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Auto-Accept Permissions (OpenAI Codex counterpart) ​

What It Means in the OpenAI Ecosystem ​

Unlike Claude Code's built-in UI mechanic for toggling permission prompts, OpenAI's Agents and function calling system require developers to explicitly define which actions are allowed. In practice, the closest equivalent to "auto-accept" is bypassing manual confirmation prompts by pre-approving tool calls or executing model-suggested commands automatically.

With function calling (released June 2023), you can configure your app to automatically invoke functions if the model outputs a tool_call. Whether you show a confirmation dialog to a human or skip it is entirely your design choice.

The Responses API and Agents SDK (2024–2025) expose tool_call events, letting developers decide if they want a review step (human-in-the-loop) or immediate execution. Auto-execution mirrors the "auto-accept" concept but shifts responsibility to developer-side configuration.

Prior to Auto-Accept (Human-in-the-Loop Defaults) ​

Best practice in early Codex / GPT-4 function-calling workflows was to confirm each model-suggested command before execution, especially for shell operations, database writes, or file edits. This human-in-the-loop pattern was emphasized in OpenAI's docs to reduce risk of unsafe model outputs.

With Auto-Execution (Developer-Defined) ​

Developers frustrated by constant approval steps (e.g., approving every file edit or CLI command) often configure their agent runtimes to auto-run trusted functions.

Examples:

  • Code refactoring: Accept and apply file edits without confirmation.
  • Data workflows: Let the model immediately call ETL scripts, given sanitized inputs.
  • Research sprints: Allow the model to fetch URLs or run structured queries continuously without human interruption.

This matches what the Claude community calls "auto-accept mode"—faster, uninterrupted iteration cycles.

Safety Considerations ​

Auto-execution introduces real risks:

  • File modifications may overwrite critical configs.
  • CLI commands could delete data or alter environments if not constrained.
  • Untrusted inputs (e.g., user-provided prompts) could trigger dangerous tool calls.

OpenAI explicitly recommends input validation, allow-lists, and sandboxing when enabling automatic function execution.

Best Practices ​

  • Whitelisting tools: Only allow auto-execution for safe operations (e.g., string formatting, local file read/write in a sandbox).
  • Role separation: Use human-in-the-loop for destructive actions (e.g., rm, DB schema changes).
  • Logging + monitoring: Record all auto-executed actions for auditing.
  • Mode cycling (manual analogy): Developers can implement their own toggle, e.g., safe-mode (confirm everything) vs. auto-mode (auto-run trusted functions).

Execution Flow ​

In the OpenAI Codex / ChatGPT ecosystem, auto-accept permissions = skipping confirmation UIs and directly executing the model's tool calls. It prioritizes speed over cautious verification, but it's entirely controlled at the app layer—not built into OpenAI's UI.

  • Normal mode: Manual review before executing tool calls.
  • Auto mode: Pre-approved tool calls execute immediately.
  • Plan-only mode: Model is restricted to reasoning, with execution disabled.

This mirrors Claude's three-mode cycle, but implemented by developers via SDK event handling rather than by pressing Shift+Tab in a native UI.

  • OpenAI Docs – Function Calling (2023)
  • OpenAI Docs – Agents SDK + Responses API (2025)