The failure mode for AI projects is not the model — it is scope. Teams spend months "exploring" because no one forced a narrow, testable first version. Here is the week that avoids that.
Days 1–2: pick one workflow and one metric
Choose a single workflow a human does today and the one number that would prove the agent helps — time per task, deflection rate, accuracy against a reviewed sample. If you cannot name the metric, you are not ready to build; you are ready to interview users.
Days 3–4: build the thinnest real loop
- Retrieve the context the agent needs (RAG over your real docs, not a toy set)
- Give it the two or three tools it must call, no more
- Wire a human-in-the-loop checkpoint before anything irreversible
- Log every input and output from the first minute
Day 5: evaluate against reality
Run the agent on a set of real cases with known-good answers. This eval set is the asset — it tells you whether the next change helped or hurt, and it is what lets you ship with confidence instead of vibes.
Why a week is enough
A week is not enough to finish an AI product. It is enough to prove whether the idea is worth finishing, which is the only question that matters at the start. Most of our AI engagements begin as exactly this kind of one-week sprint before moving to a monthly build.