How to Review AI-Generated Python Code Efficiently

To review AI-generated code efficiently, you need to put your attention where AI coding agents are most likely to be wrong. This makes your code reviews faster and more accurate.

Reviewing an agent’s code isn’t fundamentally different from reviewing a teammate’s. A code review is the process of looking at a change to a codebase before accepting it, so you can confirm that the code does what it should and that it’s correct, secure, and maintainable. This responsibility doesn’t change when the author is an agent. What changes is the amount of code, how fast it arrives, and the mix of potential issues.

An AI coding agent writes code faster than any human can read it, which makes you the bottleneck. Your job and responsibilities stay the same, but the volume and speed increase.

To manage this, you need to review the code with a plan. You can’t read every line, so focus on the parts where the risk is highest.

AI-generated code also changes the kinds of mistakes you’ll find. The logic can look correct but be wrong. The code can call APIs or import packages that don’t exist. It can also skip edge cases. A piece of code with these types of issues can look fine at first glance.

In this tutorial, you’ll learn a five-step workflow to review AI-generated code in an efficient and repeatable way:

You start with the code’s intent, then run some automated checks. After that, you read what’s left, starting with the trickiest parts. At this stage, you catch the mistakes that agents often make. You’ll also confirm each problem by running the code before you fix it.

Take the Quiz: Test your knowledge with our interactive “How to Review AI-Generated Python Code Efficiently” quiz. You’ll receive a score upon completion to help you track your learning progress:

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Interactive Quiz

How to Review AI-Generated Python Code Efficiently

Test your understanding of how to review AI-generated Python code, from automated checks to the bugs that coding agents get wrong most often.

Prerequisites

To follow along, you should be comfortable reading Python code and working with a coding agent, such as Claude Code, GitHub Copilot CLI, Antigravity CLI, and OpenCode. You’ll also get more from it if you’re already familiar with debugging Python errors, type checking, and reading a Git diff.

Ideally, you’ll want to practice with some AI-generated code. You’ll learn the most when you run the workflow on a real agent pull request or a small project that an agent has generated. Use the Python version that the target project uses. For a new project, you can choose a recent one. Python 3.14 is a good choice.

You’ll also need some tools to run quality checks on your code, such as linters, static type checkers, security scanners, and test runners. You can install them into your project’s virtual environment. Here’s a minimal set of tools you can use:

(venv) $ python -m pip install ruff mypy bandit pytest pytest-cov pip-audit

These are development dependencies, so they should stay separate from the packages your project needs to run. If you manage your project with uv, then you can add them to a dedicated dev dependency group instead:

$ uv add --dev ruff mypy bandit pytest pytest-cov pip-audit

If you prefer a faster type checker, swap mypy for ty, a newer alternative from the makers of Ruff.

For the deeper maintainability checks, add the extended set when you need it:

(venv) $ python -m pip install pylint radon import-linter pydeps

You can configure the core tools in one place. This minimal configuration turns on the rules that matter most for AI-generated code:

# ...

[tool.ruff.lint]
select = ["F", "E", "B", "S", "UP", "DTZ"]

[tool.mypy]
strict = true

[tool.coverage.run]
branch = true

You’ll also want a way to see what changed in the code. Most code reviews happen on a GitHub pull request (PR). There, you can read the diff in the browser with a friendly user interface (UI).

You can also read the diff locally in your terminal. The GitHub CLI tool prints a pull request’s changes with gh pr diff, and when there’s no PR yet, Git shows them with git diff.