OpenAI spills technical details about how its AI coding agent works
摘要
OpenAI工程师近日公开了其Codex CLI编码代理的技术细节，展示了该工具在人工监督下编写代码、运行测试和修复错误的工作原理。当前AI编码代理正进入实用化阶段，能够快速生成原型和基础代码，但在复杂任务中仍存在局限性，需要人工干预以确保生产环境中的可靠性。技术说明同时指出，这类工具在处理训练数据之外的问题时表现不稳定，实际使用中需结合人工调试。
On Friday, OpenAI engineer Michael Bolin published a detailed technical breakdown of how the company's Codex CLI coding agent works internally, offering developers insight into AI coding tools that can write code, run tests, and fix bugs with human supervision. It complements our article in December on how AI agents work by filling in technical details on how OpenAI implements its "agentic loop."
AI coding agents are having something of a "ChatGPT moment," where Claude Code with Opus 4.5 and Codex with GPT-5.2 have reached a new level of usefulness for rapidly coding up prototypes, interfaces, and churning out boilerplate code. The timing of OpenAI's post details the design philosophy behind Codex just as AI agents are becoming more practical tools for everyday work.
These tools aren't perfect and remain controversial for some software developers. While OpenAI has previously told Ars Technica that it uses Codex as a coding tool to help develop the Codex product itself, we also discovered, through hands-on experience, that these tools can be astonishingly fast at simple tasks but remain brittle beyond their training data and require human oversight for production work. The rough framework of a project tends to come fast and feels magical, but filling in the details involves tedious debugging and workarounds for limitations the agent cannot overcome on its own.
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