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AWS Agentic AI

Bedrock AgentCore skill for building AI agents with AWS Bedrock, including service-specific documentation and cross-service patterns.

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Overview

Build AI agents on AWS using Amazon Bedrock with comprehensive patterns for agentic workflows, tool integration, and multi-service orchestration.

Key Services

Amazon Bedrock

  • Foundation model access
  • Agent runtime
  • Knowledge bases
  • Guardrails
  • Model customization

Supporting Services

  • Lambda for tool execution
  • DynamoDB for state management
  • S3 for data storage
  • EventBridge for orchestration
  • Step Functions for workflows

Agent Capabilities

Core Features

  • Multi-turn conversations
  • Tool/function calling
  • Knowledge base integration
  • Memory and context management
  • Guardrails and safety

Tool Integration

  • AWS service integration
  • Custom API calls
  • Database operations
  • File processing
  • External service connections

Architecture Patterns

Simple Agent

User → Bedrock Agent → Lambda Tools → Response

Knowledge-Enhanced Agent

User → Agent → Knowledge Base → RAG → Response

Multi-Service Agent

User → Agent → Orchestrator → Multiple Services → Response

Common Use Cases

  • Customer service chatbots
  • Data analysis agents
  • DevOps automation
  • Content generation
  • Research assistants
  • Workflow automation
  • Document processing

Best Practices

Agent Design

  • Define clear agent purpose
  • Limit tool scope
  • Implement proper error handling
  • Use guardrails for safety
  • Test extensively

Tool Development

  • Keep tools focused
  • Validate inputs
  • Handle errors gracefully
  • Return structured data
  • Document tool schemas

Knowledge Bases

  • Structure data properly
  • Use appropriate chunking
  • Implement metadata filters
  • Test retrieval quality
  • Monitor performance

Development Workflow

  1. Define Agent Purpose - Clear objective and scope
  2. Design Tools - What capabilities needed
  3. Create Knowledge Base - If RAG required
  4. Implement Tools - Lambda functions
  5. Configure Agent - Bedrock console/API
  6. Test Thoroughly - Various scenarios
  7. Deploy - Production deployment
  8. Monitor - CloudWatch metrics

Security

  • IAM role management
  • Encryption at rest/transit
  • Guardrails configuration
  • Input validation
  • Output filtering
  • Audit logging

Cost Optimization

  • Choose appropriate models
  • Implement caching
  • Optimize prompt length
  • Use batch processing
  • Monitor token usage
  • Set usage limits