# Are We All Using Agents the Same Way? An Empirical Study of Core and Peripheral Developers Use of Coding Agents arXiv: https://arxiv.org/abs/2601.20106 ยท HTML: https://mlsanity.com/paper/2601.20106/ ## Key facts - Cited by 3 papers in the map (data through 14 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. ## Placed in - LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/) ## Builds on (most cited first, 30 of 34) - [Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions](https://mlsanity.com/paper/2108.09293/) (2021) - [Agentless: Demystifying LLM-based Software Engineering Agents](https://mlsanity.com/paper/2407.01489/) (2024) - [AutoCodeRover: Autonomous Program Improvement](https://mlsanity.com/paper/2404.05427/) (2024) - [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023) - [RepairAgent: An Autonomous, LLM-Based Agent for Program Repair](https://mlsanity.com/paper/2403.17134/) (2024) - [Grounded Copilot: How Programmers Interact with Code-Generating Models](https://mlsanity.com/paper/2206.15000/) (2022) - [GitHub Copilot AI pair programmer: Asset or Liability?](https://mlsanity.com/paper/2206.15331/) (2022) - [Refining ChatGPT-Generated Code: Characterizing and Mitigating Code Quality Issues](https://mlsanity.com/paper/2307.12596/) (2023) - [Interactive Debugging and Steering of Multi-Agent AI Systems](https://mlsanity.com/paper/2503.02068/) (2025) - ["It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmers](https://mlsanity.com/paper/2304.02491/) (2023) - [Comparing Code Explanations Created by Students and Large Language Models](https://mlsanity.com/paper/2304.03938/) (2023) - [On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHub](https://mlsanity.com/paper/2509.14745/) (2025) - [Towards Automating Code Review Activities](https://mlsanity.com/paper/2101.02518/) (2021) - [The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers](https://mlsanity.com/paper/2405.17739/) (2024) - [Using AI-Based Coding Assistants in Practice: State of Affairs, Perceptions, and Ways Forward](https://mlsanity.com/paper/2406.07765/) (2024) - [Exploring the Potential of ChatGPT in Automated Code Refinement: An Empirical Study](https://mlsanity.com/paper/2309.08221/) (2023) - [Can LLMs Replace Manual Annotation of Software Engineering Artifacts?](https://mlsanity.com/paper/2408.05534/) (2024) - [Human-In-the-Loop Software Development Agents](https://mlsanity.com/paper/2411.12924/) (2024) - [Security Weaknesses of Copilot-Generated Code in GitHub Projects: An Empirical Study](https://mlsanity.com/paper/2310.02059/) (2023) - [Evaluating Agent-based Program Repair at Google](https://mlsanity.com/paper/2501.07531/) (2025) - [Code with Me or for Me? How Increasing AI Automation Transforms Developer Workflows](https://mlsanity.com/paper/2507.08149/) (2025) - [The Future of AI-Driven Software Engineering](https://mlsanity.com/paper/2406.07737/) (2024) - [Prototyping with Prompts: Emerging Approaches and Challenges in Generative AI Design for Collaborative Software Teams](https://mlsanity.com/paper/2402.17721/) (2024) - [A ground-truth dataset and classification model for detecting bots in GitHub issue and PR comments](https://mlsanity.com/paper/2010.03303/) (2020) - [What Makes a Code Review Useful to OpenDev Developers? An Empirical Investigation](https://mlsanity.com/paper/2302.11686/) (2023) - [An Empirical Study of Bots in Software Development -- Characteristics and Challenges from a Practitioner's Perspective](https://mlsanity.com/paper/2005.13969/) (2020) - [Time Warp: The Gap Between Developers' Ideal vs Actual Workweeks in an AI-Driven Era](https://mlsanity.com/paper/2502.15287/) (2025) - [We Don't Need Another Hero? The Impact of "Heroes" on Software Development](https://mlsanity.com/paper/1710.09055/) (2017) - [The Effects of Continuous Integration on Software Development: a Systematic Literature Review](https://mlsanity.com/paper/2103.05451/) (2021) - [State-Of-The-Practice in Quality Assurance in Java-Based Open Source Software Development](https://mlsanity.com/paper/2306.09665/) (2023) ## Built on by (most cited first, 3 of 3) - [An Exploratory Study of Agent Plans for Agentic AI Coding Tools in Open-Source Software](https://mlsanity.com/paper/2608.04661/) (2026) - [Agentic Repository Mining: A Multi-Task Evaluation](https://mlsanity.com/paper/2605.04845/) (2026) - [Between the Commits: Process, Error, and Claim Reliability in a Wholly AI-Authored Codebase](https://mlsanity.com/paper/2609.29744/) (2026) ## 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.