QA for AI-Generated Code: What Changes When Your Developers Ship Faster Than They Read

A pull request that took a developer twenty minutes to write with an AI assistant still takes a reviewer the same twenty minutes to actually understand. That mismatch is where the risk lives now: output volume went up, review capacity didn't, and QA ends up as the last check on code that nobody on the team has read as closely as they would have written it.

The bug pattern is different

AI-generated code tends to compile, follow the project's existing style, and look correct at a glance, because it was trained on code that looks correct at a glance. The bugs that get through aren't syntax errors or obviously missing logic. They're a wrong edge case handled the way a similar-looking case would be handled elsewhere in the codebase, an error path that catches the exception and logs it instead of surfacing it, a condition that's technically true for the example the assistant was working from but false for the actual data your system sees in production.

None of that trips a linter. Most of it doesn't trip a type checker either.

Test coverage from the same source isn't verification

If the same assistant that wrote the feature also writes the tests for it, the tests tend to assert on what the code does, not on what the code was supposed to do. That's not a hypothetical failure mode, it's the natural result of generating both from the same understanding of the requirement. A suite that passes because it agrees with its own implementation gives you a green pipeline and no actual verification.

This is the same trap covered in our agentic QA piece: an agent extending a suite against existing conventions is genuinely useful, but the test authorship and the code authorship can't be the same blind spot checking itself.

What actually catches these bugs

Tests written from the requirement, not the implementation. Someone, or something, working from the spec and the acceptance criteria rather than reading the generated code and confirming it does what it does, is testing a different thing than the code review is checking.

Review focused on intent, not style. AI-generated code is usually stylistically consistent, which means style is the wrong thing to spend review time on. The question worth asking line by line is whether this is the right computation for this case, not whether it follows the linting rules, since it almost always does.

Exploratory testing on the actual feature, not just its happy path. The fastest way to find a mishandled edge case is to try the input the requirement didn't spell out, which is exactly the kind of testing that doesn't show up in a regression suite until someone's already found the bug manually once.

What this means for your process

Nothing about this requires banning AI-assisted code. It requires treating the review and test-authorship step as the part of the pipeline that can no longer assume the volume of change matches the amount of scrutiny it's gotten. If your release cadence went up after adopting AI coding tools and your bug count in production didn't go down to match, that gap is usually sitting exactly here.