mlsanityData to 14 Oct 2026

AI Agents and the Law

arXiv 2508.08544 ↗First posted 12 Aug 2025cs.CY cs.AIcited by 9

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

As AI becomes more "agentic," it faces technical and socio-legal issues it must address if it is to fulfill its promise of increased economic productivity and efficiency. This paper uses technical and legal perspectives to explain how things change when AI systems start being able to directly execute tasks on behalf of a user. We show how technical conceptions of agents track some, but not all, socio-legal conceptions of agency. That is, both computer science and the law recognize the problems of under-specification for an agent, and both disciplines have robust conceptions of how to address ensuring an agent does what the programmer, or in the law, the principal desires and no more. However, to date, computer science has under-theorized issues related to questions of loyalty and to third parties that interact with an agent, both of which are central parts of the law of agency. First, we examine the correlations between implied authority in agency law and the principle of value-alignment in AI, wherein AI systems must operate under imperfect objective specification. Second, we reveal gaps in the current computer science view of agents pertaining to the legal concepts of disclosure and loyalty, and how failure to account for them can result in unintended effects in AI ecommerce agents. In surfacing these gaps, we show a path forward for responsible AI agent development and deployment.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 8 of its 21 references that are in the map. First posted on arXiv 12 Aug 2025.

Builds on

8 of 21 references.

References · 8

  1. 2019Fine-Tuning Language Models from Human Preferencescited by 2,150
  2. 2021Ethical and social risks of harm from Language Modelscited by 966
  3. 2018Scalable agent alignment via reward modeling: a research directioncited by 447
  4. 2024The Ethics of Advanced AI Assistantscited by 107
  5. 2017Reinforcement Learning with a Corrupted Reward Channelcited by 70
  6. 2018Incomplete Contracting and AI Alignmentcited by 62
  7. 2025Characterizing AI Agents for Alignment and Governancecited by 30
  8. 2024Between Copyright and Computer Science: The Law and Ethics of Generative AIcited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 7.

9 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026A three-dimensional typology of agency for advanced AI systems
  2. 2025The Law-Following AI Framework: Legal Foundations and Technical Constraints. Legal Analogues for AI Actorship and technical feasibility of Law Alignment
  3. 2025Agentic AI: Autonomy, Accountability, and the Algorithmic Society
  4. 2025Governing AI Agents
  5. 2026A pragmatic approach to regulating AI agents
  6. 2026Mind The Gap: How The Technical Mechanism Of Agentic AI Outpace Global Legal Frameworks
  7. 2025From Values to Frameworks: A Qualitative Study of Ethical Reasoning in Agentic AI Practitioners
  8. 2026Security, privacy, and agentic AI in a regulatory view: From definitions and distinctions to provisions and reflections