Agent Evaluation Methods

Agent evaluation requires different approaches than traditional software. Agents are non-deterministic, may take different valid paths, and lack single correct answers.

Key Finding: 95% Performance Drivers

Research on BrowseComp found three factors explain 95% of variance:

Implications: Model upgrades beat token increases. Multi-agent architectures validate.

Multi-Dimensional Rubric

LLM-as-Judge

evaluation_prompt = """
Task: {task_description}
Agent Output: {agent_output}
Ground Truth: {ground_truth}

Evaluate on:
1. Factual accuracy (0-1)
2. Completeness (0-1)
3. Citation accuracy (0-1)
4. Tool efficiency (0-1)

Provide scores and reasoning.
"""

Test Set Design

test_set = [
    {"name": "simple", "complexity": "simple",
     "input": "What is capital of France?"},
    {"name": "medium", "complexity": "medium",
     "input": "Compare Apple and Microsoft revenue"},
    {"name": "complex", "complexity": "complex",
     "input": "Analyze Q1-Q4 sales trends"},
    {"name": "very_complex", "complexity": "very_complex",
     "input": "Research AI tech, evaluate impact, recommend strategy"}
]

Evaluation Pipeline

def evaluate_agent(agent, test_set):
    results = []
    for test in test_set:
        output = agent.run(test["input"])
        scores = llm_judge(output, test)
        results.append({
            "test": test["name"],
            "scores": scores,
            "passed": scores["overall"] >= 0.7
        })
    return results

Complexity Stratification

Context Engineering Evaluation

Test context strategies systematically:

1. Run agents with different strategies on same tests
2. Compare quality scores, token usage, efficiency
3. Identify degradation cliffs at different context sizes

Continuous Evaluation

• Run evaluations on all agent changes
• Track metrics over time
• Set alerts for quality drops
• Sample production interactions

Avoiding Pitfalls

Best Practices

1. Use multi-dimensional rubrics
2. Evaluate outcomes, not specific paths
3. Cover complexity levels
4. Test with realistic context sizes
5. Run evaluations continuously
6. Supplement LLM with human review
7. Track metrics for trends
8. Set clear pass/fail thresholds