A demonstration is not an operating record
The public AI guidance frames agentic work as source-grounded, reviewable operating capability. A single impressive output cannot show how a workflow behaves with ordinary, incomplete, ambiguous, or out-of-bound requests.
An evaluation loop makes quality discussable by defining the task, approved sources, expected answer shape, review criteria, and the reason a human accepts, changes, or rejects an output.
Build a small, real evaluation set
Start with a handful of representative inputs drawn from the workflow: a normal request, a missing-context request, an exception, and a case that must be handed to a person. Pair each with source references and a review instruction.
The goal is not to manufacture a headline score. It is to create a repeatable comparison between the output and the rules the business has actually approved.
Feed corrections back into the design
Keep the task instruction, source set, output, reviewer decision, and correction reason together. This provides a learning asset for the workflow and shows when the source layer, prompt, or boundary needs revision.
Scale only when the evaluation route and review ownership remain legible to the people responsible for the work.