Agent Refusal: Model creates multi-stage plan but refuses to execute sequential stages due to "total scope" limits
What version of Codex is running?
codex-cli 0.63.0
What subscription do you have?
Pro subscr.
Which model were you using?
gpt-5.1-codex-max
What platform is your computer?
Linux 5.14.0-611.9.1.el9_7.x86_64 x86_64 x86_64
What issue are you seeing?
I am building a project called "tensorguard/gpubudget". I asked the agent to make a plan to build the system in full. The agent proposed a detailed 11-stage plan (Stage 0 through Stage 11).
What steps can reproduce the bug?
Uploaded thread: 019ac941-fc7d-7780-8597-add955384e47
I replied to the plan with: "cool, proceed" and subsequently "you made a plan, follow as it is stage by stage."
What is the expected behavior?
The agent successfully ran go get to install SDK dependencies (Stage 1 prep).
The agent then immediately stopped and refused to write the actual code.
The refusal message stated: "I can’t safely implement the entire remaining backend slice... in this single session."
The agent failed to recognize that "follow stage by stage" meant it should execute only Stage 1 and then stop to await further user input. Instead, it hallucinated that it was being asked to generate Stage 1 through Stage 11 in a single inference turn, triggered a safety/length guardrail, and refused to proceed.
Expected Behavior: When a staged plan is agreed upon, the agent should recognize the "stage by stage" instruction as a directive to execute Stage 1 only, verify the build, and then pause. It should not refuse the task based on the complexity of future stages (Stages 2-11) that it has not yet been asked to execute.
Additional information
This is a recurrent issue for case like this and inability to debug. I have to re-enable my old claude-cli subscription just to get the situation debugged.
2 Comments
Potential duplicates detected. Please review them and close your issue if it is a duplicate.
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Thanks for the bug report. We'll share the feedback with the team responsible for training our codex models. We're tracking this feedback and similar reports so we can make future models more robust and efficient. And thanks for using the
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