A middleware layer that classifies AI agent actions by risk tier and only interrupts humans for genuinely high-stakes operations, eliminating approval fatigue that causes operators to click through dangerous actions on reflex without reading them

Approval prompts in AI agent workflows have become a security liability, not a safety feature. When a coding agent asks for 40 approvals per session, operators develop a reflex to approve without reading, meaning truly dangerous operations (DROP TABLE, publish to production, charge a card) are buried in a stream of trivial approvals and get clicked through. Anthropic's own research on Claude Code auto mode identified this as the problem they built auto mode to solve: classify risky actions before execution, deny dangerous operations, allow the session to continue. Multiple security researchers (Blake Crosley, Molten.bot, Approval Fatigue research) labeled this a documented security bug in 2026. EU AI Act Article 14 and NIST AI RMF require demonstrable trained, measurable human oversight not just interrupt prompts. A risk-tiered interrupt router that sits between the agent and its execution environment, auto-approves read-only and low-blast-radius operations, surfaces only irreversible/financial/privileged-escalation actions with plain-language explanations and 30-second countdown, and logs every auto-approved action for audit. Gartner predicts 40% of enterprise apps will include AI agents by end of 2026; approval fatigue is the #1 UX friction blocking enterprise adoption.