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  "path": "/t/feature-request-sentinel-mode-for-codex-chatgpt-enterprise-governed-ai-agent-operations-with-dry-runs-approvals-and-audit-logs/1380493#post_1",
  "publishedAt": "2026-05-08T10:33:02.000Z",
  "site": "https://community.openai.com",
  "textContent": "I would like to suggest an enterprise-focused concept for Codex / ChatGPT Enterprise: a “Sentinel Mode” or “AI Agent Operations Control Center.”\n\nThe core idea is simple:\n\nAs AI agents become more capable, enterprises will not only need agents that can write code or execute tasks. They will need a safe operational layer around those agents.\n\nFor many companies, the blocker will not be “Can the AI do the task?”\nThe blocker will be:\n\n  * Can we control what the agent is allowed to do?\n\n  * Can we dry-run actions before execution?\n\n  * Can admins approve or reject risky changes?\n\n  * Can every action be logged and audited?\n\n  * Can destructive actions require explicit approval?\n\n  * Can agents operate through approved playbooks instead of arbitrary commands?\n\n  * Can security teams define policy boundaries?\n\n\n\n\nA possible MVP could include:\n\n  1. **Agent Playbooks**\nPredefined workflows for common enterprise tasks, such as:\n\n     * code review support\n\n     * CI/CD troubleshooting\n\n     * log analysis\n\n     * vulnerability investigation\n\n     * environment health checks\n\n     * documentation updates\n\n     * incident summary generation\n\n  2. **Dry-Run First Execution**\nBefore an agent changes anything, it produces:\n\n     * intended action\n\n     * files/systems affected\n\n     * expected result\n\n     * risk level\n\n     * rollback plan\n\n     * verification steps\n\n  3. **Approval Queue**\nAdmins or assigned reviewers can approve, reject, or request changes before execution. This would work especially well with mobile supervision and notifications.\n\n  4. **Policy Engine**\nEnterprise admins define boundaries:\n\n     * allowed repositories\n\n     * allowed commands\n\n     * blocked commands\n\n     * allowed environments\n\n     * approval requirements\n\n     * high-risk action rules\n\n     * data handling rules\n\n  5. **Audit Logs**\nEvery agent action should generate a clear audit trail:\n\n     * who requested it\n\n     * what the agent proposed\n\n     * what was approved\n\n     * what executed\n\n     * what changed\n\n     * whether verification passed\n\n     * how rollback can be performed\n\n  6. **Local / Tenant-Side Runner**\nFor sensitive enterprise environments, the execution layer could run locally or inside the customer’s tenant, while ChatGPT/Codex provides planning, reasoning, and review. This would help with security, compliance, and trust.\n\n\n\n\nWhy this matters:\n\nA lot of developers already treat Codex like an async engineering teammate. But enterprises need more than raw capability. They need governance, approval, observability, and predictable safety controls.\n\nIn other words, the future enterprise question is not only:\n\n“Can AI agents do work?”\n\nIt is:\n\n“Can AI agents do work safely, with human control, policy boundaries, and auditability?”\n\nI think this kind of Sentinel / AgentOps layer could become a major part of enterprise AI adoption. It would make Codex and ChatGPT Enterprise easier to trust in real operational environments, especially for DevOps, platform engineering, security teams, and regulated companies.",
  "title": "Feature Request: Sentinel Mode for Codex / ChatGPT Enterprise — Governed AI Agent Operations with Dry-Runs, Approvals, and Audit Logs"
}