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.