Building Your Customer Service Agent¶

Let's create a customer service agent that can handle common support scenarios. This will take about 30 minutes.

Quick Implementation¶

```python from azure.identity import DefaultAzureCredential from azure.ai.resources import AIProjectClient from azure.ai.inference import InferenceClient import asyncio import os

class CustomerServiceAgent: def init(self): """Initialize the customer service agent.""" self.client = None self.inference_client = None self.product_docs = { "password_reset": "To reset password: 1) Click 'Forgot Password' 2) Enter email 3) Follow link", "billing": "Billing cycle runs monthly. Payment processed on 1st of each month.", "features": "Product includes: cloud storage, sync, sharing, and admin controls." }

async def initialize(self):
    """Set up the agent with Azure AI."""
    try:
        # Initialize credentials
        credential = DefaultAzureCredential()

        # Create AI Project client
        self.client = AIProjectClient(
            subscription_id=os.getenv("AZURE_SUBSCRIPTION_ID"),
            resource_group=os.getenv("AZURE_RESOURCE_GROUP"),
            credential=credential
        )

        # Create inference client
        self.inference_client = InferenceClient(
            endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
            credential=credential
        )

        return True
    except Exception as e:
        print(f"Initialization error: {str(e)}")
        return False

async def handle_inquiry(self, user_input: str) -> str:
    """Handle a customer inquiry."""
    try:
        # Create context
        context = f"""
        You are a helpful customer service agent. 
        Available product documentation:
        {self.product_docs}

        User inquiry: {user_input}

        Provide a clear, helpful response using the available documentation.
        """

        # Generate response using Azure AI inference
        response = await self.inference_client.chat_completion(
            deployment_name="customer-service-v1",
            messages=[
                {"role": "system", "content": context},
                {"role": "user", "content": user_input}
            ],
            max_tokens=200,
            temperature=0.7
        )

        return response.choices[0].message.content
    except Exception as e:
        return f"I apologize, but I encountered an error: {str(e)}"

Usage example¶

async def main(): agent = CustomerServiceAgent() if await agent.initialize(): response = await agent.handle_inquiry("How do I reset my password?") print(f"Agent: {response}")

if name == "main": asyncio.run(main())

Key Components¶

• Azure AI Project client
• Azure AI Inference client
• Context management
• Error handling
• Response generation
• State tracking

Best Practices¶

• Project organization
• Resource management
• Error handling
• Testing patterns
• Documentation
• Performance optimization
• Security best practices
• Monitoring setup

Common Implementation Patterns¶

1. Conversation Management¶

• Turn handling
• Context tracking
• State management
• History storage
• Response generation
• Error recovery

2. Azure AI SDK Integration¶

• AIProjectClient setup
• InferenceClient configuration
• Error handling with Azure SDK
• Retry policies
• Circuit breakers
• Fallback strategies

3. Operational Implementation¶

• Monitoring setup
• Logging system
• Performance tracking
• Security controls
• Backup procedures
• Recovery processes

Development Best Practices¶

1. Code Organization¶

• Project structure
• Module design
• Interface definitions
• Error handling
• Documentation
• Testing strategy

2. Quality Assurance¶

• Unit testing
• Integration testing
• Performance testing
• Security testing
• Documentation review
• Code review

3. Performance Optimization¶

• Resource management
• Memory optimization
• Response time
• Error handling
• Caching strategy
• Scaling considerations

Interactive Workshop¶

For hands-on practice with implementing AI agents in Azure AI Foundry, try our interactive notebook:

Launch Agent Implementation Workshop

This notebook provides: - Complete customer service agent implementation - Error handling and best practices - Context management examples - Testing and validation - Enhancement exercises

Next: Deploying and Testing