Agent control frameworks only support binary deploy/kill states, with no intermediate mechanisms for throttling, supervised execution, conditional operation, or escalation-owned degradation. Operators and safety systems cannot express nuanced governance policies—slow down, explain yourself, continue under supervision—that mature regulatory and safety frameworks require. This forces termination as the only intervention option, making proportionate responses to rogue or degraded behavior architecturally impossible.

Agent frameworks only support deploy or kill, forcing operators to terminate agents instead of throttling, supervising, or degrading them gracefully — making proportionate safety responses architecturally impossible.

Engineering leads at companies running autonomous AI agents in production (DevOps, fintech, customer ops) who need safety governance beyond binary on/off.

Enterprises adopting agents are blocked by compliance and safety teams who won't approve production deployments without graduated controls; this is the missing primitive that unblocks six-figure agent infrastructure deals today.

Open-source SDK/middleware that wraps agent runtimes (LangGraph, CrewAI, AutoGen) with a state machine exposing states like full-auto, throttled, supervised, explain-before-act, and frozen — plus a lightweight policy DSL and dashboard for real-time state transitions; MVP targets one framework in ~4 weeks.

Subset of the $5B+ AI infrastructure market; every company running agents in production needs governance controls, comparable to early APM/observability TAM.