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README.md

AI Trade Matching System - Documentation

This directory contains comprehensive documentation for the AI Trade Matching System, a multi-agent trade processing platform built on AWS Bedrock AgentCore.

🏗️ Architecture Overview

The system implements a multi-agent architecture using AWS Bedrock AgentCore Runtime with specialized agents for different aspects of trade processing:

  • PDF Adapter Agent - Document processing and text extraction
  • Trade Extraction Agent - Structured data extraction from trade confirmations
  • Trade Matching Agent - Fuzzy matching and reconciliation logic
  • Exception Management Agent - Exception triage and SLA tracking
  • Orchestrator Agent - Workflow coordination and handoff management

Architecture Diagrams

architecture-simplified.mmd

Mermaid diagram source file showing the simplified system architecture and data flow.

To render this diagram:

  1. Copy the content to Mermaid Live Editor
  2. Or use any Mermaid-compatible renderer
  3. Or view it in GitHub (supports Mermaid rendering)

📚 Documentation Structure

Core Documentation

Located in the root directory:

  • README.md - Complete project overview, installation, and usage
  • ARCHITECTURE.md - Detailed system architecture and design decisions
  • CONTRIBUTING.md - Contribution guidelines and development workflow
  • SECURITY.md - Security policy, vulnerability reporting, and best practices
  • CHANGELOG.md - Version history, release notes, and migration guides

Agent Development & Deployment

Following AWS Bedrock AgentCore best practices:

Agent-Specific Documentation

Individual Agent Documentation

Infrastructure & Operations

Infrastructure as Code

Web Portal & API

🚀 Quick Start Guides

For Developers

  1. Agent Development - Best practices for building agents
  2. Local Testing - Test agents locally before deployment
  3. Strands SDK Integration - Framework integration patterns

For Operations

  1. Infrastructure Setup - Deploy infrastructure
  2. Agent Deployment - Deploy agents to AgentCore Runtime
  3. Monitoring Setup - Observability and alerting

For Contributors

  1. Contributing Guide - Development workflow and standards
  2. Security Guidelines - Security requirements and best practices
  3. Testing Guide - Test execution and coverage

🔧 Agent Development Patterns

This system follows AWS Bedrock AgentCore best practices for multi-agent systems:

Agent Communication Patterns

  • Agent-to-Agent (A2A) communication using bedrock-agentcore:InvokeAgentRuntime
  • Orchestrator Pattern for workflow coordination
  • Specialist Pattern for domain-specific processing
  • Pipeline Pattern for sequential processing stages

Code Patterns

# AgentCore Runtime Integration
from bedrock_agentcore.runtime import BedrockAgentCoreApp
from strands import Agent

app = BedrockAgentCoreApp()

@app.entrypoint
def handler(event):
    agent = Agent(tools=[...])
    return agent(event["prompt"])

if __name__ == "__main__":
    app.run()

Deployment Patterns

  • Individual Agent Deployment - Each agent as separate runtime
  • Multi-Agent Coordination - Orchestrator manages handoffs
  • Resource Isolation - Separate IAM roles and permissions
  • Scalable Architecture - Auto-scaling based on demand

📖 External Resources

AWS Bedrock AgentCore

Strands SDK

AWS Services

Frontend Development

🎯 Use Case Specific Guides

Trade Processing Workflow

  1. PDF Processing - Document ingestion and text extraction
  2. Data Extraction - Structured field extraction
  3. Trade Matching - Cross-system reconciliation
  4. Exception Handling - Break management and SLA tracking

Financial Services Compliance

Performance Optimization

🤝 Community & Support

Note: This system is built following AWS Bedrock AgentCore best practices for production-ready multi-agent systems. For the latest AgentCore patterns and updates, refer to the official AgentCore documentation.