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Harness Engineering- Leveraging Codex in an Agent-First World | OpenAI
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- Humans steer. Agents execute. We intentionally chose this constraint so we would build what was necessary to increase engineering velocity by orders of magnitude. We had weeks to ship what ended up being a million lines of code. To do that, we needed to understand what changes when a software engineering team’s primary job is no longer to write code, but to design environments, specify intent, and build feedback loops that allow Codex agents to do reliable work.
- The initial scaffold—repository structure, CI configuration, formatting rules, package manager setup, and application framework—was generated by Codex CLI using GPT‑5, guided by a small set of existing templates. Even the initial AGENTS.md file that directs agents how to work in the repository was itself written by Codex.
- Throughout the development process, humans never directly contributed any code. This became a core philosophy for the team: no manually-written code.
- The lack of hands-on human coding introduced a different kind of engineering work, focused on systems, scaffolding, and leverage.
- Early progress was slower than we expected, not because Codex was incapable, but because the environment was underspecified. The agent lacked the tools, abstractions, and internal structure required to make progress toward high-level goals. The primary job of our engineering team became enabling the agents to do useful work.