Why Codex Outperforms Claude Code

Codex App is redefining the boundaries of AI programming tools. It not only enhances code writing capabilities but also builds a complete engineering workstation—ranging from sandbox permission control to task management, and from planning and steering mechanisms to Git integration. This allows users to delegate tasks to AI while maintaining control over project progress. This article delves into how this tool addresses the most challenging issues of trust and controllability in AI collaboration through systematic design.

Recently, I ran through Codex App from start to finish, initially intending to create a comprehensive tutorial. However, I realized that this was more than just a tutorial issue.

The competition among AI programming tools cannot solely focus on who writes code better anymore. Claude Code is certainly powerful; it operates smoothly in the terminal, can read projects, modify files, run commands, and connect to MCP. It also features permission control, sandboxing, sub-agents, desktop, and web interfaces. For many engineers, it is a sharp tool.

However, Codex App feels different. It does not merely sharpen the knife; it sets up the cutting board, knife rack, preparation area, and serving area. What you receive is not just an AI that can write code, but a workstation that allows AI to be assigned, constrained, reviewed, and delivered. This is what I find most noteworthy about Codex.

Sandbox: Defining Boundaries

Many people fear not that AI cannot write code, but that it is too capable. When you ask it to modify a small requirement, it might inadvertently touch several files; when you ask it to run a test, it might attempt to connect to the internet to install dependencies; when you ask it to organize a project, it might access directories you never intended for it to touch.

You might say “leave it to AI,” but your body instinctively stays vigilant, watching closely. Because you are not just observing whether it can write code; you are watching to see if it will overstep its boundaries.

What impressed me about Codex App is that its permission control revolves around the sandbox concept. It manages the current project folder as a sandbox. By default, Codex can read and write files within the sandbox without asking for confirmation every time it modifies a file. This is crucial.

If AI develops normally within the project folder, requiring confirmation for every step would quickly turn the user into a permission popup administrator. However, Codex does not completely open the floodgates. By default, it cannot modify files outside the sandbox or connect to the internet. If it needs to access external directories, download dependencies, or perform higher-privilege operations, it will initiate a privilege escalation request.

The most comfortable aspect of this mechanism is that it does not require you to supervise every step; instead, it first defines the boundaries. Within the boundaries, AI works autonomously. Outside the boundaries, it stops to ask you. This transforms “process supervision” into “boundary supervision.” The former is exhausting, requiring constant vigilance over its next move; the latter is much lighter, as you only need to know which box it is working in and when it wants to break out.

I personally recommend the automatic review mode, where low-risk privilege escalations are automatically approved, while high-risk operations require human confirmation. In daily use, it strikes a good balance between safety and efficiency.

This is also one of the most significant differences in the experience between Codex and Claude Code. It’s not that Claude Code lacks safety mechanisms; it also has permission control, sandbox configurations, and allow/ask/deny rules, all of which are robust. However, Codex places the sandbox at the core of the entire product experience. From the moment you open a project, the workspace, permissions, approvals, internet access, and context all revolve around this sandbox. It is not merely a “security option in advanced settings”; it is the prerequisite for whether you can confidently delegate tasks.

Not Just a Chat Window, But a Task List

The three-column layout of Codex App may appear simple, but I increasingly feel it captures a key point. The left side is a task list, the middle is a dialogue window, and the right side is a multifunctional area.

You can open multiple tasks across different projects or multiple dialogues within the same project. During my testing, I simultaneously opened three tasks: one project for an HTML single-page pet grooming website, another for a React to-do list tool, and a separate dialogue for React framework questions. The left side displays the status of these tasks—some are executing, some are awaiting approval, and some are completed.

This is not merely about a visually appealing interface. It signifies that Codex does not treat the AI agent as a chat window but as a set of manageable work tasks. Previously, when using AI for programming, it often felt like “I’m discussing a problem with the model.” With Codex, it feels more like “I’m coordinating several agents to get work done.”

This shift is friendly for product managers, small team leaders, content teams, and others who may not spend all day at the terminal or wish to manage task status through a slew of commands. They need a workstation that is understandable, switchable, and controllable.

In this regard, Codex App resembles a product more than traditional CLI tools.

Plan and Steer: Keeping AI on Track

Complex tasks are most at risk when AI starts working immediately. For example, if you ask Codex to transform a project into Next.js, it could easily take a route different from what you envisioned.

The Plan mode is designed for such tasks. Once activated, Codex will not immediately modify the code; instead, it will provide you with a plan first. It will also align with you on key choices using question cards, such as whether to use App Router or another form, whether to migrate styles to Tailwind, and whether to simultaneously start a local development server for validation.

Once the plan is confirmed, the risk of rework is significantly reduced. Steer is another practical feature. When testing a store map, I initially hoped Codex would leverage AI image generation capabilities to create a cute-style map. Instead, it started by drawing a rough SVG sketch.

In such cases, the best approach is not to wait for it to finish before requesting changes but to take the wheel during execution. I screenshot it to show that the image was inadequate and that it should utilize AI drawing capabilities. After being guided, Codex quickly switched to generating the image and replaced it on the webpage.

