How to Evaluate AI-Generated Code in Code Reviews

AI-generated code can arrive looking clean, confident, and finished. That is exactly why it needs serious review.

The risk is not that AI always writes bad code. The risk is that it often writes code that looks reasonable before anyone has checked whether it is correct, secure, maintainable, and appropriate for your system.

A good code review in the AI era has to go beyond style comments. It has to test the thinking behind the code.

Agent-Written Pull Requests Are a Different Problem

When most teams started worrying about this, "AI-generated code" meant a function or two that an assistant suggested and an engineer accepted while watching. That is no longer the typical case. More and more, the pull request in front of you was produced by an agent working from a short instruction, spanning many files, with nobody watching it happen. The author is reading it properly for roughly the first time when you are.

Scale changes the review problem in a specific way. A large diff resists careful reading, so reviewers skim. Skimming is exactly the failure mode that generated code exploits, because the code reads well. It is coherent, consistently styled, and sensibly named. None of that is evidence that it is correct.

Two adjustments help more than anything else I have tried. First, ask for large agent-produced changes to be split, the same way you would with any oversized pull request. "An agent wrote it in one pass" is not a reason to review it in one pass. Second, ask what instruction the agent was given. If the specification was vague, the output is not trustworthy no matter how tidy it looks, and reading the instruction is usually faster than reading the diff.

Name the Cost You Are Absorbing

It is worth being explicit about what review work has become, because reviewers tend to absorb it quietly and then wonder where their week went.

DORA's 2025 research calls it the verification tax: time saved during writing gets spent again on auditing. Its broader finding is that heavier AI adoption tends to lift delivery throughput and delivery instability together, which matches what I see on real teams. The 2025 Stack Overflow survey puts numbers on the reviewer's side of this specifically. Two thirds of developers named "almost right, but not quite" as their most common frustration with AI output, and around 45% said debugging AI-generated code takes them longer than debugging code they wrote themselves.

Two things follow from that. If your team's shipping velocity went up and review load went up alongside it, that is the expected pattern rather than a sign anyone is doing it wrong. And review capacity now has to be planned for rather than assumed, because a real share of the work has quietly moved there.

Start With Ownership

The first question is simple:

Does the engineer who opened the pull request understand the code well enough to own it?

If they cannot explain the approach, trade-offs, failure modes, and test strategy, the review is not ready. AI can help write code, but it cannot be accountable for production behavior. The engineer is still accountable.

That expectation should be explicit on the team. AI-assisted code is not exempt from understanding.

Check the Problem Fit

AI often solves the problem it thinks you asked, not necessarily the problem your product or system actually has.

Ask:

Does this implementation match the real requirement? Did it assume a simpler workflow than the product supports? Does it handle existing business rules? Does it fit with how users actually behave?

This is where domain knowledge matters. A generated solution may pass unit tests and still be wrong for the business.

Look for Missing Edge Cases

AI-generated code commonly misses boundary conditions. Review for:

Empty inputs, duplicate records, retries, timeouts, partial failures, permissions, race conditions, localization, large data sets, backwards compatibility, and migration behavior.

Do not only ask, "Does the happy path work?" Ask, "How does this fail?"

That one question catches a lot.

Review the Tests More Carefully Than the Code

AI can generate tests that mostly confirm its own assumptions. That is dangerous. A large test file is not automatically a strong test suite.

Look for tests that cover behavior, not implementation details. Check negative cases. Check integration points. Check permission and security boundaries. Make sure the tests would fail if the important business logic were wrong.

If the tests only prove that the generated code does what the generated code already does, they are weak tests.

Check Security and Data Handling

Security-sensitive code deserves extra scrutiny. Watch for:

Overly broad permissions, unsafe input handling, secrets in logs, missing authorization checks, insecure defaults, weak validation, and unnecessary exposure of customer data.

AI tools can suggest patterns that are common on the internet but inappropriate for your production environment. Common does not mean safe.

Evaluate Architecture Fit

Generated code may be locally correct while globally wrong.

Ask whether the change fits the existing architecture. Does it introduce a new pattern for no reason? Does it bypass shared abstractions? Does it duplicate business logic? Does it create coupling that will hurt the next project?

Senior reviewers are especially valuable here because they know the history of the codebase. AI does not.

Keep the Review Practical

You do not need to turn every AI-assisted pull request into a courtroom trial. Use judgment. A generated test helper is different from generated authentication logic. A low-risk internal script is different from customer-facing payment code.

Match the review depth to the risk.

A Simple Team Checklist

Before approving AI-assisted code, ask:

1. Can the engineer explain the code and trade-offs?
2. Does it solve the real product problem?
3. Are edge cases and failure modes handled?
4. Are tests meaningful and not just generated confirmation?
5. Are security, privacy, and permissions correct?
6. Does it fit the architecture and team standards?
7. Would we be comfortable debugging this in production?
8. If an agent produced it, was the instruction specific enough that we can trust the result?

If the answer to any of those is weak, slow down. That is not anti-AI. That is engineering.

For a broader manager perspective, read Leading Engineering Teams in the AI Era. For individual career positioning, read How Senior Engineers Stay Relevant in the AI Era.

About Me

Nimesh Patel is an engineering leader and career coach with over 20 years of experience building cloud-native enterprise and consumer software systems in Big Tech (including Google) and high-growth AI startups. He has led globally distributed engineering organizations of 60+ engineers and leaders, conducted 650+ interviews across engineering, management, and executive roles, made 50+ hires, and coached and promoted 30+ engineers and leaders. He provides interview and career coaching through ScaleYourCareer. Follow him on LinkedIn.

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