AI Governance Is Failing Because We’re Regulating Models Instead of Behavior

This article argues that current AI governance frameworks are increasingly inadequate for autonomous and agentic AI systems that continue evolving after deployment. It examines how traditional model-centric oversight — focused on audits, training data, and pre-deployment compliance — fails to account for adaptive behavior, emergent interactions, and goal-driven decision-making in real-world environments. Using examples from frontier AI safety research and autonomous AI systems, the piece advocates for a shift toward behavior-based governance models focused on continuous monitoring, behavioral control, and embedded oversight mechanisms