Direct answer
AI coding tools are good at making an interface look finished. They are much less careful about what happens when a call fails, so the app often shows success anyway: demo data left in a live path, fixed fallback values presented as real results, or a “saved” message before anything was saved. A crash gets reported and fixed. A fake success gets trusted, and the damage builds until someone checks the data.
Why AI-built code drifts toward fake success
A generated prototype is judged by whether the screen works. Placeholder data, catch-all error handlers and optimistic UI states make the screen work fastest. Those shortcuts are fine in a demo and dangerous in production, and nothing in the generated code marks which is which.
In the production issues we reviewed across about 25 projects in October, silent failure and fake success appeared in nine, tied with authorization gaps as the most common pattern.
Five patterns we fixed
- A demo lookup that invented legal data. In an AI agent that drafted contracts, a demo data function stayed in the live path. Given any address, it made up the seller’s name, the legal description, the HOA status and the year built, then told the assistant it had found the property and not to ask the caller again. Those fields decide which legally required documents are attached. The lookup now invents nothing; missing fields are marked unavailable and raised for review.
- Fallback scores shown as analysis. When an AI call failed, a case-preparation app displayed fixed scores, 78% readiness and 85% case strength, and marked documents “available” that had never been uploaded. Scores are now calculated from real input, and nothing is marked available without a file.
- One result slot shared by every user. A single global “latest result” variable meant two people testing at the same time saw each other’s drafts, including personal data. Results are now keyed by the call that produced them, and a two-user test confirmed nothing crosses over.
- A write-back that hit the wrong record. An update matched records on a field that wasn’t unique, so it could quietly overwrite the wrong customer’s row. It now matches on a full identifier and skips the write when the match is ambiguous.
- A config update that erased an endpoint. A partial update to a voice platform’s configuration wiped a tool’s endpoint URL, so every tool call returned “no result” and the interface sat at “not ready” with no error. A check now runs after every configuration change to confirm each tool still has its endpoint.
How to find fake success in your app
- Search for demo and fallback data. Look for mock functions, sample responses, hard-coded scores and default values reachable from production code paths.
- Read every catch block. An error that is caught and then ignored, or replaced with a default, is a silent failure.
- Check what “success” means. Confirm the UI shows “saved”, “sent” or “ready” only after the write or call actually succeeded, not when the request was sent.
- Test with two users at once. Shared variables and caches that work for one person leak between people.
- Break the dependencies. Turn off the AI provider, the database and each integration in a test environment, and check that the app says so instead of pretending.
Make failure visible, then make it rare
The fix is not more try/catch. It is a rule: never show a result the system didn’t produce. Missing data is marked missing, failed calls surface as failures, and anything a person relies on for a decision carries its source. OWASP’s guidance on logging and error handling points the same way: failures should be recorded and noticed, not absorbed.
Then add the checks that keep it that way: a test for each fallback path, an alert when a fallback is used in production, and a review flag whenever the system had to guess.
Related Zenveus services: AI Prototype Hardening and the 48-Hour Verdict