From Herding Machines to Autonomous Agents: A Taxonomy of AI-Driven Flash Crash Mechanisms and the Regulatory Gap

Abstract

Artificial intelligence (AI) is reshaping financial markets—accelerating execution, automating allocation, and concentrating analytical capacity within a shrinking set of foundational models. Prior work has documented AI’s contribution to instability through algorithmic herding and high-frequency volatility, but the literature lacks a coherent classification of the mechanisms through which AI triggers catastrophic, self-reinforcing market dislocations, or “flash crashes.” This prospective review proposes a three-category taxonomy: (1) endogenous algorithmic herding crashes, driven by correlated model behavior; (2) exogenous model error cascade crashes, in which AI system failures propagate across interconnected venues; and (3) adversarial generative AI (GenAI) disinformation crashes, in which fabricated narratives trigger automated trading responses. The taxonomy is further motivated by the structural parallel between contemporary AI model homogeneity and the homogenization of Value-at-Risk (VaR) models before the 2008 crisis—a link recently formalized in the modeling literature. The analysis is extended to the emerging frontier of agentic AI—autonomous systems capable of multi-step planning and inter-agent interaction—which introduces qualitatively new systemic risks that existing regulatory frameworks are unprepared to address. The October 2025 cryptocurrency liquidation cascade, which liquidated over $19 billion within 24 h, serves as the primary empirical case study. The article concludes with policy recommendations on model-diversity mandates, real-time AI trading surveillance, adaptive circuit-breaker design, and cross-regulatory coordination on GenAI financial disinformation.