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AWS Agentic AI

AWS Bedrock AgentCore skill for deploying, managing, and scaling agent infrastructure across Gateway, Runtime, Memory, Identity, and related services.

by @zxkane · MIT New

What this skill does

Deploy and manage complete AI agent infrastructure on AWS Bedrock to move your projects from prototype to production. You will be able to set up secure environments where agents can remember conversations, execute code, and interact with websites safely. Use this whenever you need to scale agent operations or connect your AI to external tools without handling complex cloud configuration.

@zxkane · Development
view on github ↗

name: aws-agentic-ai aliases:

  • bedrock-agentcore description: AWS Bedrock AgentCore comprehensive expert for deploying and managing all AgentCore services. Use when working with Gateway, Runtime, Memory, Identity, or any AgentCore component. Covers MCP target deployment, credential management, schema optimization, runtime configuration, memory management, and identity services. context: fork model: sonnet skills:
  • aws-mcp-setup allowed-tools:
  • mcp__aws-mcp__*
  • mcp__awsdocs__*
  • Bash(aws bedrock-agentcore-control *)
  • Bash(aws bedrock-agentcore-runtime *)
  • Bash(aws bedrock *)
  • Bash(aws s3 cp *)
  • Bash(aws s3 ls *)
  • Bash(aws secretsmanager *)
  • Bash(aws sts get-caller-identity) hooks: PreToolUse:
    • matcher: Bash(aws bedrock-agentcore-control create-*) command: aws sts get-caller-identity —query Account —output text once: true

AWS Bedrock AgentCore

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

AWS Documentation Requirement

Always verify AWS facts using MCP tools (mcp__aws-mcp__* or mcp__*awsdocs*__*) before answering. The aws-mcp-setup dependency is auto-loaded — if MCP tools are unavailable, guide the user through that skill’s setup flow.

When to Use This Skill

Use this skill when you need to:

  • Deploy REST APIs as MCP tools for AI agents (Gateway)
  • Execute agents in serverless runtime (Runtime)
  • Add conversation memory to agents (Memory)
  • Manage API credentials and authentication (Identity)
  • Enable agents to execute code securely (Code Interpreter)
  • Allow agents to interact with websites (Browser)
  • Monitor and trace agent performance (Observability)

Available Services

ServiceUse ForDocumentation
GatewayConverting REST APIs to MCP toolsservices/gateway/README.md
RuntimeDeploying and scaling agentsservices/runtime/README.md
MemoryManaging conversation stateservices/memory/README.md
IdentityCredential and access managementservices/identity/README.md
Code InterpreterSecure code execution in sandboxesservices/code-interpreter/README.md
BrowserWeb automation and scrapingservices/browser/README.md
ObservabilityTracing and monitoringservices/observability/README.md

Common Workflows

Deploying a Gateway Target

MANDATORY - READ DETAILED DOCUMENTATION: See services/gateway/README.md for complete Gateway setup guide including deployment strategies, troubleshooting, and IAM configuration.

Quick Workflow:

  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

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

Managing Credentials

MANDATORY - READ DETAILED DOCUMENTATION: See cross-service/credential-management.md for unified credential management patterns across all services.

Quick Workflow:

  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

Monitoring Agents

MANDATORY - READ DETAILED DOCUMENTATION: See services/observability/README.md for comprehensive monitoring setup.

Quick Workflow:

  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

Service-Specific Documentation

For detailed documentation on each AgentCore service, see the following resources:

Gateway Service

Runtime, Memory, Identity, Code Interpreter, Browser, Observability

Each service has comprehensive documentation in its respective directory:

Cross-Service Resources

For patterns and best practices that span multiple AgentCore services:

Additional Resources

Install this Skill

Skills give your AI agent a consistent, structured approach to this task — better output than a one-off prompt.

npx skills add zxkane/aws-skills --skill plugins/aws-agentic-ai
Download ZIP

Community skill by @zxkane. Need a walkthrough? See the install guide →

Works with

Prefer no terminal? Download the ZIP and place it manually.

Details

Category
Development
License
MIT
Author
@zxkane
Source file
show path plugins/aws-agentic-ai/skills/aws-agentic-ai/SKILL.md
aws bedrock agents