What is agent evaluation (evals)?

Evals are systematic tests that measure whether an AI agent meets its goal with the required quality and reliability before going to production.

Why a demo is not enough

An AI agent can work in a one-off test and fail at the edge cases that appear every day. Evals replace that impression with evidence: a set of representative cases —including the hard ones— against which accuracy, correct use of tools and data, and behavior in unexpected situations are measured in a repeatable way.

Part of operating AI

Evals are not a single exam but a continuous control: they are repeated when the model, the prompts or the data change, and they are part of the LLMOps practices that keep AI in production reliable. They are the filter between prototype and real operation.

How we approach it at Codara

We evaluate each agent before and after deploying it: when we build an Agentic OS on Codara's own platform, we define the tests it has to pass to enter production and leave your team the ability to keep evaluating it.