How to Evaluate AI Agents: Test Sets, Graders, and Regression Checks
Perplexity, Thursday, October 22nd, 2026
Perplexity's guide explains how to evaluate AI agents with trace-based test sets, calibrated graders and regression checks.
Perplexity defines agent evaluation as measuring whether an agent completes tasks correctly and meets requirements, examining both final outputs and intermediate steps.
The guide recommends defining success with domain experts, then building test sets by reviewing real execution traces and grouping errors into failure categories.
It covers repeatable test setups, choosing code-based, LLM-based or human graders, and running regression tests after each change, with LLM graders validated against human judgment.
After deployment, Perplexity advises running calibrated graders on production traces, tracking failure rates and using A/B tests where appropriate.