser, Observability, Agent Registry hoặc Evaluations.

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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

Verify current AWS facts through available primary documentation tools or official AWS documentation. The following are optional upstream integrations, not tools bundled by this single-file catalog skill:

AgentCore-specific docs (mcp__acdocs__*) — upstream integration that may provide search_agentcore_docs and fetch_agentcore_doc for AgentCore documentation
General AWS docs (mcp__aws-mcp__* or mcp__*awsdocs*__*) — may be configured separately for broader AWS documentation

Prefer the AgentCore docs MCP for AgentCore-specific questions. If these tools are unavailable, use official documentation; do not install a new server unless requested.

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) |

This catalog contains no service README or executable templates locally. Read the selected upstream revision and inspect every dependency before implementation; do not claim those references were reviewed from their titles. Verify account/region/IAM, costs, data retention and publication scope before provisioning or sending conversation/trace data to cloud services.

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

> Verify current Agent Registry release status and regional availability in official AWS documentation before planning a deployment.

Evaluating Agent Quality

Read [services/evaluations/README.md](https://github.com/zxkane/aws-skills/tree/main/plugins/aws-agentic-ai/skills/aws-agentic-ai/services/evaluations/README.md) first — evaluators, scoring modes, and IAM setup vary between online monitoring and on-demand testing.

1. Instrument the agent with OpenTelemetry (ADOT) for trace collection

2. Create evaluators (use built-in like Builtin.Helpfulness or create custom)

3. Set up online evaluation with sampling rate and data source

4. Monitor scores in CloudWatch dashboards; investigate low-scoring sessions

Monitoring Agents

Read [services/observability/README.md](https://github.com/zxkane/aws-skills/tree/main/plugins/aws-agentic-ai/skills/aws-agentic-ai/services/observability/README.md) for the full monitoring setup — observability configuration depends on your Runtime protocol and framework choice.

1. Enable observability for agents

2. Configure CloudWatch dashboards for metrics

3. Set up alarms for error rates and latency

4. 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](https://github.com/zxkane/aws-skills/tree/main/plugins/aws-agentic-ai/skills/aws-agentic-ai/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](https://github.com/zxkane/aws-skills/tree/main/plugins/aws-agentic-ai/skills/aws-agentic-ai/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](https://github.com/zxkane/aws-skills/tree/main/plugins/aws-agentic-ai/skills/aws-agentic-ai/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](https://github.com/zxkane/aws-skills/tree/main/plugins/aws-agentic-ai/skills/aws-agentic-ai/references/agentcore-runtime-protocols.md) - HTTP, MCP, A2A, AG-UI protocol specifications with container contracts, endpoint specs, and selection guide

Runnable Script Templates

External upstream templates (not bundled or product

… (nội dung đầy đủ có trong gói tải về)

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