5 key Codex features worth trying in GPT 5.4

AI has been a coding buddy for programmers for quite some time now, whether it’s helping spot errors, fix bugs, or even with something as fundamental as building logic. Furthermore, Codex from OpenAI has become one of the most loved AI tools in the developer community, not just because of the solutions it provides but also because of the way it explains and teaches concepts.

What’s even more impressive is how consistently OpenAI has been improving its GPT models to make them more friendly for developers. Some time ago, they launched Codex, and since then the company has continued to expand its capabilities. Recently, they also introduced a dedicated Codex app that focuses on helping developers manage larger coding tasks with more automation and better structure.

After spending some time testing the latest GPT5.4 model in the Codes app with small projects and experiments, I discovered a few features that are definitely worth trying. So without any further ado, let’s deep dive into the must-try Codex features in GPT5.4.

1. Autonomous AI coding agents

One of the very first Codex features I tested was the idea of autonomous coding agents. Instead of asking Codex for small pieces of code, I tried giving it a full task to see how it would respond. I asked Codex to create a simple Python task manager using Flask. Normally the AI generates a small code snippet and stops there. However, with the latest Codex models, it actually started suggesting a project structure.

It suggested a folder layout, created routes for adding and completing tasks, and even wrote a basic HTML template. I was surprised because the response felt like someone had thought through the whole feature before writing code.

After that I kept modifying the project to my liking. I asked Codex to add login functionality and basic error handling. What surprised me was that, unlike the old times, it adjusted the earlier code and explained the changes instead of replacing everything and writing everything again from scratch.

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From my experience, this feature works best when you give Codex a clear goal. Rather than asking for one function at a time, you describe the feature you want to build. Codex then breaks the work into smaller steps and starts generating code that fits the overall structure.

However, I feel that it still requires human supervision, but it removes a lot of repetitive setup work that normally takes time.

2. Handling multiple development tasks in a single workflow

One major limitation of earlier AI assistants was that they could only do tasks one after another. But with the Codex experience, the system can work on several development tasks in the same workflow. This makes it easier to handle different parts of a project at the same time.

I gave Codex two tasks in a single prompt to evaluate the feature. At first I asked it to debug a Python script that was using too much memory, and at the same time I asked it to create a small React interface to display the script’s output.

Codex treated the tasks as separate problems and started analysing the Python code while also generating the React components. The response suggested that the tasks were being handled independently within the same request.

For someone who often experiments with both backend and frontend ideas, this feature can turn out to be highly useful. Instead of finishing one step before starting another, a developer can work on multiple parts of the project within the same interaction.

This approach saved time during experimentation, and I was able to debug logic while also asking Codex to prepare interface components that I planned to use later.

3. Long-context understanding for large codebases

One of the most common problems with AI coding tools is that they struggle when the code becomes too long. I’ve tried using many assistants, but each one of them loses track of context once several files are involved. This was the case with ChatGPT as well. However, the company has now improved its long-context understanding with the latest Codex models.

To test it, I tried using it on an old JavaScript project that I had not touched for quite some time. Rather than sharing my code for just one function, I decided to share a few related files for the project so that the assistant could understand the whole project better.

I asked the Codex assistant to review my code and suggest improvements for making it simple. The AI coding assistant provided an analysis of my code and offered several suggestions for making it simple, which was impressive.

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It pointed out a function that was not being used anywhere in the project. It also pointed out a part of the code where complicated nested conditions could be replaced with a simple structure.

Codex did not just mark lines of code; rather, it described how the functions in the code interacted and why making certain changes would make the program easier to maintain.

4. Codebase Q&A and project understanding

OpenAI also said that Codex could now understand a whole project and answer questions about it. So I put it to the test. Instead of asking it to write new code, I used it to help me understand some old code that I had written a few months earlier.

I pasted multiple files from the project and asked Codex a simple question about where a certain function was being used. Rather than pointing to a specific file, it actually described the connection between the function and the rest of the project.

Additionally, it described the different parts of the project that were depending on the function. Making it easier to quickly grasp the connections between different parts of the codebase without digging through every file.

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5. Automatic test fixes and debugging

Lastly, I tested how Codex handles debugging tasks when something breaks in the code. I gave Codex a small script with a few logical errors. I then requested it identify the errors. Instead of simply pointing out the line of code where the errors were located, Codex actually explained the reasoning behind the errors.

However, I still wasn’t satisfied, so I also shared a small test file where one of the test cases was not passing. The tool analysed the test and the function related to the test, and it then offered a small change to the function to fix the problem.

In my opinion, such debugging assistance can be particularly useful when working on larger projects where it may take a considerable amount of time to pinpoint the source of the problem for which the test is failing.