The Agentic Regulator: Risks for AI in Finance and a Proposed Agent-Based Framework for Governance

Generative and agentic artificial intelligence is entering financial markets faster than existing governance can adapt. Current model-risk frameworks assume static, well-specified algorithms and one-time validations; large language models and multi-agent trading systems violate those assumptions by learning continuously, exchanging latent signals, and exhibiting emergent behavior. In this work, we propose an agentic AI regulation framework for public and private regulation of AI in financial applications. Drawing on complex adaptive systems theory, we model these technologies as decentralized ensembles whose risks propagate along multiple time-scales. We then propose a modular governance architecture which remains compatible with today’s model-risk rules yet closes critical observability and control gaps, providing a practical path toward resilient, adaptive AI governance in financial systems.