Evaluating Agent Performance

Agents need evaluation across multiple dimensions:

Task completion: - Did the agent complete the task? - Did it achieve the user's actual goal? - Did it stop appropriately (not too early, not too late)?

Quality: - How good was the output? - Was the reasoning sound? - Were intermediate steps correct?

Efficiency: - How many steps did it take? - How many tokens were used? - How much time elapsed? - What was the cost?

Safety: - Did it stay within bounds? - Were there any harmful outputs? - Did it require human intervention?

User experience: - Was the interaction smooth? - Did the user understand what was happening? - Would they use it again?

Create datasets to systematically evaluate agents:

Dataset components: - Input: User request or task description - Expected output: Correct answer or completion criteria - Context: Any additional information needed - Difficulty: Easy/medium/hard classification

Building evaluation sets:

From production logs: - Sample real user requests - Annotate with correct answers - Include edge cases that occurred

Synthetic generation: - Create variations of known patterns - Generate edge cases systematically - Test boundary conditions

Adversarial examples: - Prompts designed to confuse - Malicious inputs - Ambiguous requests

Coverage requirements: - All major task types - Various input lengths/complexities - Different user intents - Error recovery scenarios

Scale evaluation with automated methods:

Exact match metrics: - Did the agent produce the exact right answer? - Good for factual tasks with clear answers - Limited for open-ended tasks

LLM-as-judge: Use a separate LLM to evaluate outputs: - Rate quality on defined criteria - Compare to reference answers - Check for specific attributes - Correlates reasonably with human judgment

Component evaluation: Test individual pieces: - Tool selection accuracy - Parameter extraction correctness - Reasoning step validity - State transitions correctness

Trace evaluation: Evaluate the full execution trace: - Were all steps necessary? - Was the order logical? - Were errors handled well?

Regression testing: - Run benchmark suite on every change - Catch degradations early - Track metrics over time

Human judgment is essential for quality assessment:

When to use human evaluation: - Quality matters more than speed - Output is subjective or creative - Validating automated metrics - High-stakes decisions

Human evaluation methods:

Direct rating: Rate outputs on defined criteria (1-5 scale): - Correctness - Helpfulness - Safety - Naturalness

Pairwise comparison: - Compare two outputs, pick better one - More reliable than absolute ratings - Good for comparing versions

Task completion study: - Give evaluators the task and agent output - Can they complete their actual goal? - Measures real utility

Error analysis: - Review failed cases in detail - Categorize failure modes - Inform improvement priorities

Ongoing evaluation in production:

Key metrics to track:

Success metrics: - Task completion rate - Successful tool calls / total attempts - User satisfaction (thumbs up/down) - Escalation rate

Efficiency metrics: - Steps per task - Tokens per task - Cost per task - Latency distributions

Safety metrics: - Guardrail trigger rate - Human override rate - Error rate by type - Out-of-scope request rate

Monitoring setup: - Real-time dashboards - Alerting on anomalies - Trend tracking over time - Segmentation by task type/user

Continuous improvement: - Review samples regularly - Investigate failures - Update benchmarks with new patterns - A/B test changes before full rollout

Production is the ultimate test. Benchmarks tell you if changes are safe to deploy; production tells you if they actually work.