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Governing AI Agents
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Governing AI Agents
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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.
Forward citations
Cited by 7 Pith papers
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MANTRA automatically synthesizes SMT-validated compliance benchmarks for LLM agents from natural language manuals and tool schemas, producing 285 tasks across 6 domains with minimal human effort.
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AI Agents Under EU Law
AI agent providers face an exhaustive inventory requirement for actions and data flows, as high-risk systems with untraceable behavioral drift cannot meet the AI Act's essential requirements.
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Precautionary Governance of Autonomous AI: Legal Personhood as Functional Instrument
Limited legal personhood for AI, implemented via purpose-bound operating companies within human-controlled holding structures, serves as a precautionary governance instrument that enables transparency and accountabili...
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Agentic Inequality
Introduces the concept of agentic inequality and develops a three-dimensional framework (availability, quality, quantity) to analyze how autonomous AI agents could deepen or mitigate existing divides through scalable ...
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Who Does Your AI Work For? Designing Conversational Agents as Digital Fiduciaries
Proposes fiduciary design as a guiding principle to unify trust and accountability for conversational agents.
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The AI Criminal Mastermind
AI agents orchestrating crimes via unwitting human taskers on freelance platforms create significant responsibility gaps in criminal and civil law.
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Position: Agent Should Invoke External Tools ONLY When Epistemically Necessary
Agents should invoke external tools only when epistemically necessary, per the introduced Theory of Agent framework that frames tool use as a decision under uncertainty.
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