mlsanityData to 7 Oct 2026

Early Adoption of Agentic Coding Tools by GitHub Projects

arXiv 2607.14037 ↗First posted 15 Jul 2026cs.SE cs.AI cs.CYcited by 0

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

Agentic coding tools are increasingly capable of generating and submitting pull requests (PRs) to software projects, introducing new forms of human-agent collaboration in software development. While prior studies have examined PR-level outcomes of agent-generated contributions, less is known about how agentic coding tools are adopted and managed at the project level. In this paper, we analyze 25,264 agentic PRs from 2,361 popular GitHub repositories to investigate (1) the adoption of agentic coding tools, (2) project-level agentic PR productivity, and (3) human-agent collaboration patterns. Our results show that the median repository generates only one to two agentic PRs during a three-month period, indicating that intensive adoption remains concentrated in a small subset of projects. At the same time, small projects (1-5 contributors) exhibit higher participation ratios and average levels of agentic PR activity than medium-sized and large projects. We also observe substantial variation in project-level agentic PR productivity. While a small number of projects exceed an industry-reported estimate of 36 PRs per participant during the three-month observation period, most projects remain below this threshold. Finally, human-agent collaboration is dominated by a single-human oversight model, in which one developer reviews and/or modifies the agent's contributions, while multi-human collaboration patterns remain uncommon. These findings provide early empirical evidence on how open-source projects organize human oversight around agentic coding tools and suggest that successful integration of agent-generated contributions depends not only on advances in agent capabilities but also on the human and organizational processes that govern their use. Because this study captures an early snapshot of agent adoption, future work should continue to track how adoption patterns evolve over time.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 9 of its 23 references that are in the map. First posted on arXiv 15 Jul 2026.

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9 of 23 references.

Closest ancestors · 6

Same direction, newest first

  1. 2026Collaborator or Assistant? How AI Coding Agents Partition Work Across Pull Request Lifecycles8 May
  2. 2026On the Adoption of AI Coding Agents in Open-source Android and iOS Development12 Feb
  3. 2026Let's Make Every Pull Request Meaningful: An Empirical Analysis of Developer and Agentic Pull Requests26 Jan
  4. 2026Where Do AI Coding Agents Fail? An Empirical Study of Failed Agentic Pull Requests in GitHub21 Jan
  5. 2025On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHub18 Sep
  6. 2025Code with Me or for Me? How Increasing AI Automation Transforms Developer Workflows10 Jul

Other references · 3

  1. 2024Towards AI-Native Software Engineering (SE 3.0): A Vision and a Challenge Roadmapcited by 30
  2. 2026Novice Developers Produce Larger Review Overhead for Project Maintainers while Vibe Codingcited by 3
  3. 2022Big Data = Big Insights? Operationalising Brooks' Law in a Massive GitHub Data Setcited by 1

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  5. 2026How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requests
  6. 2026Investigating Autonomous Agent Contributions in the Wild: Activity Patterns and Code Change over Time
  7. 2026Humans Integrate, Agents Fix: How Agent-Authored Pull Requests Are Referenced in Practice
  8. 2026Not All Agents Are Equal: Code Quality and Post-Merge Maintenance Across Five Autonomous Coding Agents in the Wild