How to Evaluate AI Agents Once They Reach Production

Evaluation is the single least-taught skill in the modern AI engineering stack, and it is the skill that most separates a working production agent from a demo. In this piece we'll walk through the evaluation methodology we've deployed at every AI client in the last two years, why it works, and where it fails.

Start with a labeled dataset. This is not optional. If you cannot enumerate the inputs your agent is going to see and the outputs you want, you cannot evaluate anything. The dataset does not need to be huge to start — 100-200 labeled examples across the failure modes you care about is more than enough to detect meaningful regressions.

The scoring function is the second decision. LLM-as-judge is a valid strategy for a lot of tasks but you need to validate the judge against human labels before you trust it. We typically get to a judge with ~90% agreement with a human panel before we use it in a CI gate. If you can't get there, fall back to programmatic scoring on the axes you can measure objectively.

The third piece is CI integration. Every prompt change, every model swap, every RAG index rebuild should trigger the eval suite and block the deploy if regressions exceed a threshold you've agreed on. This is the moment your team stops arguing about 'does it feel better?' and starts arguing about specific numbers.

Once you have this stack running, everything downstream becomes easier. You can swap models with confidence. You can A/B test prompts. You can decompose a failing agent into per-step scores and see exactly where it's breaking. And when the vendor you depend on ships a new model at 6PM on a Friday, you can decide within an hour whether to adopt it.

The failure mode we see most often is teams who invest heavily in prompt engineering before investing in evaluation. The prompt engineering is fine work but it is essentially trial and error without a scoring function you trust. Get the scoring function first. Then engineer.