ChecklistMarch 26, 2026

What to check before trusting an AI-made business tool

1.7x

more issues in AI-generated code vs human-written

TL;DR

A business tool should be checked for the user flow, data handling, unnecessary complexity, and whether the result truly matches the original request.

DriftLess writes for business owners first. Some posts include advanced details for technical handoff, but the practical takeaway is always in plain English.

I shipped a five-endpoint API in one afternoon. Authentication, CRUD operations, file uploads, webhooks, and a health check. The code worked. The tests passed. The linter was clean. Then I actually read it. What I found was not broken code — it was over-engineered code. Every endpoint had abstractions nobody asked for, error handling for scenarios that could not happen, and configuration for features that did not exist.

Why does AI over-abstract by default?

The pattern is consistent: ask for a simple CRUD endpoint and you get a full repository pattern with interfaces, factories, error types, and middleware. Ask for a login page and you get OAuth, session management, CSRF protection, rate limiting, and audit logging. The AI is not wrong — these are real concerns. But you did not ask for them, you do not need them yet, and now you maintain them forever. Every unnecessary abstraction is a surface area increase you did not consent to.

The patterns I found in my own code

Premature abstraction: a UserRepository class with an interface, a factory, and dependency injection — for an app with one user type and one database. Phantom error handling: try-catch blocks around operations that cannot throw, with custom error classes that log to a monitoring service I do not have. Unnecessary dependencies: three npm packages imported for functionality that exists in the standard library. Config sprawl: environment variables for twelve settings, nine of which have hardcoded defaults that will never change. Every line compiled. Every line was unnecessary.

Why is plausible code more dangerous than buggy code?

AI does not write bad code. It writes plausible code that does more than you asked. That is worse than a bug, because bugs fail loudly. Over-engineered code passes every check — tests, linting, type safety, code review by anyone who is not paying close attention. It looks professional. It looks thorough. It looks like exactly the kind of code that gets approved in a pull request and maintained for years.

The question is not "does it work?"

The real review question for AI-generated code is not "does it work?" but "should this exist?" Before merging any AI output, apply this checklist: Does every file serve a stated requirement? Does every abstraction have more than one consumer? Does every error handler catch an error that can actually occur? Does every dependency replace more code than it adds? If the answer to any of these is no, delete it. The code that should not exist is more expensive than the code that does not work.

How DriftLess catches this before generation

DriftLess prevents over-engineering at the source. Scope locking defines what the build will produce — and implicitly, what it will not. When the AI starts generating abstractions beyond the stated scope, DriftLess flags it before publish. The review step evaluates output against your goal, not against "best practices" that may not apply to your context. The result is code that does what you asked, nothing more.

Ship code that should exist. Delete the rest.

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