In this article, you will learn how to evaluate AI agents rigorously by examining their full execution process rather than only their final outputs.
Topics we will cover include:
Why agent evaluation differs from traditional language model evaluation, and where agents fail across the reasoning and action layers.
How to grade agents with deterministic code-based checks and model-based judges, matched to the type of agent you are building.
How to account for non-determinism using metrics like pass@k and pass^k, and how to extend evaluation from development into production monitoring.
