Not every model call needs to generate text. Routing is often a small decision hidden inside a large agentic system, yet many systems ask a decoder language model to generate that choice token by token. A decoder router is not inherently wrong. It is useful when policy is open-ended or the route set changes rapidly. But a stable, finite route set creates a classification-shaped problem that should be measured as one.

The practical opportunity is a decision layer: explicit candidate routes, typed scores, a threshold or abstention policy, and deterministic software branches. The novelty is a new system primitive, not a rediscovery of classification. Jev is a useful public example of a typed, probabilistic decision interface; its proprietary architecture and training objective are not public.

Efficiency and accuracy must be demonstrated, not assumed. Compare decoder routing, an encoder-plus-head baseline, and a structured-decision implementation on the same routes, data, policies, and evaluation metrics.

The dominant mental model of modern AI is generative: provide a prompt, then let a decoder predict the next token. That is an extraordinary capability, but it is not the only useful form of machine intelligence. Many actions inside an agentic system are bounded operational questions: Which specialist should receive this case? Is the evidence sufficient to proceed? Should the system act, escalate, or abstain?