This video provides an overview of Codex, a new cloud-based software engineering agent from OpenAI. The agent integrates with GitHub repositories to perform tasks like implementing features, fixing bugs, and answering codebase questions via natural language prompts. It is powered by a model named "Codex 1," described as a fine-tuned version of OpenAI's latest "03" model (presumably GPT-4o), and is benchmarked as state-of-the-art on SWE-bench.
Unlike interactive tools like Cursor, Codex operates asynchronously, with individual tasks taking 1-30 minutes in isolated environments. It provides verifiable outputs through terminal logs and test results and can directly propose pull requests. The agent's behavior can be guided using an agents.md file in the project's root directory. Access is being rolled out, starting with paid ChatGPT tiers, and is available through the ChatGPT app for mobile use cases.
An Overview of OpenAI's New Coding Agent, Codex
0:00 Introduction to Codex: OpenAI has released Codex, a cloud-based software engineering agent. It's explicitly not a replacement for interactive, in-IDE tools like Cursor or Windsurf; its function is different.
0:22 Core Functionality: The agent connects to a GitHub repository and branch, taking natural language instructions to write features, fix bugs, or generate pull requests. You can see diffs and PR statuses within the interface.
0:41 Underlying Model: It's powered by "Codex 1," a version of OpenAI's "03" model (the video is unclear, but this likely refers to GPT-4o) fine-tuned with reinforcement learning for software engineering tasks. Why they're recycling the "Codex" name is beyond me; it's just confusing.
1:08 Phased Rollout: Access is available first for ChatGPT Pro, Team, and Enterprise users. Support for Plus and Education users is planned for later.
1:30 Asynchronous Workflow: Each task runs independently in an isolated, pre-loaded environment with access to your codebase and terminal. This is not an interactive process.
1:55 Long Task Completion Times: A single task can take from 1 to 30 minutes. This reinforces that it's an asynchronous delegation tool, not a real-time coding assistant. You can, however, run multiple tasks concurrently.
2:23 Verifiable and Actionable Output: After a task, Codex commits changes and provides terminal logs and test outputs as evidence. It can also open a pull request directly from the interface, which seems useful. The presenter notes this supports the trend of developers becoming reviewers of AI-generated code rather than primary authors.
3:22 Agent Guidance: You can create an agents.md file in your repository's root to provide specific instructions and context to the agent, similar to cursor.rules in Cursor. This is for defining project standards, test commands, and other operational details.
3:54 SOTA Performance: According to the video, Codex 1 is state-of-the-art on the SWE-bench benchmark, performing particularly well in one-shot attempts. As far as I can tell, benchmarks like these have limited predictive power for real-world utility, but it's better than nothing.
4:22 Mobile Accessibility: The agent will be accessible from the ChatGPT mobile app, which could be useful for on-call engineers who need to diagnose issues or review code away from a computer.