How to Evaluate AI Agents: Metrics, Test Sets, and Real-World Checks
If you only evaluate agents by reading a few outputs, you will ship surprises.
Agents need evaluation that reflects real work:
- Success rates
- Time saved
- Error patterns
- Safety behavior
This guide focuses on practical evaluation that teams can run every week.
Define success per workflow
Agents do not have a single universal score.
For lead qualification:
- Correct categorization
- Correct routing
- Completeness of CRM fields
For support triage:
- Correct urgency
- Correct team assignment
- Response quality with policy compliance
Pick 3 to 5 metrics that match outcomes.
Build a small test set from reality
Start with 50 to 200 historical examples.
Include:
- Typical cases
- Edge cases
- Messy inputs
- Incomplete information
Label them with the outcomes you want, not just the text.
Measure task success, not eloquence
An agent can sound confident and still be wrong.
Prefer checks like:
- Did it create the record?
- Did it set the correct fields?
- Did it follow the rules?
If the goal is an API update, evaluate the API state after execution.
Add regression tests for tools
Tools are part of the product.
Test:
- Input validation
- Permission checks
- Error handling
- Idempotency
If a tool changes, your agent behavior changes.
Track handoffs and interventions
A good agent reduces human effort.
Track:
- Handoff rate
- Number of human edits
- Time spent reviewing
If humans rewrite everything, you do not have automation yet.
Run evaluations continuously
Make evaluation part of your release cycle.
- Run on every prompt change
- Run on every tool change
- Run on every model upgrade
This is how you ship improvements without breaking workflows.
Closing thought
The winning teams treat agent evaluation like product quality.
You do not need a perfect metric. You need a consistent one that matches the business outcome.