Ask a team how they will know their agent is working, and the quiet ones are the ones in trouble. "It seems good in the demo" is not a measurement. It is a vibe. And a vibe does not survive a model upgrade, a prompt edit, or the long tail of inputs a real workflow throws at you.
So we build the eval suite first — before the agent. We collect the inputs that matter, define what a correct output looks like, and write the checks that score it. The suite becomes the spec: it tells us when we have shipped, it catches regressions when a vendor changes the model under us, and it is the layer that keeps a production agent stack honest.
An eval suite is also the cheapest insurance you can buy in AI. It turns "trust me" into "here is the score," and that single shift is what lets a non-deterministic system live in production at all — which is why it is the first thing we build in an AI Transformation Sprint, before a line of the agent itself.
