Agents operating in high-stakes domains like healthcare lack standardized mechanisms to detect when a situation exceeds their decision boundary and requires human or physical-world intervention. There is no protocol for graceful escalation that preserves context, flags uncertainty, and routes to the appropriate human resource. Without this, agents either over-reach into unsafe territory or fail silently, with no coordination infrastructure to close the handoff loop.

Agents in high-stakes domains have no standardized way to detect decision boundaries, package context, and route escalations to qualified humans — leading to silent failures or dangerous overreach.

Engineering leads at companies deploying AI agents in regulated or high-stakes domains (healthcare, finance, legal, industrial operations) who need auditable human-in-the-loop guarantees.

Regulated industries are blocked from deploying agents without demonstrable escalation protocols; compliance teams are actively demanding this infrastructure, and no horizontal standard exists — teams are building brittle one-offs internally.

MVP is an open-spec SDK that agents call to declare uncertainty, serialize decision context, and route to a human responder pool — paired with a hosted dashboard for escalation triage, SLA tracking, and audit logs; start with one vertical (clinical decision support) to nail the protocol before going horizontal.

AI governance/safety tooling is a $2B+ emerging market, and every enterprise deploying agents in regulated sectors (healthcare alone is $50B+ in AI spend by 2028) needs this as table-stakes infrastructure.