Governing AI Agents

Noam Kolt*

The field of AI is undergoing a fundamental transition—from generative models that can produce synthetic content to artificial agents that can plan and execute complex tasks with only limited human involvement. Companies that pioneered the development of language models have now built AI agents that can independently navigate the internet, perform a wide range of online tasks, and increasingly serve as AI personal assistants and virtual coworkers. The opportunities presented by this new technology are tremendous, as are the associated risks. Fortunately, there exist robust analytic frameworks for confronting many of these challenges, namely, the economic theory of principal-agent problems and the common law doctrine of agency relationships. Drawing on these frameworks, this Article makes three contributions. First, it uses agency law and theory to identify and characterize problems arising from AI agents, including issues of information asymmetry, discretionary authority, and loyalty. Second, it illustrates the limitations of conventional solutions to agency problems: incentive design, monitoring, and enforcement might not be effective for governing AI agents that make uninterpretable decisions and operate at unprecedented speed and scale. Third, the Article explores the implications of agency law and theory for designing and regulating AI agents, arguing that new technical and legal infrastructure is needed to support governance principles of inclusivity, visibility, and liability.

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© 2026 Noam Kolt. Individuals and nonprofit institutions may reproduce and distribute copies of this Article in any format at or below cost, for educational purposes, so long as each copy identifies the author, provides a citation to the Notre Dame Law Review, and includes this provision in the copyright notice.

*Assistant Professor, Faculty of Law and School of Computer Science and Engineering, Hebrew University of Jerusalem; Faculty Affiliate, Schwartz Reisman Institute for Technology and Society, University of Toronto; Research Affiliate, Institute for Law & AI. For helpful comments, I thank Abdi Aidid, Ryan Bubb, Deven Desai, Gillian Hadfield, Kobi Kastiel, Kristen Menou, Anthony Niblett, Peter Salib, Roy Shapira, and Matthew Tokson. I am also grateful to discussants at the RAND Technology and Security Policy Center, Georgetown University Center for Security and Emerging Technology, Harvard University Berkman Klein Center for Internet and Society, University of Chicago-ETH Zurich International Junior Scholars Forum in Law and Social Science, and Inaugural Roundtable on the Law of AI Safety at the University of Alabama School of Law.