Operationalizing Ethics for AI Agents: How Developers Encode Values into Repository Context Files

Christoph Treude, Sebastian Baltes, Marc Cheong

Keywords: AI governance, ethics operationalization, repository-level context files

TL;DR: A position paper highlighting the emerging practice of encoding ethics for AI agents in repository-level context files and outlining a research agenda for studying this shift.

Abstract: As AI coding agents become embedded in software development workflows, developers are beginning to operationalize ethical principles by encoding behavioral rules into repository-level context files, such as AGENTS.md files. Rather than examining the ethics of AI agents in the abstract, this vision paper investigates how ethics and values are already being translated for AI agents into actionable instructions that shape agent behavior. Through a preliminary investigation, we find that developers are already embedding guidance related to fairness, accessibility, sustainability, tone, and privacy. These artifacts function as a developer-authored governance layer, translating abstract principles into situated, natural-language directives within development workflows. In this position paper, we outline a research agenda for studying this emerging practice, including how encoded values vary across communities, what governance dynamics emerge when multiple contributors negotiate these files, and whether agents reliably adhere to the constraints specified. Understanding how ethics and values are operationalized for AI agents is essential to ground AI governance in modern software engineering practice.

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Paper Type: Short papers (i.e., vision, new ideas, and position papers). 2–4 pages

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Submission Number: 41