Are We All Using Agents the Same Way? An Empirical Study of Core and Peripheral Developers Use of Coding Agents
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
Autonomous AI agents are transforming software development and redefining how developers collaborate with AI. Prior research shows that the adoption and use of AI-powered tools differ between core and peripheral developers. However, it remains unclear how this dynamic unfolds in the emerging era of autonomous coding agents. In this paper, we present the first empirical study of 9,427 agentic PRs, examining how core and peripheral developers use, review, modify, and verify agent-generated contributions prior to acceptance. Through a mix of qualitative and quantitative analysis, we make four key contributions. First, a subset of peripheral developers use agents more often, delegating tasks evenly across bug fixing, feature addition, documentation, and testing. In contrast, core developers focus more on documentation and testing, yet their agentic PRs are frequently merged into the main/master branch. Second, core developers engage slightly more in review discussions than peripheral developers, and both groups focus on evolvability issues. Third, agentic PRs are less likely to be modified, but when they are, both groups commonly perform refactoring. Finally, peripheral developers are more likely to merge without running CI checks, whereas core developers more consistently require passing verification before acceptance. Our analysis offers a comprehensive view of how developer experience shapes integration offer insights for both peripheral and core developers on how to effectively collaborate with coding agents.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 34 of its 101 references that are in the map. First posted on arXiv 27 Jan 2026.
Builds on
34 of 101 references.
Closest ancestors · 7
Same direction, newest first
- 2025On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHub18 Sep
- 2025Code with Me or for Me? How Increasing AI Automation Transforms Developer Workflows10 Jul
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- 2024Human-In-the-Loop Software Development Agents19 Nov
- 2024Agentless: Demystifying LLM-based Software Engineering Agents1 Jul
- 2024RepairAgent: An Autonomous, LLM-Based Agent for Program Repair25 Mar
Other references · 27
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- 2021Towards Automating Code Review Activitiescited by 54
- 2024The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmerscited by 46
- 2023Exploring the Potential of ChatGPT in Automated Code Refinement: An Empirical Studycited by 44
- 2024Using AI-Based Coding Assistants in Practice: State of Affairs, Perceptions, and Ways Forwardcited by 44
15 more
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- 2024The Future of AI-Driven Software Engineeringcited by 23
- 2024Prototyping with Prompts: Emerging Approaches and Challenges in Generative AI Design for Collaborative Software Teamscited by 16
- 2020A ground-truth dataset and classification model for detecting bots in GitHub issue and PR commentscited by 10
- 2023What Makes a Code Review Useful to OpenDev Developers? An Empirical Investigationcited by 10
- 2020An Empirical Study of Bots in Software Development -- Characteristics and Challenges from a Practitioner's Perspectivecited by 8
- 2025Time Warp: The Gap Between Developers' Ideal vs Actual Workweeks in an AI-Driven Eracited by 4
- 2017We Don't Need Another Hero? The Impact of "Heroes" on Software Developmentcited by 3
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- 2023State-Of-The-Practice in Quality Assurance in Java-Based Open Source Software Developmentcited by 2
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- 2025How do Copilot Suggestions Impact Developers' Frustration and Productivity?cited by 1
Built on by
Citing papers per year: 2026: 3.
3 papers.
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- 2025The Rise of AI Teammates in Software Engineering (SE) 3.0: How Autonomous Coding Agents Are Reshaping Software Engineering
- 2026On the Adoption of AI Coding Agents in Open-source Android and iOS Development