AI Agent Testing
The systematic evaluation of AI agent behavior across functional, safety, and performance dimensions before and after deployment. Unlike traditional software testing (deterministic: same input produces same output), agent testing is probabilistic—the same input may produce different valid outputs. Testing approaches include golden dataset evaluation (test cases with known-correct answers), adversarial testing (attempts to break guardrails), regression testing (ensuring updates don't degrade behavior), and A/B testing (comparing agent versions on live traffic).
Written by Max Zeshut
Founder at Agentmelt · Last updated Sep 9, 2026
Example
Before deploying an updated support agent, the team runs it against 500 historical tickets with verified resolutions, 100 adversarial prompts testing guardrails, and 50 edge cases that previously caused failures. The agent must achieve 90%+ accuracy on the golden dataset and 100% guardrail adherence.
Frequently asked questions
- How many test cases do I need?
- Start with 50-100 golden dataset examples covering your most common query types. Add 20-50 adversarial/safety tests. Add 10-20 edge cases per failure mode you've observed. A mature evaluation suite typically has 500-2,000 test cases. Quality matters more than quantity—well-crafted tests covering distinct scenarios are more valuable than thousands of similar tests.