Interpretation benefits from intelligence.
Human requests rarely arrive as perfectly typed API calls. They contain incomplete context, implied goals, and ambiguous language. A capable model can classify the work: research, coding, communication, infrastructure, finance, or some other operating category. It can estimate complexity and recommend a route.
Permission is a different question.
Understanding what a task is does not answer whether the system may execute it. Sending a message, deploying code, purchasing something, and reading public documentation carry different side effects. ASCEND therefore separates classification from authority. The model can recommend; deterministic policy decides what is permitted.
The Decision Fabric connects the two.
The Decision Fabric evaluates task class, context scope, model and tool requirements, risk level, approval rules, and the appropriate execution route. A research request may move directly into a read-only route. A production deployment may select the coding route but stop at a human approval gate before any external change occurs.
Capability can improve without permission expanding.
This separation matters as models become stronger. A better model can improve classification, planning, and execution quality without automatically receiving broader authority. The operating envelope remains an explicit system decision instead of an accidental consequence of model capability.