#AI Agent Development

This document outlines the AI agent architecture, implementation details, and testing strategy employed in the 5CRSE project.

#Architecture Overview

The AI agent system is built with a flexible, modular architecture that supports multiple agent types and tool functions. The key components are:

1. AI Service Layer: Provider-agnostic AI model interface
2. Tool Registry: Central registry for all AI agent tools
3. Tool Adapter: Adapts functional tools to agent-compatible tools
4. Agent Types: Customer Service and Business Manager agents
5. Tools Implementation: Modular, function-based tool implementations
6. API Endpoints: RESTful endpoints for AI interaction and tool execution

#Key Components

#1. AI Service

The AIService class provides a provider-agnostic interface for AI models, allowing seamless switching between providers like OpenAI, Anthropic, etc. It handles:

• Text completion
• Streaming responses
• Embedding generation
• Interaction storage

const aiService = new AIService();
const response = await aiService.getResponse("What events are happening this weekend?", {
  model: "gpt-4-turbo",
  userId: "user123"
});

#2. Tool Registry

The ToolRegistry is a central repository for all AI agent tools. It allows tools to be registered, retrieved, and executed in a type-safe manner.

// Registering a tool
toolRegistry.registerFunctionalTool(
  'customer_service',
  'search_events',
  'search_events',
  'Search for events based on criteria',
  { /* parameters schema */ },
  searchEvents
);

// Getting a tool function
const searchFunction = getToolFunction('search_events', 'customer_service');

#3. Tool Adapter

The ToolAdapter provides an adapter pattern to convert functional tools into Hume-compatible tools, allowing for a cleaner, more modular implementation.

const humeTool = createToolAdapter(
  'search_events',
  'search_events',
  'Search for events',
  { /* parameters schema */ },
  searchEventsFn
);

#4. Agent Tools

Tools are implemented as modular functions with clear input/output contracts. Each tool:

• Validates input using Zod
• Interacts with the database via Payload
• Returns structured data
• Handles errors gracefully

Example tool implementation:

export const searchEvents = async (args: SearchEventsArgs, payload: Payload) => {
  try {
    // Validate args using Zod
    const validatedArgs = SearchEventsArgsSchema.parse(args);
    
    // Construct query
    const query = { /* ... */ };
    
    // Execute query
    const result = await payload.find({
      collection: 'events',
      where: query,
      /* ... */
    });
    
    return {
      events: result.docs,
      totalCount: result.totalDocs,
      /* ... */
    };
  } catch (error) {
    throw new Error(`Failed to search events: ${error.message}`);
  }
};

#Testing Strategy

The AI agent system follows a test-driven development approach with comprehensive test coverage:

1. Unit Tests: Test individual components in isolation

   • AI Service
   • Tool Adapter
   • Tool Registry
   • Individual Tools

2. Integration Tests: Test components working together

   • API Endpoints
   • Tool Execution
   • Database Interactions

3. End-to-End Tests: Test complete user flows

   • AI Assistant Interaction
   • Tool Execution Chains

#Available Agent Types

#Customer Service Agent

The Customer Service Agent assists users with event discovery, booking, and support.

Available Tools:

• search_events: Search for events based on criteria
• (Additional tools to be implemented)

#Business Manager Agent

The Business Manager Agent provides business insights, analytics, and administrative functions.

Available Tools:

• get_business_metrics: Retrieve business performance metrics
• manage_content: Create or update content in the CMS
• send_communication: Send communications to users
• generate_report: Generate business reports
• (Additional tools to be implemented)

#API Endpoints

#1. AI Interaction

POST /api/ai/interact

Request Body:

{
  "message": "I'm looking for concerts in New York",
  "agentType": "customer_service",
  "sessionId": "optional-session-id"
}

#2. Tool Execution

POST /api/ai/execute-tool

Request Body:

{
  "toolId": "search_events",
  "agentType": "customer_service",
  "args": {
    "keyword": "concert",
    "location": "New York"
  },
  "sessionId": "optional-session-id"
}

#Future Enhancements

1. Advanced Context Management: Enhance context tracking and management
2. Multi-turn Tool Execution: Support for multi-turn, multi-tool interactions
3. Memory and Personalization: Add long-term memory and personalization
4. Voice Integration: Deeper integration with voice processing
5. Agent Customization: Allow admin-defined agent personalities and tools

#Troubleshooting

Common issues and solutions:

1. Tool Execution Errors: Check the tool implementation and input validation
2. AI Model Errors: Verify API keys and model availability
3. Database Errors: Ensure the database schema matches tool expectations
4. Performance Issues: Consider implementing caching for frequently used tools