Loading…
Loading…
Written by Max Zeshut
Founder at Agentmelt
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).
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.