Is your AI-built backend production-ready?
AI tools can stand up a working backend in an afternoon. The endpoints respond, the demo runs, the founder is thrilled. Then real users arrive, and the parts the AI quietly skipped start to show.
That gap is the whole story. Models are trained to produce code that looks right and passes the happy path. They are not trained to care about the boring, expensive parts of running software: who is allowed to do what, what happens when input is malformed, where the secrets live, how the database changes over time. None of that shows up in a demo. All of it shows up in production.
This is not an argument against AI. It is fast, and for a prototype it is genuinely great. It is an argument for knowing what it leaves behind before you put it in front of customers. The scorecard below is the checklist I run when a client hands me an AI-built system to harden.
Tick every statement that is true of your system today. Be honest, no one is watching.
Start ticking the boxes above to see where you stand.
What the scorecard is really checking
The ten checks fall into four buckets. Here is what usually goes wrong in each, and the fix.
Security. AI gives you authentication (a login) but rarely authorization (per-resource permission), so any logged-in user can often reach data that is not theirs. It validates the fields it was told about and trusts the rest. Errors dump stack traces. Keys end up hardcoded. The fix is non-negotiable: enforce access control on every resource, validate every input against a schema, return clean errors, and move secrets into a vault.
Data. Schemas drift because changes are made by hand instead of versioned migrations, so staging and production quietly diverge. Queries that are instant on ten rows fall over on a hundred thousand, usually from missing indexes or N+1 loops. The fix: a migration for every change, and read your slow query log before your users make you.
Reliability. With no tests, every change is a gamble. And when critical writes are not idempotent, a retried request charges a card twice or double-books an order. The fix: cover the paths that lose money or data, and make those writes safe to retry.
Operability. When something breaks, there is no logging or error tracking to tell you what or where, and no rate limiting to stop one bad actor taking the whole thing down. The fix: ship observability and basic abuse protection from day one, not after the first outage.
When to bring in a person
If you scored well, you probably do not need me, and that is a good place to be. If you scored in the middle, you are in the most common and most dangerous spot: the system feels finished, so the gaps get deferred until they cause an incident.
That is the work I do. I take an AI-built or rushed backend, find what will break, and make it solid: the access control, the data layer, the reliability, the observability. Often in days, not months. If that is where you are, tell me what you are building.
Frequently asked
Is AI-generated backend code safe to ship to production?
Sometimes, but rarely without review. AI reliably produces code that works on the happy path and skips the security, data, and reliability concerns that matter under real traffic. Treat it as a strong first draft, not a finished system.
What do AI coding tools usually get wrong in a backend?
Most often: authorization (per-resource permissions, not just login), input validation, error handling, secrets management, database migrations, and query performance. None of these show up in a demo, which is exactly why they get missed.
When should I hire a backend engineer to review AI-built code?
Before real users, payments, or sensitive data are involved, or as soon as you are scaling past a prototype. A short review at that point is far cheaper than the incident it prevents.