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Subtitle:

Methodologies and tools for evaluating AI agent systems' reliability and performance

Core Idea:

AI agent testing involves specialized techniques to validate agent behavior, accuracy, and safety through unit tests, mocked LLM responses, simulated environments, and end-to-end evaluation.

Key Principles:

1. Mock LLM Testing:
   • Uses deterministic, predefined LLM responses to test agent logic
   • Enables consistent, reproducible testing without API costs
2. Tool Validation:
   • Tests correct function calling, parameter handling, and error management
   • Ensures tools operate correctly in isolation before agent integration
3. Scenario-based Testing:
   • Creates multi-turn conversations that test complex agent behaviors
   • Validates response appropriateness across varying inputs

Why It Matters:

• Reliability Assurance:
  • Identifies failure modes before production deployment
• Cost Efficiency:
  • Reduces expensive LLM API calls during development and testing
• Safety Verification:
  • Confirms guardrails and safety mechanisms function properly

How to Implement:

1. Create Mock LLM Responses:
   • Develop predefined responses for test queries
   • Include variations to test edge cases
2. Set Up Test Harnesses:
   • Build automated test suites that evaluate agent behaviors
   • Incorporate both unit and integration tests
3. Implement Evaluation Metrics:
   • Define success criteria for agent performance
   • Create scoring mechanisms for response quality

Example:

• Scenario:
  • Testing a customer service agent that can access order information and issue refunds
• Application:

# Using Pynatic AI for agent testing
def test_refund_request():
    # Mock LLM that will always extract order_id=12345
    mock_llm = MockLLM(responses={
        "extract_order_id": {"order_id": "12345"}
    })
    
    # Mock order tool that returns predefined data
    mock_order_tool = MockTool(
        name="get_order",
        responses={"12345": {"amount": 50.00, "status": "delivered"}}
    )
    
    # Mock refund tool to verify it gets called with correct parameters
    mock_refund_tool = MockTool(
        name="issue_refund",
        assert_called_with={"order_id": "12345", "amount": 50.00}
    )
    
    # Create agent with mock components
    agent = Agent(
        llm=mock_llm,
        tools=[mock_order_tool, mock_refund_tool]
    )
    
    # Run test
    result = agent.run("I want a refund for my order #12345")
    
    # Assertions
    assert mock_refund_tool.was_called
    assert "refund has been issued" in result.lower()

• Result:
  • Confirms the agent correctly extracts order ID, retrieves order information, and issues the appropriate refund amount

Connections:

• Related Concepts:
  • Agent Framework Comparison: How different frameworks support testing capabilities
  • Guardrails for AI Agents: Testing safety mechanisms in agent systems
• Broader Concepts:
  • Software Testing Methodologies: Traditional testing approaches adapted for AI
  • LLM Evaluation Techniques: Specialized methods for evaluating language model outputs

References:

1. Primary Source:
   • Pynatic AI documentation on agent testing
2. Additional Resources:
   • Research papers on LLM-based system evaluation
   • Best practices for AI system testing from major AI labs

Tags:

#ai #agents #testing #mocking #evaluation #quality-assurance #reliability

Connections:

Sources:

• From: Cole Medin - El nuevo SDK de agentes de OpenAI (curso intensivo)