Production Checklist for Codex-Generated Apps

Codex makes it easy to get from idea to a working app. "Working" on your laptop and "ready for real users" are separated by dozens of small decisions the agent either made silently or skipped. This checklist is grouped so you can split it across people, and every item is phrased so the answer is yes or no.

How to use this checklist

Treat agent output the way you treat a pull request from a capable contractor you have not worked with before: assume competence, verify everything that matters. Three rules make the list useful:

• Evidence over assertions. "Codex said tests pass" is not evidence. A green CI run, a screenshot or a log line is.
• One owner per section. Security, UX and operations need different eyes.
• Ask the agent to audit, not to certify. Codex is useful for finding candidate problems ("list every endpoint and the authorization check it performs"), but a human decides whether the answer is complete.

Security and secrets

1. No API keys, tokens or passwords in the repository history, client bundle or mobile app binary.
2. Every server endpoint checks authentication and authorization (user A cannot read user B's data by changing an ID).
3. Database access rules (for example row-level security) are enabled and tested with a second account.
4. Input validation happens on the server, not only in the form.
5. Dependencies are pinned and scanned; no packages the agent added that nobody recognizes.
6. Rate limiting exists on login, signup, password reset and any endpoint that calls a paid AI API.
7. Error messages shown to users do not leak stack traces or SQL.

The broader threat model, including prompt injection against the agent itself, is covered in security risks of agentic coding.

Data and privacy

8. You can list every category of personal data the app stores and why.
9. Personal data sent to third-party AI APIs is minimized or anonymized first.
10. Users can delete their account and data from inside the app if they can create one there.
11. Backups exist and you have restored one at least once.
12. Migrations are versioned and reversible; no schema changes applied by hand in production.
13. Privacy policy and cookie or consent handling match what the code actually does.

If prompts or logs contain customer text, a local anonymization layer such as Textwash Pro can strip personal data before it reaches an external model.

Tests and reliability

14. Core user journeys (signup, main action, payment if any) have automated end-to-end tests.
15. Tests were reviewed by a human: agents sometimes write tests that assert whatever the code currently does, bugs included.
16. CI runs tests on every pull request and blocks merges on failure.
17. The app handles slow and failed network calls with timeouts, retries and visible error states.
18. Load has been tested at least at a few times your expected launch traffic.
19. Third-party outages (AI provider, payment, email) degrade gracefully instead of crashing the app.

For patterns that catch the specific weaknesses of generated tests, see testing strategies for AI-generated code.

User experience and accessibility

This is the area agents check least, because they rarely look at the rendered result. It is also what users notice first.

20. Every screen has defined loading, empty and error states.
21. The first-time user flow works without explanation from the founder.
22. Copy is consistent: one name per concept, no placeholder text, no leftover "Lorem ipsum" or "TODO".
23. Forms show helpful validation messages next to the field.
24. Layouts work on small phones, large tablets, and with large system font sizes.
25. Color contrast and keyboard or screen-reader navigation meet basic accessibility expectations.
26. Destructive actions ask for confirmation and can ideally be undone.

An outside perspective is valuable here because the people who built the app already know how it is supposed to work. UXreviewer.io delivers a UX audit with commented screenshots, mockups and concrete improvement ideas within 10 business days, which fits well into the final weeks before launch. Our article on UX audits for AI-built apps explains what such a review typically uncovers.

Operations and observability

27. Errors are reported to a monitoring tool with enough context to reproduce them.
28. Logs exclude passwords, tokens and unnecessary personal data.
29. Uptime checks and alerts reach a person who will actually respond.
30. Environment variables are documented; staging and production are separated.
31. A rollback is one command or one click, and someone has practiced it.
32. Costs of AI API usage are capped or alerted per day.
33. Somebody other than the agent can explain how deployment works.

Legal, store and launch basics

34. Imprint, terms and privacy policy are reachable from the app (an imprint is mandatory for many businesses in Germany).
35. Licenses of all dependencies allow your use.
36. If the app uses AI features visible to users, you know which transparency obligations may apply under the EU AI Act as it phases in.
37. For mobile: privacy labels and the Google Play data safety form match the data actually collected.
38. Support contact and a way to report problems exist.
39. Analytics events for the key funnel are firing correctly.
40. A launch-day owner is named and available.
41. You have decided which known issues you are consciously shipping with.

When the checklist reveals too much

If more than a handful of items in the security or data sections fail, the codebase usually needs structural work rather than patches. That is a normal outcome for a fast prototype, not a failure. The hardening playbook for vibe-coded prototypes describes how to sequence that work, and the team at OnlineAppCreator takes on production hardening when you need extra hands.