What this is
This week, Part 2 of a "Codex Source Code Walkthrough" circulated widely among Chinese developers on Juejin. Codex is OpenAI's command-line AI coding tool — you chat with the AI in your terminal and let it read and write code directly; its underlying code (codex-rs) is open-source. The article dissects the most basic question: after a user hits Enter, what exactly does the AI run internally?
The answer is more complex than expected. The system first determines whether this is "a new question or a follow-up to the previous turn," then has the model reason (let the AI think about the next step), then decides whether to invoke a tool. Before invoking a tool, it must pass five approval gates: execution policy validation, approval popups, sandbox isolation (preventing the AI from accidentally deleting files), concurrency control, and result collection. The entire flow involves dozens of files and thousands of lines of coordinating code.
Our judgment: when an AI product is worth dissecting "source-code-style," it has already moved from the stage of "can it work" to "how to make it work reliably."
Industry view
The optimistic side sees this as a sign that Agents (AI assistants capable of autonomously completing multi-step tasks) are entering the deep waters of engineering. OpenAI laying the code bare amounts to an admission that an Agent isn't "just plug in ChatGPT and you're good to go" — it's a system that needs to be understood, customized, and governed.
But there are dissenting voices too. The more complex the source code, the further Agents still are from "out-of-the-box usability." A senior architect commented: "Five approval gates look safe, but they're compromises piled up from countless edge cases (exceptional situations). It's more realistic for ordinary enterprises to use the cloud-hosted version directly; deploying it yourself is digging your own pitfall." Others caution that open-sourcing the code is a double-edged sword for OpenAI — competitors can copy it, but users' trust in "why the AI made this decision" will be higher.
Impact on regular people
For enterprise IT: deploying an Agent isn't installing software — it's onboarding a new employee who needs KPIs, logs, and permission boundaries. Approval workflows, behavioral auditing, and compliance trails must be designed in advance; otherwise, when something goes wrong, you won't be able to trace the responsibility chain.
For individual careers: people using AI to write code and reports are splitting into two camps. One group understands the Agent's internal logic and can direct it to perform complex tasks; the other only knows how to press Enter. The premium for the former will grow increasingly obvious.
For the consumer market: future AI products will be more transparent, but also more complex. When evaluating them, don't just look at the demo — ask: what decisions did it make? Who approved them? Who's accountable when it fails?