What this is

The author of the open-source plugin codex-plugin-cc posted this week: when Claude Code (an AI programming assistant) writes code and Codex (OpenAI's code review assistant) reviews it, the "ALLOW" prompt that pops up on screen — meaning "allow end" — can come from two scenarios: either the review found no blocking issues, or this round didn't actually modify any code. The author ran five manual inputs through offline testing (code verification without internet access); all were parsed by the rules. But he emphasized that the parser only recognizes "what code was changed in the immediately previous round" and will not proactively re-check issues that piled up earlier.

In other words: "the AI says we can wrap up" ≠ "the work is actually done." The division of labor between the two Agents (AI programs that autonomously complete multi-step tasks) is: the reviewer only flags problems and reports results; fixing, verification, and archiving are the implementer's responsibility. Converting "please fix" into a task the implementer can verify — with a clear original agreement, modification scope, validation input, and returned evidence — is what makes this workflow actually run.

Industry view

The author drew a clear line: the above conclusion only applies to the implementation version he checked (commit db52e28f) and cannot be directly carried over to all Codex clients. AI Agent behavior is highly dependent on specific implementation and prompts (the instruction text given to AI); cross-product analogies tend to backfire, and the semantics of "allow end" may differ entirely across vendors and plugins.

A deeper issue was glossed over: when enterprises introduce "dual-Agent collaboration" workflows, "process passed" and "result qualified" are routinely conflated. One Agent reports "no problem," and the other Agent moves on to the next step — but nobody actually verified the code, documentation, or decision itself. This is an organizational-level accountability vacuum, not something a tool upgrade can solve. Treating AI mutual review as "already accepted" is the most insidious pitfall when enterprises adopt AI.

Impact on regular people

  • For enterprise IT: Before introducing AI programming combinations, sort out who owns final acceptance. If only AI cross-checks each other without human review of key deliverables, something will eventually break in production.
  • For individual professionals: When collaborating with AI tools, build one habit — distinguish "process completed" from "task completed." After letting AI self-check, run through the core results yourself; don't skip this step for the AI's sake.
  • For the consumer market: When you see pitches like "fully automated AI," "AI mutual review," or "AI gatekeeper," ask one more question: who is responsible for the final result? Without a human backstop, "automation" just shifts responsibility to the user.