Closed-loop systems that generate, verify, and correct their own work.
View All Tags
We argued that you can't ship what you can't measure. Here's the uncomfortable half of that sentence: your measurement is a model too, and it is also wrong sometimes.
Everyone who designs an agent loop spends their time on two steps. They tune the generate step, because that's where the intelligence appears to live. They harden the verify step, because that's where the discipline lives. Almost nobody budgets serious design effort for the third step, which is the one that actually determines whether the loop is trustworthy: the stop.
Here is the uncomfortable mechanical fact underneath every loop you have ever built. A turn that produces text with no tool call is the only place a run can stop. That's it. That's the entire exit condition — the absence of a function call. And that single signal conflates three completely different situations, which is why agents routinely "finish" tasks they never did.
This post assumes you already have a loop — generate, verify, correct — and that it has burned you at least once. We're going one level down, into the exit.
Every team that builds seriously with AI walks the same road, usually without noticing. It starts with a chat window and a good feeling, and — if the work survives contact with production — it ends somewhere far more disciplined. This is a field note on that road: the three stages we see teams pass through, what each one is genuinely good at, and the specific failure that forces the jump to the next.
Every team that has tried to ship an AI feature has hit the same wall. The demo works. A stakeholder asks, "is it good?" and the honest answer is a shrug. You feel like it's good. You changed a prompt and it feels a little better, or a little worse, and you cannot say which, because "feels" is the only instrument you have.