Production reality check

Your AI-generated app is not working. Start here.

Do not ask the agent to rewrite everything yet. First identify whether the failure is in the code, configuration, data, build or production environment.

Fix my app →What you can check yourself ↓

What happened?

“Not working” usually hides a smaller, testable failure: the browser cannot reach an API, a server lacks an environment variable, a database schema is behind, or the deployed commit is not the one you tested.

Why this happens

Runtime and build environments use different versions or variables.
A generated change crossed a boundary between frontend, API, authentication and data.
The error was “fixed” by replacing code before anyone captured a reproducible case.

What you can check yourself

Record the exact URL, action, expected result and actual error.
Check browser console, server logs and deployment logs at the same timestamp.
Compare the deployed commit, runtime versions, environment variables and database migrations with the working environment.
Reproduce the failure with the smallest safe input and save it as a regression test.

NOT TECHNICAL

You don't need to read the logs to help fix this.

You do not need to read a stack trace to help fix this. You need to slow down and hand over the right information.

Stop asking the AI to try random fixes — each attempt can bury the real cause under a new one.
Keep the current, broken version instead of overwriting it. It is evidence, not garbage.
Write down what changed right before it broke, and what you were doing when it happened.
Collect screenshots, the exact error text and access to the project for whoever investigates.

Hand that over to Shipvise or a developer and they can take it from there — tracing the failure across code, configuration and environment is the technical part you are not expected to do yourself.

How to test the fix safely

Create a branch, reproduce the bug in staging, add a test that fails for the same reason, then change one cause at a time. Release only after checks pass and a known-good version can be restored.

When you need a developer

Bring in a developer when the problem crosses systems, touches production data, authentication, payments or permissions, or when the proposed fix changes code you cannot explain. Shipvise provides a controlled route through staging, automated checks, AI or senior review, approval, deployment, hosting and rollback — and if you do not have your own DevOps or infrastructure team, you do not need to build one just to fix one application.

FAQ

Questions people actually ask

Should I ask the AI to regenerate the broken feature?

Usually not first. Capture the failure and inspect the smallest responsible change. Regeneration can remove the symptom while introducing unrelated changes.

Why does the app work locally but fail online?

Production differs in domains, HTTPS, secrets, runtime versions, permissions, network access and data. Compare those differences before changing business logic.

Can Shipvise fix every broken app automatically?

No. Shipvise is a controlled workflow for reviewing, testing, staging, approving, deploying and operating AI-built software; difficult faults may still require senior engineering judgment.

What should I do first when an AI-built app suddenly stops working?

Stop making changes and write down exactly what's broken: the URL, the action you took, and what happened instead of what you expected. Check whether it fails for everyone or just for you, and look at the error message before asking the AI for another fix.

Do I need to understand code or read logs to get this fixed?

No. You can hand off the broken version, a description of what changed right before it broke, and any error text to Shipvise or a developer. Reading logs and tracing the failure through code and configuration is the technical part someone else can do.

How do I find the last version of my app that actually worked?

If you use version control, it's the last commit before the failing change — compare it against the current state instead of guessing. If you don't use Git, check your AI tool's version history or previous exports and deployments; keeping a working copy before every big change makes this much easier next time.