When an AI-assisted shopping flow feels unclear, “it did not work” is rarely enough for someone else to reproduce the problem.
Disclosure: this is a WebAZ project note. The apron scenario below is an illustrative template, not a customer report or a claim of an observed product defect.
A useful report contains four things:
- The exact buyer goal: for example, “find an oil-resistant apron for a small restaurant kitchen.”
- The visible facts: title, selected option, displayed price range, destination or other conditions.
- The question left unresolved: “does this range mean one item or a multipack?”
- The expected clarification: “show option name and quantity beside the price range.”
That is better than a screenshot alone. It lets the product team replay the decision point without asking for a customer identity, payment information or a real order.
WebAZ exposes an anonymous suggestion entry and a public task-suggestion route for this kind of input. A report is not a promise that a change will be accepted or formally attributed. It is simply the fastest way to turn a vague friction point into something another person can test.
For agent-commerce systems, that distinction matters: a model can summarize a problem, but the human report needs to preserve the facts and unknowns that made the recommendation ambiguous.
Public discovery: https://webaz.xyz/#discover
Suggestion entry: https://webaz.xyz/#welcome
Writing assistance: prepared with AI assistance; the public feedback entry points were checked on October 1, 2026.
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