How to Scope an AI Agent Proof of Concept

An AI agent proof of concept should answer a question that affects a business decision. “Can we build an agent?” is usually too broad. “Can this system prepare a usable order draft from our actual incoming requests?” is specific enough to investigate.

The most useful pilot scope connects that question to representative inputs, a review method and a decision at the end. It should make uncertainty smaller before the business commits to a wider implementation.

Write down the decision the pilot supports

Start by identifying what you would do differently if the pilot succeeds. Would you fund an integration, expand the task to another team or stop spending time on a manual step? If there is no next decision, the demonstration may become an interesting side project.

The sponsor and the operational owner should agree on the question. A sponsor may care about handling capacity while the person doing the work cares about correction effort. Both can matter, but the pilot needs to measure them explicitly.

I would also define what a negative result means. A pilot can be useful if it shows that the source material is inadequate or that the integration cost outweighs the current opportunity.

Keep the first scope narrow enough to explain

Choose one process, a limited group of users and a defined set of inputs. For an order-intake pilot, this might mean one document type and a draft output reviewed by staff. It does not need to include every supplier format, automatic submission and a customer portal at the same time.

Write exclusions as part of the scope. A pilot may exclude handwritten documents, unsupported languages or changes to the destination system. The excluded work remains visible, so nobody mistakes a bounded result for a complete production capability.

Resist expanding the pilot whenever a new idea appears. Record those ideas for the next decision. Otherwise you lose the original comparison and turn discovery into an open-ended build.

Use examples that represent the work

A small evaluation set should include ordinary cases, confusing cases and inputs the system should decline or send for review. Selecting only clean examples can produce a successful demonstration that says little about daily use.

Have the operational owner define an acceptable result before reviewing model output. If the criteria change after every example, it becomes difficult to tell whether the system improved or the goal moved.

Keep some cases out of the development loop and use them for the final review. They provide a useful check on whether repeated tuning merely accommodated the examples everyone has already seen.

Specify the deliverables

A practical pilot package might include a working workflow, the agreed evaluation cases, a result summary and a list of unresolved dependencies. It should identify which elements can be reused and which need further engineering before release.

The result summary should distinguish system accuracy, human correction effort and integration feasibility. A good draft that cannot reach the destination system is a different outcome from a complete workflow that needs too much review.

Agree who owns the code, configuration and evaluation material. A pilot should leave you with an understandable record of what was learned, even if you decide not to continue.

End with a release decision

The final discussion should choose a path: proceed, revise the scope, resolve a dependency or stop. Continuing the experiment without a decision can consume budget while leaving the original question unanswered.

If the pilot moves forward, production work may still include access management, monitoring, operational training and release preparation. Name those items in the next scope instead of assuming that a successful demonstration already includes them.

I approach a proof of concept as a bounded engineering investigation. Its value is the evidence it creates for your next investment of time and effort.

Updated 30 September 2026.