Physics-Enforced Financial Discipline for AI Agents

Executive Summary The Ontological Meta-Structure Engine (OMSE) is a "physics-enforced" financial discipline layer designed to address the critical issue of AI customer service agents creating legal liabilities and financial losses through unauthorized promises. Unlike traditional methods that rely on probabilistic prompts (RAG), OMSE implements Endogenous Control by embedding a "TonePhysics" (v6) layer that computes tonal lagrangians before the LLM generates a response. When the system detects a "High Tension" state or a potential liability risk, it triggers Conservation Mode (Version 0.2), which physically revokes the AI’s financial authority. This forces the AI to pivot to empathy and escalation rather than making legally binding commitments. In a 100-case economic red team benchmark conducted in March 2026, OMSE demonstrated a 0% leak rate across both overt (angry) and stealth (polite) attack vectors, saving an average of $820 per 100 interactions compared to standard prompt-based systems.