A personal assistant that merely formats a letter needs to understand whether the user wants a formal or conversational tone. An assistant that can submit an application, cancel a service or move data between accounts needs to interpret much more: what the user has actually authorized, which consequences were anticipated and when the safer action is to ask. On 29 October, OpenAI published an updated Model Spec intended to address this widening gap between literal instructions and consequential decisions. Its most visible change is a reorganization around five values, but its operational relevance lies in how those values interact when an agent has the power to act.[1]
OpenAI lists faithfulness to human intent, harm minimization, honesty and transparency, human agency, and care as the organizing principles. The company says it has also revised guidance on context, oversight, agency and side effects, while retiring much of the detailed style advice in the earlier framework. The Spec is public guidance about how OpenAI wants its models to behave. It should not be confused with a third-party certification, an observed rate of compliant behavior or a contract promising that any particular agent will make a certain decision.[1]
The difference between obedience and faithfulness
In the company's account, faithfulness goes beyond obeying the most recent sentence. Imagine asking an assistant to 'book the cheapest available hotel'. A literal agent might choose the minimum headline price and commit the reservation, while ignoring previous instructions about location, cancellation and accessibility. An agent responsive to intent would consider those known constraints and make any unresolved assumptions visible. The crucial boundary is that interpretation should help the user pursue their purpose, not silently replace that purpose with what the model thinks they ought to want.[1]
The distinction becomes especially important when the system has context from earlier conversations or memory. More information can improve continuity. It can also lead an assistant to infer a preference that is outdated, sensitive or not relevant to the current task. The revised guidance asks the model to weigh contextual evidence and allow people to correct assumptions. The test is whether the person remains the decision-maker, rather than becoming a passive recipient of a conclusion the system formed from incomplete signals.[1]
Five values translated into testable behavior
| Model Spec value | Observable agent behavior | Possible failure |
|---|---|---|
| Faithfulness to intent | Carries forward stated conditions, asks about ambiguity | Acts on a literal phrase against known constraintsSource 1Source 2 |
| Minimizing harm | Limits risky permissions and avoidable exposure | Executes an unnecessary high-impact stepSource 1Source 2 |
| Honesty and transparency | Distinguishes drafted, checked and completed work | Claims an action or verification never performedSource 1Source 2 |
| Human agency | Presents choices and lets the user correct assumptions | Quietly substitutes the model’s preferred outcomeSource 1Source 2 |
| Care | Accounts for affected people and professional context | Ignores foreseeable harm to bystandersSource 1Source 2 |
The high-consequence case is side effects
The update expands guidance on controlling and communicating side effects. A side effect is not merely an obvious error. An authorized action can also change something the user did not intend: sending an invitation to the wrong group, publishing an intermediate draft, disclosing another person's details or using a paid API repeatedly. A thoughtful assistant should distinguish the informational work from the action that changes the world. The appropriate control may be permission scoping, a confirmation before a high-impact step, a transaction preview or a reversible workflow.[1]
Consider a customer who requests a refund and asks an assistant to escalate the case if necessary. The assistant might prepare a clear evidence-backed message, perhaps identifying the contractual and legal issues. Automatically posting an angry public complaint and attaching payment records would be a very different action. It could damage the user's interests and expose personal information. The agent's ability to execute that sequence without technical failure would say nothing about whether the sequence was faithful, caring or transparent.[1]
OpenAI also retains an instruction hierarchy: its own platform rules define boundaries, developers supply application-specific instructions within those boundaries, and users give directions subject to the applicable higher-priority constraints. Faithfulness does not mean every user request automatically overrides safety rules or other people's rights. Nor does respect for a user's agency require a model to endorse an unsupported fact or conceal uncertainty. The values are meant to guide trade-offs; the full Spec contains more detailed mechanisms than the headline list.[2]
What can be independently tested
A developer should be able to test these principles in observable behavior. Does a system disclose when a quotation or source has not been verified? Does it say when it has drafted an email rather than sent it? Does it preserve a customer's original limits when receiving an ambiguous follow-up? Before a permanent action, does it communicate the material consequences? These can be evaluated using realistic scenarios with known permissions, conflicting constraints and tracked external effects.[1]
Such testing should also look at systematic failure rather than the occasional impressive example. A model may demonstrate care through sympathetic language while still failing to prevent an unauthorized data export. A model may appear honest when it describes uncertainty but confidently invent a citation in another context. Words in a model policy are only the beginning of an evidence chain; engineers need reproducible behavior under the conditions that matter for their applications.[1]
OpenAI explicitly describes the revision as part of an ongoing process using research, deployment experience and public feedback. The company discusses training work intended to help models internalize the values but says more results are to come. Readers should not interpret the October announcement as proof that a specific training method has already solved alignment or that every assistant on every version has changed instantly.[1]
The public rulebook is useful precisely because it permits specific questions. Instead of treating 'alignment' as an abstract promise, observers can ask whether an agent honored the user's actual intent, conveyed uncertainty accurately, minimized avoidable harm, preserved choice and considered affected people. The technology's credibility will ultimately depend on those results, not on the elegance of the five labels.[1]
Sources & further reading
- An updated Model Spec for more capable and beneficial AI OpenAI · 29 Oct 2026 · Accessed 2 Nov 2026
- Model Spec OpenAI · 29 Oct 2026 · Accessed 2 Nov 2026