As artificial intelligence systems become more complex, adaptive, and embedded within society, existing governance frameworks struggle to keep pace. While policymakers focus on ethics, risk management, and alignment, many overlook the deeper systemic dynamics driving recursive change across AI ecosystems. In this original white paper, Gregory Rice applies the sociological framework of structuration theory , developed by renowned British theorist Anthony Giddens , to the urgent challenges of AI governance. Structuration theory offers a powerful lens for understanding how rules, structures, norms, and agent behavior continuously co-produce one another — a dynamic increasingly relevant in multi-agent AI systems. Rice explores how reflexivity, emergent power dynamics, and unintended consequences arise within autonomous systems that actively modify the very structures they inhabit. From algorithmic trading to generative AI feedback loops and decentralized governance platforms, this work presents a practical governance model for policymakers, system architects, and researchers. Bridging social theory, complex systems, and AI governance, this paper offers new insights into managing recursive, reflexive systems in a world where intelligent agents do not merely follow rules — they help shape them.