At every inference, an entity arrives with a feature vector, a model produces a decision, and the serving path moves on. That exchange (entity, features, decision, time) is the only complete record of what the system knew and what it chose, and in almost every production architecture it is discarded the moment it occurs. A system with no record of its own decisions cannot know whether the conditions that shaped them are still valid, cannot observe how its entities evolve, and cannot distinguish its own drift from the world's: each inference is an isolated act, connected to nothing before it.

Entities accumulate meaning over time; the intelligence is in the longitudinal record (which features move together, which dimensions lead, where distributions shift), not in any single snapshot. ARA is the decision data plane. It binds entity, time, features, and decision into a permanent structured record at the moment of inference, giving your AI stack a memory of its own behavior and a single point of truth for what it knew, what it decided, and how it has changed.