Topic 2: Evaluation Frameworks

What You’ll Learn

This topic teaches you how to:

• Design systematic evaluation frameworks
• Create test cases for agents
• Structure evaluation pipelines
• Compare different evaluation approaches
• Build reusable evaluation infrastructure

Why We Need This

Business Need

• Quality assurance: Ensure agents work correctly before deployment
• Risk mitigation: Catch issues before they reach users
• Performance validation: Verify agents meet requirements
• Cost control: Avoid deploying broken agents

Technical Need

• Systematic testing: Need structured approach to testing
• Reproducibility: Same tests should give same results
• Scalability: Test many agents efficiently
• Comparability: Compare different agents fairly

Industry Use Cases

1. Pre-Deployment Testing

Company: All companies deploying agents Use Case:

• Test agents before production
• Catch bugs early
• Validate functionality

2. A/B Testing Agents

Company: Tech companies Use Case:

• Compare agent versions
• Measure improvements
• Make data-driven decisions

3. Continuous Evaluation

Company: ML platforms Use Case:

• Monitor agent performance
• Detect regressions
• Track improvements

Industry-Standard Boilerplate Code

Basic Evaluation Framework

"""
Evaluation Framework
Industry standard pattern for evaluating agents
"""
from typing import List, Dict, Any, Callable
from dataclasses import dataclass
from enum import Enum

class EvaluationResult(Enum):
    PASS = "pass"
    FAIL = "fail"
    PARTIAL = "partial"

@dataclass
class TestCase:
    """Represents a test case"""
    name: str
    description: str
    input: Any
    expected_output: Any
    evaluation_criteria: Callable

@dataclass
class EvaluationReport:
    """Results of evaluation"""
    test_case: TestCase
    result: EvaluationResult
    actual_output: Any
    score: float
    details: Dict[str, Any]

class Evaluator:
    """
    Base evaluator class
    Industry standard: Test case → Evaluation → Report
    """
    
    def __init__(self, agent: Any):
        self.agent = agent
    
    def evaluate(self, test_case: TestCase) -> EvaluationReport:
        """Evaluate agent on a test case"""
        # Run agent
        actual_output = self.agent.run(test_case.input)
        
        # Evaluate using criteria
        score, details = test_case.evaluation_criteria(
            expected=test_case.expected_output,
            actual=actual_output
        )
        
        # Determine result
        if score >= 1.0:
            result = EvaluationResult.PASS
        elif score >= 0.5:
            result = EvaluationResult.PARTIAL
        else:
            result = EvaluationResult.FAIL
        
        return EvaluationReport(
            test_case=test_case,
            result=result,
            actual_output=actual_output,
            score=score,
            details=details
        )
    
    def evaluate_batch(self, test_cases: List[TestCase]) -> List[EvaluationReport]:
        """Evaluate multiple test cases"""
        return [self.evaluate(tc) for tc in test_cases]

Exercises

1. Create test cases for a simple agent
2. Implement evaluation criteria
3. Build evaluation pipeline
4. Compare different agents

Next Steps

• Topic 3: Learn about metrics and benchmarks
• Topic 4: Evaluate tool usage