Governing State Transitions in Autonomous AI Systems: Why Action Authorization Is Insufficient for Agentic AI

This research note proposes state-transition admissibility as a governance unit for autonomous AI systems. It argues that identity and action authorization, while necessary, are insufficient when an individually valid action can produce an inadmissible cumulative or persistent consequence. The proposed model evaluates a candidate action against the current state, authorized intent, delegated authority, constraints, evidence, history, and expected effects. After execution, the observed effect is reconciled against the authorized transition envelope. The central thesis is: an authorized action does not necessarily produce an authorized consequence. This version does not claim universal AI safety, first-in-world novelty, or that identity, intent, policy enforcement, stateful authorization, or auditability are individually novel. The next proof obligation is executable and reproducible evaluation of governed state transitions across agentic systems. Canonical publication: https://oruvo.ai/research/governed-state-transitions/