Artificial Id: Drive and Persistent Alignment in Agentic AI

As agentic AI shifts from bounded execution toward continuous systems that carry consequential state across tasks, relying on external stopping rules and manual harnesses becomes brittle. This paper proposes an "artificial id," an adaptive internal drive that decides whether an agent should continue, stop, or pivot. In a minimal virtual Petri-dish setup, a compact controller lacking explicit behavioral targets developed functional control purely via differential persistence. The authors highlight that the exact persistence that enables cross-task autonomy also allows corrupted state and misalignment to endure, reframing alignment from single-trajectory evaluation into an ongoing systemic property.