OpenAI’s new Codex agent is essentially a vibe-coding environment based on a ChatGPT-like comment interface. As much as the vibe-coding idea seems like a meme for wannabe cool-kid coders, the new Codex agent is impressive as heck.
Also: What is AI vibe coding? It’s all the rage but it’s not for everyone – here’s why
OpenAI described Codex as a research preview still under active development. Right now, it’s available to Pro, Enterprise, and Team-tier ChatGPT users, but it’s expected to release to Plus and Edu users “soon.”
According to the recording of OpenAI’s announcement livestream, the Codex name has been applied to an evolving coding tool since as far back as 2021. That said, when I refer to Codex in this article, I’m talking about the new version being announced now.
What is Codex?
I haven’t had the opportunity to get hands-on with Codex yet, so I’m taking everything I’m sharing with you from information provided by OpenAI. When I watched the announcement, I noticed that even the engineers seemed a little shocked at how capable this tool is.
Codex lives on OpenAI’s servers and interacts with your GitHub repositories. If the demo is to be believed (and OpenAI has repeatedly proven that unbelievable demos are real), Codex basically acts like another programmer on your team.
Also: 10 professional developers on vibe coding’s true promise and peril
You can tell it to fix a series of bugs, and it will go off and do just that. It asks you to approve coding changes, although it looks like it can also just go ahead and modify code.
You can ask it to analyze and modify code, look for specific problems, identify problem areas and room for improvement, and other coding and maintenance tasks. Each assignment spawns off a new virtual environment where the AI can go all the way from concept and design to unit testing.
A mindset change
There is a real coding mindset change going on here. Earlier AI coding help took the form of auto-complete. Lines and even blocks of code were automatically generated based on existing code.
Then we got to the point where small segments of code could be written or debugged by the AI. This is the area I’ve been focusing on in terms of the ZDNET programming tests.
Another AI role is analysis of the overall system. Last week, I showed a remarkable new Deep Research tool that can deconstruct entire codebases and provide code reviews and recommendations.
Now, with Codex, we’re getting to the point where entire programming tasks can be delegated to the AI in the cloud, in much the same way those tasks were given to other programmers on a team or to junior programmers learning their way through code maintenance.
OpenAI calls this “Agent-native software development, where AI not only assists you as you work but takes on work independently.”
Changing developer workflow
The launch video demonstrated the ability of Codex to take on a variety of tasks at once, each running in its own isolated virtual environment.
Programmers assigned tasks to the agent, which went off and did the work without supervision. When the work was complete, the agent returned with test results and recommended code changes.
The demo showcased the Codex agent performing bug fixes, doing a scan for typos, making task suggestions, and performing project-wide refactoring (modifying code to improve structure without changing behavior).
Senior developers and designers are no strangers to articulating requirements and reviewing others’ work. Using Codex won’t be much of a change for them. But for developers who haven’t yet developed good requirements-articulation and review skills, properly managing Codex may prove to be a bit of a challenge.
Yet, if the tool performs as the demo appears to indicate it can, Codex will enable smaller teams and individual developers to accomplish more, reduce repetitive work, and be more responsive to problem reports.
Consistency and flexibility
One of the problems I found early on with ChatGPT’s coding was that it had a tendency to lose the thread or go off in its own direction. For individual blocks of code, that’s annoying but not catastrophic. But if a coding agent is allowed to run fairly unsupervised, such stubborn refusal to follow directions could cause unintended and problematic consequences.
Also: The best AI for coding in 2025 (including two new top picks – and what not to use)
To help mitigate this, OpenAI has trained Codex to follow directions specified in an AGENTS.md file. This file in the repository allows programmers and teams to steer Codex’s behavior. It can contain instructions on naming conventions, formatting rules, and any other set of consistent guidelines desired in the coding process. It’s essentially an extension of the ChatGPT personalization settings, but for a repository-centric team environment.
OpenAI has also introduced a version of Codex called Codex CLI that runs locally on a developer’s machine. Unlike the cloud-based Codex, which runs asynchronously and reports back on completion, the local version operates on the programmer’s command line and is synchronous.
In other words, the programmer types out an instruction and waits for the Codex CLI process to return a result. This allows a programmer to work offline with the local context of the active development machine.
Thinking through the implications
The demo was impressive, but during the launch video, the developers were very clear that what they were showing off and releasing is a research prototype. While it offers what they called “magical moments,” it still has a long way to go.
Also: I test a lot of AI coding tools, and this stunning new OpenAI release just saved me days of work
I’ve been trying to dig in and triangulate on what exactly this technology means for the future of development and for my development process specifically. My main product is an open-source WordPress plugin, which itself has proprietary add-on plugins. Clearly, Codex could work itself through the public repository for the open-source core plugin.
But could Codex manage the relationship between one public and multiple private repositories as part of one overall project? And how would it do when testing involves not only my code but also spinning up an entire additional ecosystem — WordPress — to evaluate performance?
As a solo programmer, I definitely see the advantages of something like Codex. Even the $200-per-mnth Pro subscription makes sense. Hiring a helper programmer would cost a whole lot more per month than that fee, assuming I were to achieve tangible monetizable value out of it.
As a long-time team manager and professional communicator, I feel very comfortable delegating to something like Codex. It’s not all that different chatting with an agent than it is chatting with a team member over Slack, for example.
Also: How to turn ChatGPT into your AI coding power tool – and double your output
The fact that Codex will make recommendations, draft versions, and wait for me to approve the results makes me feel a bit safer than merely letting it run loose in my code. It does open a very interesting door for a new development lifecycle, where the human sets goals, the AI drafts possible implementations, and then the human goes back in and either approves or redirects the AI for another cycle.
Based on my earlier experiences using AIs for coding, it’s clear that Codex could reduce maintenance time and get fixes out to users faster. It’s not quite as clear how Codex would perform adding new features based on a specifications document. It’s also not clear how much more or less difficult it would be to go into the code after Codex has worked on it to tweak functionality and performance.
It’s interesting that AI coding is evolving across companies at about the same pace. I’m dropping another article soon on GitHub Copilot’s Coding Agent, which does some of the same things that Codex does.
In that article, I expressed some concern that these coding agents will replace junior and entry-level programmers. Beyond concern for human jobs, there’s also the question of what critical training opportunities will be lost if we delegate a middle phase of a developer’s career to the AI.
Into the unknown
There’s a song in Disney’s Frozen II called “Into the Unknown,” performed by Idina Menzel. The song centers on the main character’s internal conflict between maintaining the status quo and her familiar life, and venturing out “into the unknown.”
With agentic software development, more than just AI coding, the entire software industry is going into the unknown. The more we rely on AI-based systems to build our software for us, the fewer skilled maintainers there will be. That’s fine as long as the AIs continue to perform and be available. But are we letting some key skills atrophy, letting some good-paying jobs go, for the convenience of delegating to a not-yet-sentient cloud-based infrastructure?
Also: 10 professional developers on vibe coding’s true promise and peril
Only time will tell, and hopefully we won’t experience that telling when we’re out of time.
Do you see yourself delegating real development tasks to a tool like this? What do you think the long-term impact will be on software teams or solo developers? And do you worry about losing critical skills or roles as more of the code lifecycle is handed off to AI? Let us know in the comments below.
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