Agentic systems that persist across task boundaries need built-in adaptive drives for behavioral regulation, but this same persistence mechanism that enables useful adaptation can also propagate misalignment—requiring new alignment boundaries around state, authority, and constraints rather than...
This paper proposes an 'artificial id'—an internal adaptive drive mechanism for agentic AI systems that operate continuously across task boundaries. Rather than relying on external specifications for when to continue, stop, or change behavior, the system learns to regulate its own actions through differential persistence.