Decision layer
A dedicated component between AI judgment and organizational action that decides whether the work should continue, separate from the part of the system that does the work.
A decision layer is an explicit layer between what an AI system concludes and what the organization does about it. Instead of hiding a judgment inside a long answer, a decision-native interface exposes it: a question, a bounded set of outcomes and some indication of how sure the system is.
That separation matters most for agents. A dedicated layer can separate doing the work from deciding whether the work should continue: one part acts, another part checks, routes or stops. It gives uncertainty somewhere to go, supports abstention and makes the boundary between model output and authority visible and ownable.
What it requires
- A threshold policy with a named owner
- Measured calibration on real outcomes
- Awareness of decision behaviour as a dependency
The Decisions API is an example of this pattern.
Read more in OpenAI Decisions API turns probability into policy.
Related terms
Decisions API
OpenAI's announced decision layer: developers pose a question with a fixed set of possible answers plus text or image context, and the model returns a choice, for classification, routing or an agent's next action.
Threshold policy
The explicit rules that turn a model's probability or score into an action — reject, send to a human, or act autonomously — owned and reviewed separately from the model itself.
Abstention
A deliberate option for an AI decision system to decline to decide and return uncertain or high-consequence cases to a person, rather than forcing every input into yes or no.
Decision behavior
How a particular AI system tends to decide across cases — where it leans, abstains or errs. Once embedded in operational thresholds, it becomes a dependency that makes switching vendors harder.