AI tools can take a product from an empty repository to a convincing demo quickly. That is useful, but a smooth happy path does not answer a harder question:
What happens when users, permissions, retries, and partial failures do not follow the demo script?
This does not mean AI-generated code is automatically bad. Human-written code can fail in the same ways. The practical problem is that generation often moves faster than the team’s ability to define boundaries, write negative tests, and record why a launch-critical behavior should be trusted.
You do not have to review the whole universe to make progress. Pick one 3–6-screen journey—signup, onboarding, checkout, or create / save / export—and run these nine checks.
Do not start with “review the backend” or “check our Supabase app.” Write one sentence that identifies:
Example:
A standard member completes checkout once and receives exactly one active entitlement tied to the correct account.
Specific wording gives you observable pass and fail conditions.
A test result is difficult to trust if the target keeps changing. Record:
The honest conclusion is then “this behavior was checked under these conditions,” not “the app is safe.”
Write the flow in plain language:
At every step, ask what identity is trusted, what values the caller can choose, and where the rule is actually enforced.
For an authorized test account, safely ask whether changing an object or tenant identifier can expose or modify another user’s data. The UI hiding a button is not enough. The relevant server or database boundary should reject an unauthorized action.
Use sanitized test records only. Do not probe systems you do not own or have permission to test. Retries happen because browsers refresh, networks time out, users double-click, and providers redeliver events.
Repeat the same safe request or test event and check whether it creates:
Record the expected idempotent behavior before running the check.
External events may arrive late or out of order. In a controlled test environment, check whether a delayed cancellation, completion, or retry can overwrite a newer valid state.
If order matters, the system should use durable state rules rather than assuming events always arrive in the demo sequence. Ask what remains if the flow fails after one write but before the next.
For example: The desired outcome may be a transaction, a retryable state, cleanup, or a clearly visible recovery path. What matters is that the half-finished state is intentional and observable.
Every fix or rule needs two checks:
Without both, it is easy to “fix” unauthorized access by breaking access for everyone or to stop duplicates by blocking valid retries.
For each check, record:
If the behavior cannot be reproduced, say so. If access or setup prevents the check, record the blocker. Uncertainty is a result; it should not be turned into a dramatic finding.
If you want a second set of eyes without opening your codebase, the **AI App One-Flow Preflight** reviews one submitted 3–6-screen journey from a sanitized recording of up to 10 minutes or up to eight screenshots.
For **USD 129**, the fixed deliverable is a flow map, up to three evidence-linked hypotheses, ten manual tests with expected results, a Now / Next / Later action plan, and one asynchronous follow-up. Turnaround is within two business days after complete accepted materials.
Applying is free, and a misfit application stops before checkout:
This is an evidence-limited product review, not source-code review, production testing, a security audit, penetration test, certification, defect guarantee, or approval to launch.
Disclosure: This article was prepared with AI assistance and manually reviewed for scope, factual claims, and safety boundaries.