Codex: OpenAI’s AI Agent That Takes Tasks, Not Code Lines
Codex is OpenAI's coding agent that takes a task, works on your repository, runs tests, and delivers changes for review. How it works and where it fits.
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Codex is OpenAI’s coding agent. You give it a task, it works on your repository code, makes changes, and presents them for review. The name is confusing for those who remember the previous iteration: a few years ago, Codex was a model that completed lines of code, but today it’s a completely different tool that works as an autonomous executor, not a suggestion engine.
How Codex Works
You describe a task, the agent orients itself in the project and executes it in an isolated environment where it can run commands and tests. The result is a set of changes with a description of what it did, ready for you to review and either accept or reject. Multiple tasks can run in parallel, which changes how you work: instead of sitting over one thing, you distribute several assignments and come back to the results.
It’s available in the browser, in the terminal, and as plugins for development environments. The terminal version is open source, so you can look at how it works and run it your own way.
What Works Well and What Doesn’t
What works well are closed, verifiable tasks: fix this bug, add tests to this module, update dependencies, move this function. Anything with a clear end condition, the agent completes on its own because it can see whether tests pass.
What doesn’t work well are design decisions. The agent doesn’t know why your system is built one way and not another, so it will solve the problem correctly but inconsistently with the rest of the project. For tasks touching architecture, it makes more sense to point the direction yourself and let it handle only the execution.
How It Differs from Other Tools
Compared to Cursor: there you work with your eyes in the editor and see every change in real time, here you hand off a task and evaluate the result. Compared to Claude Code: similar agent philosophy, different model provider and different billing, so the choice usually comes down to whose subscription you already have. Compared to Lovable: there a ready-made application is created for a non-programmer, here code for evaluation by someone who can read it.
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First, your project code goes to the model provider, so with confidential material you need to configure this consciously before the first run. Second, billing is based on usage or from a subscription, and long tasks on a large repository can surprise you with the bill. Third, and most important: just because changes are described and look tidy doesn’t mean they’re correct. Code review stops being a formality and becomes the main part of your work. I maintain a broader overview of tools in this category in the best AI for code section.
See also: Factory.ai and Droid: coding agent · Linear: task management for teams working with agents
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