Transition Discipline for AI-Assisted Systems: Custody, Authority Ceilings, Revocation, and Trace

AI-assisted workflows become consequential at transitions: an output becomes a recommendation, a recommendation becomes a decision, a decision becomes a record, a record becomes institutional memory, or a memory state influences future action. This paper develops transition discipline as a governance method for making those passages explicit before an AI-produced artifact acquires greater authority than its evidence or role warrants. The method has six required elements: declared custody, decision authority, evidence status, trace, revocation path, and consequence owner. A seventh element, the claim ceiling, limits what may be asserted from the available evidence. The proposal is situated alongside the NIST AI Risk Management Framework, the NIST Generative AI Profile, human-automation research, decision provenance, and the European Union's requirements for effective human oversight of high-risk AI systems. The contribution is not a new general theory of governance. It is a compact runtime checklist for preventing quiet promotion: a generated summary should not silently become the official record, a model score should not silently become authority, and nominal human review should not be counted as oversight unless the reviewer has information, competence, time, and power to intervene. The method is designed for adaptation to domain-specific law, policy, and risk.