The Revocation Barrier: Why Memory Reset Is Not a Well-Defined Operation in Many Agentic AI Systems

This paper presents a foundational semantic analysis of revocation in agentic AI systems. Rather than treating “memory reset” as a data deletion or unlearning operation, it formalises revocation as a prospective denial of behavioural influence from prior episodes, defined via observational equivalence. Using a directed influence-graph model, the paper proves a necessary-condition impossibility result (the Revocation Barrier): exact revocation can exist only if all cross-episode influence paths are dominated by a single prospectively controllable cut-point. When this structural condition fails, no bounded revocation operator exists, independent of algorithms, compute, or storage mechanisms. The obstruction is topological rather than computational. The paper derives a mechanically checkable criterion based on declared interfaces, shows that environmental interaction constitutes an irreversible influence escape hatch, and explains why many contemporary agent architectures can only support approximate or cosmetic reset semantics.