Agent Benchmarking Evaluation

Agent Benchmarking and Evaluation

Systematically evaluating AI agent performance — from task completion metrics through cost analysis, latency measurement, and benchmarking against baselines.

When to Use

• Comparing different agent architectures or models
• Measuring if an agent improvement actually helps
• Evaluating agent performance before production deployment
• Tracking agent performance regression over time
• Publishing agent benchmark results

Evaluation Framework

AGENT_EVAL_FRAMEWORK = {
    'task_success': '% of tasks completed successfully',
    'cost_per_task': 'Average cost (tokens + API calls) per task',
    'latency_p50_p95': 'Median and P95 completion time',
    'error_rate': 'Rate of errors, hallucinations, or unsafe outputs',
    'tool_accuracy': 'Correct tool selection and parameter usage rate',
}

class AgentBenchmark:
    """Benchmark agent performance across tasks."""
    
    def __init__(self, test_suite: List[Dict]):
        self.tests = test_suite  # [{task, expected, tools_allowed}, ...]
    
    def evaluate(self, agent) -> Dict:
        results = {'success': [], 'latency': [], 'cost': [], 'errors': []}
        
        for test in self.tests:
            start = time.time()
            try:
                response = agent.run(test['task'])
                latency = time.time() - start
                
                success = self._check_success(response, test['expected'])
                results['success'].append(success)
                results['latency'].append(latency)
                results['cost'].append(agent.get_last_run_cost())
            except Exception as e:
                results['errors'].append(str(e))
                results['success'].append(False)
        
        return {
            'success_rate': round(sum(results['success']) / len(self.tests) * 100, 1),
            'avg_latency': round(np.mean(results['latency']), 2),
            'p95_latency': round(np.percentile(results['latency'], 95), 2),
            'avg_cost': round(np.mean(results['cost']), 4),
            'error_rate': round(len(results['errors']) / len(self.tests) * 100, 1),
        }

Common Pitfalls

1. Task leakage — evaluation tasks seen during development overstate performance
2. Cost not tracked — an agent that works but costs $100/task isn't useful
3. Only success rate — latency and cost matter just as much; track all three
4. No baseline — agent evaluation without a baseline (random, simple prompt) is meaningless
5. Overspecified tasks — tests too close to training data don't measure true capability

Verification Checklist

• Test suite represents real-world tasks (not toy examples)
• Baseline established (human performance or simple prompt)
• Metrics cover success, latency, cost, and safety
• Multiple runs per task to account for nondeterminism
• Test set versioned and not used in development
• Results reported with confidence intervals