Agent Evaluation & Testing
Metrics, testing pipelines, scorecards
📖 Analogy
Evaluating an AI agent is like a performance review for an employee. You don't just check if they showed up — you review their decision-making process, how they handled edge cases, whether they used the right tools, and if the final result met quality standards.
Why Agent Evaluation is Harder
Non-determinism
The same input can produce different outputs. Traditional unit tests with exact matching don't work — you need semantic evaluation and statistical testing.
Multi-step reasoning
Agents take multiple steps. An error in step 2 can compound into step 5. You need to evaluate not just the final answer but the entire trajectory.
Tool interactions
Agents call external APIs, databases, and tools. Testing requires mocking external services and validating that the right tool was called with correct parameters.
Error compounding
If each step has 90% accuracy, a 5-step agent has only 59% accuracy (0.9^5). Small per-step improvements have outsized impact on end-to-end quality.
4 Levels of Evaluation
Component Testing
Test individual pieces: prompt templates, tool parsers, output formatters. Fast, cheap, catches 60% of bugs.
Trajectory Evaluation
Compare the agent's reasoning path against golden trajectories. Did it choose the right tools in the right order?
End-to-End Testing
Run the full agent on real-world scenarios. Measure task completion, cost, and latency. Slowest but most realistic.
Human Evaluation
Expert review of agent outputs on a sample. Catches subtle quality issues that automated metrics miss.
Key Metrics
⚠️ Common Pitfall
Demo-driven development: your agent works perfectly on 3 hand-picked examples, then fails on 100 real ones. Always test with a diverse golden dataset of 50+ cases covering edge cases, not just the happy path.
Testing Patterns
Golden Datasets
Curate 50-200 test cases with expected outputs. Include happy paths, edge cases, and failure modes. Version them like code.
Regression Suites
Every bug becomes a test case. Run the suite on every prompt change or model update. Catch regressions before they reach production.
Sandbox Environments
Mock external APIs and databases. Test tool interactions without side effects. Use record-replay for deterministic tests.
Adversarial Testing
Throw edge cases, malformed inputs, and prompt injections at your agent. Test recovery behavior when tools fail or return unexpected data.
💡 Fun Fact
Anthropic's internal agent testing revealed that 73% of agent failures stem from incorrect tool parameter formatting — not reasoning errors. Simple input validation on tool calls can dramatically improve agent reliability.
What's the weather in Tokyo tomorrow?
I need to check the weather for Tokyo. I'll use the weather API tool with the city name and tomorrow's date.
Lesson Quiz
1.Why is evaluating AI agents harder than evaluating standard LLM outputs?
Create a free account to solve challenges
3 AI-verified challenges for this lesson
Related recipes
This lesson is part of a structured LLM course.
My Learning Path