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InvoiceKit made every displayed total a testable product claim. Its invoice detail brings the record identity, customer context, line items, and completed total into the same review surface, so the calculation does not hide behind a dashboard number. That was an important delivery decision for a data-heavy app. When accuracy matters, place the inputs close enough to the output that a reviewer can reconcile them.
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While building InvoiceKit, I stopped reviewing business features as a list of screens and used five questions: Who owns the record? What can change? Where is it summarized? Which state leaves the app? How is persistence verified? For an invoice, those questions connect owner-scoped Firebase access, revisions and statuses, dashboard and detail views, and PDF/CSV sharing. If any answer is vague, the estimate still contains hidden work.
Your AI agent just did 9 things: pulled 412 orders, matched them to Stripe, caught a duplicate £248 charge, drafted a ledger entry, opened a ticket. Whole run cost $0.011.
Now the real design question — what does the user actually SEE?
A — The transcript. A paragraph of text and a collapsed "Used 3 tools".
B — The run canvas. Nine colour-coded rows: what ran, what it cost, what got flagged, and one step that stops and waits for you before money moves.
Same agent. Same run. Two completely different products.
Vote A or B — and tell me why. I build the B version for a living.
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Wow! So beautiful and professional work!👌