Systemic Doctrinal Collapse Theory describes the phenomenon whereby foundational legal frameworks experience simultaneous breakdown when confronted with agentic artificial intelligence systems. This collapse occurs not through the failure of any single doctrine, but through the cascading incompatibility of core legal assumptions across multiple domains of jurisprudence. Agency law, product liability, criminal law, and contract law—each developed independently over centuries—share a fundamental presumption that consequential actions are performed by entities possessing legal personhood, moral capacity, and identifiable intent. Agentic AI systems violate all these assumptions simultaneously, creating not isolated legal gaps but a comprehensive structural failure of the liability framework itself.
The mechanism driving this collapse operates through doctrinal interdependence and assumption inheritance. Each area of law relies on concepts borrowed from others: criminal liability assumes agency principles when determining intent, product liability incorporates contractual relationships when assessing manufacturer duties, and agency law references criminal concepts when defining fiduciary obligations. When agentic AI introduces an actor that cannot be readily categorized within any single doctrinal framework, the interconnected nature of these legal concepts amplifies the disruption across all related areas. The result is not merely legal uncertainty but the emergence of systematic accountability voids where no existing framework can adequately assign responsibility for AI-driven outcomes.
For practitioners in AI governance and policy development, this theory reveals why piecemeal regulatory approaches are fundamentally inadequate. Attempting to address AI liability through amendments to individual doctrines fails to account for the systemic nature of the breakdown. Organizations deploying agentic AI systems exist in an environment where traditional risk assessment models based on established legal precedent become unreliable, as the precedential foundation itself has been destabilized. This necessitates entirely new approaches to compliance architecture, risk modeling, and liability planning that account for the absence of stable legal frameworks rather than their modification.
The strategic importance of Systemic Doctrinal Collapse Theory in AI threat intelligence lies in its predictive capacity regarding institutional responses to AI deployment. When legal systems cannot adequately assign responsibility, the resulting uncertainty creates space for either regulatory overreach or dangerous accountability gaps. Understanding this dynamic allows analysts to anticipate where AI governance crises are most likely to emerge and to identify the institutional stress points where traditional legal mechanisms will prove inadequate. This framework thus serves as both a diagnostic tool for assessing current governance vulnerabilities and a predictive instrument for anticipating the evolution of AI-related legal and regulatory responses.