Shipping AI agents that actually reach production

Every organisation now has an AI prototype. Very few have an AI product. The gap is rarely the model — it is everything around it: retrieval quality, evaluation harnesses, cost ceilings and a rollback plan.

We start every AI engagement with a measurable task definition. If success cannot be scored automatically on a fixed dataset, the feature is not ready to be built.

From there the work looks like ordinary software engineering: versioned prompts, deterministic tests, structured outputs validated at the boundary, and observability on every call so regressions surface before customers find them.