How to Review AI-Generated Code

The agent just handed you 200 lines of code. It looks right. It might even be right. But "looks right" and "is right" are two very different things — and the gap between them is where bugs live, where security holes hide, and where technical debt quietly accumulates.

Reviewing AI-generated code is one of the most important skills a developer can build right now. Not because the code is bad. Because it's fast, and fast without oversight is how projects go sideways.

Here's how to do it without burning out, second-guessing everything, or slowing down to the speed of writing it all yourself.

First, Change How You Think About Your Role

When you write code yourself, you're the author. You know every decision, every tradeoff, every shortcut you took. The code is an expression of your thinking.

When an agent writes code, you're the editor. You didn't make the decisions — you're evaluating them. That's a different job, and it requires a different mental posture.

Editors ask: Does this do what it's supposed to do? Does it do anything it's not supposed to do? Does it fit the rest of the work?

Stop reading AI-generated code looking for things you would have done differently. Start reading it looking for things that are actually wrong. That shift alone makes the process faster and less frustrating.

The 4-Layer Review

Think of reviewing AI-generated code in four passes, each answering a different question. You don't have to be exhaustive on every layer for every change — calibrate depth to the risk of what's being merged.

Layer 1: Intent — Does it do the right thing?

Before reading a single line, restate what you asked for. Not what the agent generated — what you asked for. Write it down in one sentence if you have to.

Then read the code through that lens: does this implementation match the intent? Agents are excellent at producing code that solves a slightly different problem than the one you described. Not wrong, exactly — just subtly off. Catch it here.

Questions to ask:

• Does the function name describe what the function actually does?

• Are the edge cases I care about handled?

• Is anything here that I didn't ask for?

Layer 2: Logic — Does it work?

This is the traditional code review pass. Read the logic. Trace the execution path mentally, or better, run it.

Focus on:

• Conditionals and their edge cases (the null, the empty array, the unexpected type)

• Loops and their exit conditions

• Error handling — is it catching the right things, or catching everything and hiding real failures?

Agents tend to write optimistic code. They handle the happy path elegantly. They sometimes treat errors as an afterthought. This is where that shows.

Layer 3: Fit — Does it belong here?

A piece of code can work perfectly and still be wrong for your codebase. Layer 3 is about coherence.

Ask:

• Does this follow the patterns already established in the project?

• Does it introduce a new dependency? Is that dependency worth it?

• Is this in the right place — the right file, the right abstraction layer?

This matters more as your project grows. The agent has context for the task it was given. It doesn't always have context for the architecture you're building toward.

Layer 4: Security & Data — Can this be abused?

This layer is non-negotiable for anything that touches user input, authentication, external APIs, or a database.

Ask:

• Is user input validated before it's used?

• Are there any SQL queries, shell commands, or file paths being constructed from unsanitized data?

• Is anything being logged that shouldn't be (tokens, passwords, PII)?

• Does the agent have a reason to fetch more data than this operation needs?

You don't need to be a security engineer to do this pass. You need to be paranoid in a focused, structured way. That's different.

A Real Scenario: The Feature That Almost Shipped

Say you're building a solo SaaS project — a dev tool with user accounts. You ask your coding agent to implement the "delete account" feature. It comes back quickly with a clean-looking endpoint that deletes the user record, clears the session, and returns a 200.

Layer 1 catches nothing — the intent matches.

Layer 2 catches nothing — the logic looks sound.

Layer 3 flags something — the agent put the deletion logic directly in the route handler, but the rest of your data operations live in a service layer. Minor, but inconsistent.

Layer 4 catches something real — the endpoint doesn't verify that the authenticated user is the same as the user being deleted. Any logged-in user could technically delete any account by passing a different user ID.

One layer, one critical bug found before it shipped. That's what structured review looks like in practice.

When You Don't Understand Something, Ask

This sounds obvious. It isn't practiced enough.

If you're reading a section of agent-generated code and you don't fully understand what it's doing, ask the agent to explain it. Not because you're not smart enough to figure it out — because asking is faster, and the explanation often reveals assumptions the agent made that you'd want to know about.

Go further: ask the agent to write a test for the function it just wrote. If it can't write a meaningful test, or if the test it writes only covers the happy path, that tells you something important about the code's complexity or fragility.

The agent is not just a code generator. It's also the most informed code reviewer of its own output. Use that.

Build the Habit, Not the Checklist

The 4-layer review isn't meant to be a literal checklist you run through on every commit. It's a mental model that, over time, becomes automatic.

What you're building is calibrated trust — knowing when to look closely and when to move fast. Senior developers have this with colleagues. You're developing it with an AI collaborator.

The goal is not to review everything. The goal is to never ship something you don't understand.

The Skill That Compounds

Here's the thing about getting good at reviewing AI-generated code: it makes you a better developer, full stop.

You start to see patterns in what agents get wrong. You write better prompts to prevent the mistakes you've already caught. You develop a sharper instinct for where risk lives in a codebase. You become faster at reading code you didn't write.

The developers who thrive with AI coding agents aren't the ones who let the agent run unchecked. They're the ones who know exactly what to look for — and build that into their rhythm from day one.

That's not losing your mind. That's gaining one.

Want to go deeper? Read our guide on building a full-stack solo project with an AI coding agent. [Read next →]