Will AI pass a code review?

AI can do in an hour what used to take weeks. But would you stake your production environment on it without a Senior’s review?

🚩 Probably not. “Working code” is no longer the benchmark. Maintainable code is.

As we lean more heavily on AI agents, the core engineering question is shifting from “How do I write this?” to “How clean is this code?”

Here is why an output that “just works” might still be a ticking time bomb:

1. The Spaghetti Code Risk. An agent might solve a complex problem with a single 500-line function. It passes the unit tests, but it’s a maintenance nightmare. The human’s job now is to enforce architecture and SOLID principles where AI defaults to “just make it work.”

2. Security “Hallucinations”. AI defaults to the most common patterns in its training data – not always the most secure ones. We’re seeing everything from hardcoded credentials to deprecated, vulnerable libraries woven into codebases because they “looked right” to the LLM.

3. The Efficiency Trap. Sure, the UI looks great. But what’s under the hood? Is it an optimal algorithm, or a nested O(n²) loop that will choke on real-world data volumes? Functional testing isn’t enough anymore; we need deep architectural evaluation.

We are entering a world where AI is the Developer, and the Human – backed by robust evaluation frameworks – is the Senior Reviewer.

Writing code is becoming cheap. Reviewing, auditing, and orchestrating code are the new premium skills.

That is why we have adopted a practice of double-checking everything the AI writes.

First, you review what the AI agent has written from a human perspective, and only then do you submit a pull request to the shared repository.
In the second stage, your colleague reviews the final result once more.

At both stages, comments regarding the implementation or suggestions for major refactoring often arise.