AI agent pricing models: fixed scope vs. hourly

Fixed scope and time-and-materials (T&M) both exist in AI agent development for the same reason they exist everywhere else — but AI work has one wrinkle that normal software doesn't: you often can't write the acceptance test until you've evaluated the model against real data. That single fact should decide which contract structure you sign.

The decision rule: if you can write the acceptance test before writing the code — the input format is fixed, the success criteria are checkable, and the workflow already exists elsewhere — fixed scope is safe for both sides, and it's what a fixed-fee offer like a readiness assessment is built on. If the acceptance criteria will only be known once you've run the model against your actual data — a new document type, an unfamiliar edge case, a capability nobody's built with this model before — that uncertainty is real, and pricing it as fixed scope just moves the risk onto whoever eats the change orders.

The honest middle path, and the one we use most: a short, capped discovery phase (T&M or a small fixed fee) that ends with a golden dataset and a working definition of 'correct' — the same evaluation suite discipline that makes production AI measurable rather than a matter of opinion — followed by a fixed-scope build phase once that definition exists. The discovery phase is cheap insurance against pricing an unknown as if it were known.

One question surfaces which structure you're actually being offered: ask what happens if the model's real-world accuracy comes in lower than expected. A fixed-scope quote should answer with a defined re-scope trigger. An open T&M quote should answer with a cap. If the answer is silence, the risk hasn't been priced at all — it's just been deferred to the invoice.