Aws-skills aws-agentic-ai

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 tool integration, credential management, agent discovery, governance workflows, and automated quality assessment. Essential when user mentions AgentCore, agent runtime, agent registry, agent evaluation, MCP gateway, deploy agent, register MCP server, discover agents, evaluate agent quality, agent credentials, or wants to build, deploy, catalog, or monitor AI agents on AWS.

install

source · Clone the upstream repo

git clone https://github.com/zxkane/aws-skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/zxkane/aws-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/plugins/aws-agentic-ai/skills/aws-agentic-ai" ~/.claude/skills/zxkane-aws-skills-aws-agentic-ai && rm -rf "$T"

manifest: plugins/aws-agentic-ai/skills/aws-agentic-ai/SKILL.md

AWS Bedrock AgentCore

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.

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.Helpfulness
```
or 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