Plan aligns the direction before work begins. Steer takes the wheel when it veers off course. Together, these two features make Codex not just an executor but a manageable collaborator.

The most troublesome aspect of AI agents is not that they can’t get the job done, but that they can become increasingly serious about pursuing the wrong direction. Codex at least provides you with two points to hit the brakes.

Git, Rollback, and Worktree: Handling Project Closure

Once AI programming enters the production process, the most critical question is not “Can it write?” but “How do we wrap things up after writing?” During my testing, I asked Codex to add an “Expected Arrival Time” field to the pet grooming page. After it completed the task, I saved it using Git. Later, I asked it to adjust the field position, which resulted in a less satisfactory layout, and I wanted to revert to the previous state.

In this case, merely rolling back the dialogue is insufficient because the code has already changed. Codex’s dialogue branching allows you to return to a specific dialogue node and, in conjunction with Git, revert the code to the corresponding commit. This way, a single unsatisfactory modification can be undone from both the dialogue history and code state perspectives.

This is crucial. The more AI does, the more important rollback capabilities become. If users are afraid to revert changes, they will hesitate to experiment; if they hesitate to experiment, they will not let AI do more.

Worktree takes it a step further. I created two independent worktrees: one for optimizing customer reviews and another for improving store information layout. The two branches developed in separate folders without interference, and once completed, they could be merged back into the main branch.

This essentially assigns independent workstations to different agents. In the past, when discussing multiple agents, it often meant merely opening several chat windows. But the real issue is: if multiple agents modify code simultaneously, will the site become polluted? How do we merge after completion? What if something goes wrong?

Worktree provides an engineering solution. Each task has its own workspace. If it’s successful, it merges; if it fails, it gets removed. This is another way Codex resembles an engineering workstation. It cares not only about generation but also about isolation, review, merging, and rollback.

Cloud, Plugins, Skills, MCP: Making Codex a Platform

If we only consider local development, Codex is already quite complete. However, its greater potential lies in transforming the AI agent into a platform that can connect to the external world.

The Cloud mode is one example. After syncing code to GitHub, Codex can run tasks in the cloud. For instance, I asked it to set the default “Expected Arrival Date” on the homepage to tomorrow at 9:30 AM. It initializes a cloud environment, pulls the GitHub code, completes the modification, and then creates a Pull Request.

You can review the code on GitHub, confirm it, merge it, and sync it back to local. This means you don’t have to be sitting at your computer to have the agent work. Even when out and about, you can approve tasks via your phone, allowing cloud tasks to continue progressing.

Additionally, there are agents.md, plugins, Skills, and MCP. agents.md addresses project memory issues. In complex projects, re-explaining the background for each new dialogue is inefficient. By writing down project rules, author preferences, tech stacks, and considerations, Codex can automatically read this information when starting new dialogues.

Plugins facilitate connections to external services, such as GitHub, Gmail, and Netlify. Skills encapsulate professional workflows. You can call a Remotion skill for animations or install a web PPT skill to generate presentation-ready pages from text. You can even use Skill Creator to encapsulate repetitive tasks like “turning video subtitles into text tutorials” into your own skill.

MCP standardizes external tools into interfaces. For example, through Supabase MCP, Codex can create appointment business tables, modify backend interfaces, and write form data into databases.

These capabilities combined make Codex not just a code assistant. It begins to resemble an agent work platform. It can write code, connect plugins, solidify workflows, connect databases, deploy websites, run automation, and even operate a computer on a Mac through Computer Use.

This is what makes Codex worth paying attention to. It does not merely enhance the action of “writing code”; it is gradually incorporating the environment needed for AI agents to work into a single product.

So, Where Does Codex Excel?

When evaluating model capabilities, the differences between Codex and Claude Code may not always be apparent. The real distinction lies in the product form.

Claude Code feels more like a sharp tool for engineers. It is close to the terminal, offers extensive configuration space, and is suitable for those familiar with command lines, permissions, scripts, and engineering automation.

Codex resembles a controllable engineering workstation. It integrates sandboxing, permissions, tasks, planning, guidance, browser validation, Git, Worktree, Cloud PR, plugins, Skills, MCP, and automation into a single experience.

This makes it more user-friendly for a broader audience, especially product managers, entrepreneurs, small team leaders, content teams, and operations teams. These individuals may not want to become terminal experts, but they genuinely want to delegate a piece of work to AI, hoping to understand what it has done, know if it has overstepped, and confirm whether the results can be merged.

In the past, when evaluating AI programming tools, we often asked:

Now, I will ask a few more questions:

• Where does it write?
• Will it stop when it oversteps?
• After it finishes writing, can the team catch it?

This is what has impressed me about Codex. It is not just about adding a few more features. It tells users: you can let AI work, but you do not have to hand over the entire computer, the whole project, or all decision-making authority.

The truly important AI programming products of the future may not be the agents that write code the best, but rather the systems that instill the most confidence in delegating work.