OpenAI has introduced the Codex app for macOS, a new desktop interface designed to help developers manage multiple AI agents working in parallel across long-running software projects. The app is built for teams that rely on agents to design, build, test, deploy, and maintain code over extended periods—addressing a growing gap between AI capability and practical developer control.

Since the initial release of Codex in April 2025, developers have increasingly relied on AI agents to handle complex, end-to-end tasks rather than isolated code snippets. As models became capable of sustained reasoning and execution, the bottleneck shifted from what agents could do to how humans could supervise and coordinate them at scale.

As agent workloads expanded in duration and complexity, existing IDEs and terminal-based tools proved poorly suited for supervising multiple agents at once. OpenAI positions the Codex app as a response to that gap, providing a centralized environment where developers can direct, monitor, and collaborate with coordinated agents across projects.

The Codex app provides a focused environment for managing multiple agents working in parallel. Agents run in separate, project-based threads, allowing developers to move between tasks without losing context. Within each thread, developers can review an agent’s changes, comment directly on code diffs, or open the work in their editor to make manual adjustments.

Built-in support for Git worktrees allows multiple agents to operate on the same repository without conflicts. Each agent works on its own isolated copy of the codebase, enabling parallel experimentation while keeping local Git state untouched. Developers can choose to check out an agent’s changes locally or allow work to continue independently in the background.

The app also syncs session history and configuration from the Codex CLI and IDE extensions, enabling teams to transition into the desktop app without reconfiguring existing projects.

Codex is evolving from a coding assistant into a system that uses code to get work done across a developer’s environment. Skills allow teams to bundle instructions, resources, and scripts so Codex can reliably complete tasks that involve research, synthesis, writing, and tool integration.

The Codex app includes a dedicated interface for creating and managing skills, which can be invoked explicitly or used automatically depending on the task.

OpenAI highlighted an internal example where Codex was prompted to build a complete racing game, including multiple racers, eight playable maps, and in-game items activated by player input. Using image generation and web game development skills, Codex independently handled design, development, and testing, processing more than 7 million tokens from a single initial prompt. According to OpenAI, the agent took on the roles of designer, game developer, and QA tester, validating its work by actually playing the game.

The Codex app includes a growing library of skills developed and used internally at OpenAI, designed to help teams reliably delegate complex, repeatable work to agents. Examples include:

Skills created in the Codex app can be used consistently across the desktop app, CLI, and IDE extensions. Teams can also check skills into repositories, making them available across shared projects and workflows.

The Codex app also introduces Automations, which allow agents to run on a scheduled basis in the background. Automations combine instructions with optional skills and deliver results to a review queue when complete.

OpenAI reports using Automations internally for tasks such as:

Future updates will add cloud-based triggers, enabling Codex to run continuously without requiring the app to remain open.

Developers have different preferences in how they work with AI agents. Some prefer a blunt, execution-focused partner, while others favor more communicative, conversational interactions. Codex allows developers to choose between a terse, pragmatic communication style and a more empathetic one, without changing underlying capabilities. The setting affects tone rather than functionality and can be adjusted using the /personality command across the app, CLI, and IDE extensions.

Security is integrated by design across the Codex agent stack. The Codex app uses native, open-source, and configurable system-level sandboxing, consistent with the Codex CLI. By default, agents are restricted to editing files within their working directory and using cached web search.

Commands that require elevated permissions—such as network access—prompt for explicit approval. Teams can also define configurable rules that allow specific commands to run automatically, enabling controlled flexibility without removing oversight.

The Codex app is available starting today on macOS. Codex usage is included with ChatGPT Plus, Pro, Business, Enterprise, and Edu subscriptions, with the option to purchase additional credits. Codex can be accessed across the desktop app, CLI, web, and IDE extensions using the same ChatGPT login.

For a limited time:

OpenAI reports that since the release of GPT-5.2-Codex in December, overall Codex usage has doubled, with more than one million developers using the tool in the past month alone.

Upcoming plans include:

OpenAI frames Codex around a central idea: everything is controlled by code. By making advanced agent capabilities easier to direct and supervise, the company aims to narrow the gap between what frontier models can do and what developers can practically use in real work. OpenAI describes Codex’s strength as a coding agent as the foundation for extending its use into broader technical and knowledge work beyond writing code.

Q: What is the Codex app for macOS?
A: The Codex app is a desktop interface from OpenAI that allows developers to manage, supervise, and collaborate with multiple AI agents working in parallel across software projects.

Q: How is the Codex app different from using Codex in an IDE or CLI?
A: While the CLI and IDE extensions focus on individual tasks, the Codex app is designed for orchestration—organizing agents by project, tracking long-running work, reviewing changes, and switching contexts without losing progress.

Q: What are Skills in Codex?
A: Skills are reusable bundles of instructions, resources, and scripts that allow Codex to perform structured workflows such as deploying apps, translating designs, generating images, or creating documents reliably.

Q: What are Automations used for?
A: Automations allow Codex agents to run tasks on a schedule in the background, such as triaging issues, summarizing failures, or generating reports, with results delivered to a review queue.

Q: Who can use the Codex app?
A: The Codex app is available to ChatGPT Plus, Pro, Business, Enterprise, and Edu users, with temporary access extended to Free and Go users during the launch period.

As AI models continue to improve rapidly, the constraint for developers is no longer raw capability—it’s coordination. The Codex app reflects a shift from single-agent assistance toward supervised, multi-agent execution, where the challenge is managing parallel work, maintaining context, and ensuring reliability over time.

Who should care:
Software developers working on complex systems, engineering leaders overseeing distributed teams, and organizations experimenting with agent-based development should pay close attention. The Codex app addresses a growing pain point: how to safely delegate meaningful work to AI agents without losing visibility or control.

Why it matters now:
As AI agents become capable of sustained, long-running tasks, traditional development tools struggle to support this new mode of work. Without clear supervision, permission boundaries, and workflow structure, agent-based systems can become difficult to trust. Codex’s desktop app arrives as more teams move beyond experimentation and into real production use of AI agents.

What decision this affects:
The Codex app highlights a choice facing development teams: continue using AI as an isolated productivity tool, or invest in systems that allow multiple agents to operate as coordinated contributors across the software lifecycle. That decision affects development speed, reliability, and how comfortably teams can scale agent-driven workflows.

How teams answer that question will shape whether AI agents remain side tools—or become dependable collaborators in everyday development work.

Editor’s Note: This article was created by Alicia Shapiro, CMO of AiNews.com, with writing, image, and idea-generation support from ChatGPT, an AI assistant. However, the final perspective and editorial choices are solely Alicia Shapiro’s. Special thanks to ChatGPT for assistance with research and editorial support in crafting this article.