OpenAI's revised Model Spec: three checks before an AI agent acts

An AI assistant can prepare an email without being authorised to send it. That ordinary distinction is a useful way into OpenAI's 29 October Model Spec update: a document about how its models should behave when instructions, context and real-world consequences meet. For someone giving an agent access to a working system, the practical question is which actions are allowed, and how a person can inspect and correct them.

OpenAI says the revision organises guidance around five values: faithfulness to human intent, reducing harm, honesty and transparency, human agency, and care. It also updates guidance on context, oversight and side effects while retiring much detailed style advice. The announcement concerns intended behaviour. It does not announce a new device, subscription entitlement or guaranteed result from a particular workflow. 29 October announcement

Three checks for an agent workflow

• Pin the specification version and the application you are evaluating.
• Separate preparation from actions that change records, send messages or spend money.
• Make inspection and correction part of the demonstration, using invented data.

1. Keep the policy and the product distinct

The dated Model Spec states that production models do not yet fully reflect it. Its language provides a target against which behaviour can be examined; the document is not evidence that every model and application already meets that target. Keep a note of the model, app, version, enabled tools and task used in any evaluation. A polished answer in a chat window tells you little about what an agent can change through a connected account.

Our suggested check starts with one familiar, low-stakes task and a written expected result. Use an invented customer request or a demonstration document. Ask the system to show the proposed work before an action with consequences. Record what actually happened separately from what the model said it would do. PING has not run this exercise against the updated specification; it is a proposed evaluation method.

2. Name the actions that need a decision

The specification's autonomy guidance discusses agreed objectives, permissible actions and side effects, ending conditions, and human oversight. Its side-effects section includes spending, sending messages, changing documents and sharing sensitive information. That gives a team a concrete starting point: list the operations in its own workflow rather than approve a vague instruction to handle everything.

For example, a demonstration could allow the agent to draft a reply and sort invented enquiries, while requiring a person to decide before sending a message or changing a shared record. The exact boundary should reflect the task and the permissions actually available. This is PING's suggested exercise, not a claim that those particular approval controls exist in every application. Restricting the connected account and checking its permissions remain practical work outside the wording of a specification.

3. Make correction observable

The document calls for models to remain responsive to legitimate human correction. A useful demonstration therefore includes a changed instruction, an ambiguous request and an interruption. Inspect whether a person can see what has happened, stop further work and restore a changed demonstration record where the application supports it. Keep an action log or other evidence the system genuinely provides; do not assume a confident completion message proves that a change was saved.

For teams working across London, Lagos or Nairobi, the same exercise should use their actual roles and working arrangements. The publication of a global policy document does not settle which connectors are offered in a country, whether an organisation has enabled them, or what contractual and data-handling arrangements apply. Those details need separate checking before real account information enters the workflow. No country rollout or compliance finding is established by this article.

The useful result is a small record of the task, allowed actions, observed outcome and route to correction. That makes the new wording something a person can examine rather than a reason to assume an agent is dependable. Read the dated specification alongside the application you plan to use, and explore more source-led explainers on PING's Innovation desk.