The Core Problem

You shipped an AI agent. It works in demos. Then it runs 10,000 times in production, and you realize you have no idea which runs were good.

This is the agent evaluation problem, and most teams approach it backwards. They reach for model-as-judge ("ask GPT-4 if the output is good") because it feels natural. But this is like using a microscope when you needed a ruler first.

Here's my thesis: a tiered evaluation architecture—deterministic checks first, model-as-judge only where necessary—catches more failures, costs less, and gives you actionable signal faster.

The Three Tiers