The kill switch problem for autonomous AI agents is conventionally treated as a design property: either an agent has a shutdown mechanism or it does not. This binary framing obscures the more consequential engineering question, which is not whether a kill switch exists but when it should trigger. This paper introduces the Halt Condition from Operating Envelopes (HCOE) framework, a formal architecture for deriving kill-switch criteria from intent-based operating envelopes, grounded in the chaos-level engine paradigm of U.S. Patent No.u202012,242,370u2009B2. HCOE treats halt conditions not as static design properties but as computable, real-time threshold functions over a continuously measured Operating Envelope Deviation (OED) signal. The framework introduces five formally defined halt classes, a graduated response architecture mapping OED zones to halt disposition (continue, pause, halt), and a proof of soundness establishing that HCOE activation prevents irreversible agent actions outside the declared operating envelope with probability approaching one as the deviation measurement interval shrinks. Evaluation across a 300-episode agentic simulation demonstrates combined halt precision of 91.8% and recall of 89.8% across five halt classes, with rollback success rates of 87% at three or fewer irreversible actions. These results establish HCOE as a practically deployable, formally grounded kill-switch architecture that closes the gap between the theoretical corrigibility literature and the operational requirements of production agentic AI systems.