How to Review AI-Generated Code Before Shipping

An AI coding agent finishes a feature, tells you it's done, and the cursor blinks waiting for your next instruction. That moment — right before you say "ship it" — is where most AI-coding mistakes actually happen, not in the code itself. The agent isn't lying to you when it says "done." It just means the code compiles and the task looks complete from where it's sitting. Whether it actually works, and whether it's safe, is still your job to check. Here's a concrete workflow for doing that in a few minutes, not a few hours.

Step 1: Click through it yourself before you trust it

This is the step people skip most often, because "the agent said it works" feels like enough. It isn't. Open the app or the page and actually use the feature — click the button, submit the form, load the page on a phone-sized window, log out and back in if that's part of the flow.

You're not looking for bugs specifically yet. You're looking for the gap between "the code runs without errors" and "the feature does the thing I asked for." An agent can build a working signup form that never actually sends the confirmation email, or a dashboard that renders fine with fake data but breaks on an empty account. You won't catch that by reading code — you catch it by using the thing.

Step 2: Ask the agent to explain the change in plain language

Before you dig into the diff yourself, ask: "explain what you just changed and why, in plain language, no code." This is useful for developers and essential for non-developers.

Two things to listen for:

• Does the explanation match what you asked for? If you asked for a password reset flow and the explanation mentions it also "simplified the login check," that's scope creep worth a second look.
• Does the explanation make sense on its own terms? If the agent's own description of its change sounds hand-wavy or overconfident ("this should handle most cases"), that's a signal to push further before you accept it, not a green light.

This step costs you thirty seconds and catches a surprising number of problems before you've even opened a file.

Step 3: Read the actual diff, not the summary

The summary is the agent's opinion of what it did. The diff is what it actually did. Open the changed files and skim every line that was added or modified — you don't need to understand every function deeply, but you do need to see it.

Specifically check for:

• Files you didn't expect to be touched. If you asked for a new button and three unrelated files changed, ask why before merging.
• Hardcoded secrets or credentials. API keys, database passwords, tokens pasted directly into code instead of read from an environment variable. This happens more often than you'd think, especially when an agent is debugging a connection issue and takes a shortcut. Search the diff for anything that looks like a real key before it gets committed — and if you find one, rotate it, don't just delete the line.
• New dependencies. A package that got added to fix a small problem is a new piece of code you're now trusting, with its own maintainers and its own risk.
• Anything touching permissions, auth, or environment flags. Even in a change that's supposedly unrelated to those things.

Step 4: Test edge cases, not just the happy path

The agent tested the path it was thinking about — the normal, expected input. It's on you to try the things a real user eventually will:

• Submit the form with a field empty, or with way too much text in it.
• Try it with no internet connection, or a slow one.
• Log in with an account that has zero data instead of the seeded demo account.
• Click a button twice quickly, or navigate away mid-action.
• Try an input the feature clearly wasn't designed for — a negative number where you'd expect positive, an emoji in a name field.

None of this needs to be exhaustive. Ten minutes of deliberately trying to break the feature catches more real bugs than an hour of reading code for correctness, because it tests what actually happens, not what the code looks like it should do.

Step 5: Use version control so you can always roll back

Before you let an agent make a nontrivial change, make sure you're on a clean commit you can return to. This isn't about distrust — it's the same discipline you'd want for any change, AI-written or not. Commit often, in small chunks, so that if a change turns out to be wrong you're reverting one feature, not untangling three hours of mixed edits. If something breaks after you ship, "roll back to the last commit" should be a one-line command, not a scramble.

Step 6: Know when to slow down and when a quick check is enough

Not every change deserves the same scrutiny, and treating a landing page tweak with the same paranoia as a payments change just slows you down without making anything safer. Calibrate the depth of review to what the code touches:

The rule of thumb: if a mistake here could touch money or someone else's data, read the code. If a mistake here is annoying but reversible and low-stakes, a working click-through is enough.

The checklist, all together

• Click through the actual feature yourself — don't trust "it's done."
• Ask the agent to explain the change in plain language and check it matches what you asked for.
• Read the diff, not just the summary — look for unexpected files and hardcoded secrets.
• Test at least a few edge cases, not just the happy path.
• Commit in small chunks so you can always roll back.
• Match your review depth to what the change touches — deep read for payments/auth/user data, quick check for everything else.

This isn't a heavyweight process. Most of it takes minutes, and it's the same judgment a careful developer already applies to a human teammate's pull request. The agent just means you apply it every time — not only when something "feels" risky, since the changes that turn out to matter rarely announce themselves in advance.

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