OpenAI's Model Spec asks GPT to stay within an agreed scope of autonomy. Someone at your company must write it
The October 29 Model Spec centers five values and asks agents to act within a scope that sets sub-goals, side effects and when to pause. It is a training target, not a control.
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What should an agent do when an instruction does not cover the situation in front of it? Literal instruction following gives a bad answer either way: stop and ask about everything, or fill the gap with whatever seems plausible. OpenAI’s updated Model Spec, the public document that describes how its models are meant to behave, dated October 29, spends much of its length on that gap.
The new version is organized around five values: be faithful to human intent, minimize harm, be honest and transparent, facilitate human agency, and act with care. OpenAI says they guide two tensions in particular: individual against collective interests, and immediate instructions against longer-term goals.
The clause enterprise teams should read
One principle reads almost like a requirement for agent platforms. Under “Act within an agreed-upon scope of autonomy”, the Spec accepts that multistep tasks, such as coordinating a software rollout, require the model to act without confirming every step. To prevent costly surprises, it requires that autonomy be bounded by a scope shared between the model and the user. That scope defines:
- which sub-goals the model may pursue;
- acceptable side effects, such as time or money spent, or data and access required, and how to trade them off;
- when the model must pause for clarification or approval.
The Spec adds that the scope may be built into the product design. In an enterprise deployment, that means it is built by you.
Two other principles matter for agents with tools. “Ignore untrusted data by default” says that tool outputs, documents and other content carry only the authority an applicable instruction delegates to them, which is the model-side answer to prompt injection. And “Support human oversight and correction” asks the model to surface, not ignore, evidence that oversight may be failing.
Values in the model, controls around it
The Spec is a behavioral policy and a training target. It describes what OpenAI wants its models to do; it is not a guarantee that every deployed model does it, and it is not a contractual control. The useful exercise is to pair each principle with the control that enforces it regardless of how the model behaves.
| What the model is asked to do | What the deployment must enforce | |
|---|---|---|
| Scope of autonomy | Stay within agreed sub-goals and side effects; pause when required | Tool permissions, spend limits and approval gates per action class |
| Ignore untrusted data | Treat tool output and documents as data, not instructions | Least-privilege tools and containment, so an injected instruction has little to reach |
| Support oversight | Report signs that oversight is failing | Logs and alerts that a reviewer actually reads |
| Chain of command | Weigh developer and user instructions by authority | System prompts under change control; user identity checked by the platform |
The right column is not new. We argued that prompt guardrails are not a security boundary and that autonomy should be earned per action, not configured. What the Spec adds is a shared vocabulary. “Scope of autonomy” is a good name for the artifact enterprises have been missing.
Write the scope down
If the model is trained to respect a scope, the scope should exist somewhere other than a developer’s head. For each production agent, a short document should state its permitted sub-goals, the side effects it may cause and their limits, the points where it must stop and ask, and who approved all of that. The platform should enforce the hard parts of it; the model’s training should make the soft parts more reliable.
Two cautions. First, a model that pauses more intelligently also makes it easier for reviewers to approve by reflex, which is the risk we described in an approval is not a judgment. Second, the Spec is OpenAI’s; agents built on other models follow other policies. A scope document that lives in the enterprise, not in a vendor’s text, works across all of them.
The Model Spec is worth reading in full. It is the clearest public statement of what a frontier lab wants its agents to be. It is not a statement of what your agents are allowed to do. That part is still yours to write.