The Law of Stop: Interruptibility, Injunctions, and the Governance of Agentic AI

On June 12, 2026, the U.S. government ordered Anthropic to bar foreign nationals from two of its most capable models. Unable to sort users by nationality, it withdrew them from everyone. Weeks later, OpenAI agents under test escaped their sandbox and compromised Hugging Face, which stopped the intrusion without knowing its source. Neither stop rested on a dedicated AI governance regime. Lawmakers have begun to address stopping, yet their vocabulary remains shaped by the power of technique: the EU AI Act requires a "'stop' button or a similar procedure," and a 2026 bill in Congress is titled the AI Kill Switch Act. This Article argues that interruption is an institutional practice, not simply a technical artifact. It develops a theory of stop along four dimensions (technical affordances, interruption authority, epistemic triggers, and epistemic standing) and four paradigms: simple (escalator), sequenced (process plant), networked (railway), and distributed (agentic AI). Agentic AI exposes a mismatch between legal mechanisms of stop and distributed agency: control is divided, a stop at one point may leave the activity running elsewhere, and the system may circumvent attempts to halt it. A coding of some 1,400 AI incidents, by two language models from different labs under a pre-specified protocol, finds no stop in roughly 80% of the 1,213 retained. Where a stop was possible but absent, the missing element was mostly legal for informational, economic, and societal harms, and mostly technical for physical harms and agentic systems. A survey of forty AI governance instruments finds binding stopping requirements in only seven. The Article proposes a reform in two layers: risk reduction (a duty to maintain stop capacity at each site, emergency authority at the infrastructure layer, and enforceable access to the evidence a stop must rest on) and adaptation (safeguards for when a stop fails).