# AI Agents and the Law

arXiv: https://arxiv.org/abs/2508.08544 · HTML: https://mlsanity.com/paper/2508.08544/

## 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.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 8 of 8)

- [Fine-Tuning Language Models from Human Preferences](https://mlsanity.com/paper/1909.08593/) (2019)
- [Ethical and social risks of harm from Language Models](https://mlsanity.com/paper/2112.04359/) (2021)
- [Scalable agent alignment via reward modeling: a research direction](https://mlsanity.com/paper/1811.07871/) (2018)
- [The Ethics of Advanced AI Assistants](https://mlsanity.com/paper/2404.16244/) (2024)
- [Reinforcement Learning with a Corrupted Reward Channel](https://mlsanity.com/paper/1705.08417/) (2017)
- [Incomplete Contracting and AI Alignment](https://mlsanity.com/paper/1804.04268/) (2018)
- [Characterizing AI Agents for Alignment and Governance](https://mlsanity.com/paper/2504.21848/) (2025)
- [Between Copyright and Computer Science: The Law and Ethics of Generative AI](https://mlsanity.com/paper/2403.14653/) (2024)

## Built on by (most cited first, 9 of 9)

- [The 2025 AI Agent Index: Documenting Technical and Safety Features of Deployed Agentic AI Systems](https://mlsanity.com/paper/2602.17753/) (2026)
- [Social Theory Should Be a Structural Prior for Agentic AI: A Formal Framework for Multi-Agent Social Systems](https://mlsanity.com/paper/2605.07069/) (2026)
- [MASLegalBench: Benchmarking Multi-Agent Systems in Deductive Legal Reasoning](https://mlsanity.com/paper/2509.24922/) (2025)
- [Safety Compliance: Rethinking LLM Safety Reasoning through the Lens of Compliance](https://mlsanity.com/paper/2509.22250/) (2025)
- [Parthenon Law: A Self-Evolving Legal-Agent Framework](https://mlsanity.com/paper/2606.04602/) (2026)
- [SovereignNegotiation-Bench: Evaluating User-Owned Personal Agents In Delegated Bargaining Under Privacy, Consent, Evidence, And Institutional Pressure](https://mlsanity.com/paper/2607.02814/) (2026)
- [Distributed Legal Infrastructure for a Trustworthy Agentic Web](https://mlsanity.com/paper/2603.06884/) (2026)
- [Towards Agentic AI Governance: A Preliminary Assessment](https://mlsanity.com/paper/2607.07612/) (2026)
- [When Agents Act Unwatched: The Reduced-Supervision Paradox in Agentic AI](https://mlsanity.com/paper/2609.29547/) (2026)

## 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.