Agentic AI challenges current stop mechanisms and legal controls
The Law of Stop: Interruptibility, Injunctions, and the Governance of Agentic AI
Computers and SocietyArtificial Intelligence
Summary
Stopping advanced AI systems quickly and effectively is harder than just pressing a button. The authors explain that stopping AI involves technical tools and legal authority, but agentic AI spreads control across many parts, so stopping one part might not stop the whole system. They looked at 1,400 AI problems and found that most lacked proper stops, mostly due to legal issues. Their work suggests new layered laws to improve how and when AI systems can be shut down safely.
What this means in practice
- •For ai policy makers: Design legal frameworks that require and coordinate AI stop mechanisms and define when suspension of AI operations is necessary.
- •For ai compliance teams: Develop procedures ensuring AI systems include coordinated stoppage features that meet emerging binding requirements.
A position paper. It proposes an approach and reports no results.
Authors
Oren Perez
Abstract
On June 12, 2026, the U.S. government ordered Anthropic to bar foreign nationals from two of its most capable models within ninety minutes. Unable to sort users by nationality in that time, 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 AI-specific regulation. The EU AI Act requires that high-risk systems be capable of interruption "through a 'stop' button or a similar procedure," and a bill introduced in Congress in July 2026 is titled the AI Kill Switch Act. Yet interruption is not simply a technical artifact, a red button; it is an institutional practice. This Article develops a theory of stop along four dimensions: technical affordances, interruption authority, epistemic triggers, and epistemic standing; and four shutdown paradigms: simple (escalator), sequenced (process plant), networked (railway), and distributed (agentic AI). Agentic AI exposes a mismatch between those mechanisms and distributed agency: control is divided, a stop at one point may leave the activity running elsewhere, and the system may resist being halted. An original coding of 1,400 AI incidents, by two language models from rival laboratories under a pre-specified protocol, finds no stop in roughly 80% of the 1,213 retained; where no usable stop existed, the missing element was legal rather than technical four times in five. A survey of thirty-nine AI governance instruments finds the same gap: only seven contain binding stopping requirements, and none says how a stop should be coordinated or when operation may resume. The Article proposes a layered law of stop: emergency authority to interrupt at the infrastructure layer, enforceable access for regulators and independent evaluators to the evidence a stop must rest on, and safeguards for when a stop fails.