# Context Engineering for AI Agents in Open-Source Software

arXiv: https://arxiv.org/abs/2510.21413 · HTML: https://mlsanity.com/paper/2510.21413/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2025.

## Built on by (most cited first, 14 of 14)

- [Agentic Much? Adoption of Coding Agents on GitHub](https://mlsanity.com/paper/2601.18341/) (2026)
- [Evaluating AGENTS.md: Are Repository-Level Context Files Helpful for Coding Agents?](https://mlsanity.com/paper/2602.11988/) (2026)
- [Harness Engineering for Agentic AI Coding Tools: An Exploratory Study](https://mlsanity.com/paper/2602.14690/) (2026)
- [Structured Context Engineering for File-Native Agentic Systems: Evaluating Schema Accuracy, Format Effectiveness, and Multi-File Navigation at Scale](https://mlsanity.com/paper/2602.05447/) (2026)
- [A Dataset of Agentic AI Coding Tool Configurations](https://mlsanity.com/paper/2605.08435/) (2026)
- [Context Rot in AI-Assisted Software Development: Repurposing Documentation Consistency for AI Configuration Artifacts](https://mlsanity.com/paper/2606.09090/) (2026)
- [Cheap Code, Costly Judgment: A Case Study on Governable Agentic Software Engineering](https://mlsanity.com/paper/2607.01087/) (2026)
- [Context Engineering: A Practitioner Methodology for Structured Human-AI Collaboration](https://mlsanity.com/paper/2604.04258/) (2026)
- [An Exploratory Study of Agent Plans for Agentic AI Coding Tools in Open-Source Software](https://mlsanity.com/paper/2608.04661/) (2026)
- [BootstrapAgent: Distilling Repository Setup into Reusable Agent Knowledge](https://mlsanity.com/paper/2605.15815/) (2026)
- [ETAS: An Effect-Typed Language for Agent Systems](https://mlsanity.com/paper/2607.17780/) (2026)
- [Who Maintains Agent Skills? A Longitudinal Study of Human-Governed, AI-Assisted Skill Maintenance](https://mlsanity.com/paper/2609.05677/) (2026)
- [Specifying and Maintaining Agentic Workflows: An Empirical Study of GitHub Agentic Workflows](https://mlsanity.com/paper/2609.27263/) (2026)
- [What Will Remain Human in Software Architecture? A Focus Group Report](https://mlsanity.com/paper/2609.30334/) (2026)

## Abstract (arXiv)

GenAI-based coding assistants have disrupted software development. The next generation of these tools is agent-based, operating with more autonomy and potentially without human oversight. Like human developers, AI agents require contextual information to develop solutions that are in line with the standards, policies, and workflows of the software projects they operate in. Vendors of popular agentic tools (e.g., Claude Code) recommend maintaining version-controlled Markdown files that describe aspects such as the project structure, code style, or building and testing. The content of these files is then automatically added to each prompt. Recently, AGENTS.md has emerged as a potential standard that consolidates existing tool-specific formats. However, little is known about whether and how developers adopt this format. Therefore, in this paper, we present the results of a preliminary study investigating the adoption of AI context files in 466 open-source software projects. We analyze the information that developers provide in AGENTS.md files, how they present that information, and how the files evolve over time. Our findings indicate that there is no established content structure yet and that there is a lot of variation in terms of how context is provided (descriptive, prescriptive, prohibitive, explanatory, conditional). Our commit-level analysis provides first insights into the evolution of the provided context. AI context files provide a unique opportunity to study real-world context engineering. In particular, we see great potential in studying which structural or presentational modifications can positively affect the quality of the generated content.