Agentic Very Much! Adoption of Coding Agent in New GitHub Projects

2026-06-05

AI Summary

This study investigates the adoption rate and depth of engagement with AI coding agents in newly created GitHub projects. Departing from prior work, it focuses on a cohort of repositories initiated after earlier studies concluded, employing an empirical approach grounded in project metadata and commit histories, augmented by techniques for detecting AI-assisted commits. The findings reveal that the adoption rate of coding agents in these new projects has doubled compared to previous estimates, with a substantially higher proportion of commits identified as AI-assisted. This indicates that developers are integrating AI coding tools more deeply from the earliest stages of project development. The results underscore a significant shift in how coding agents are utilized and provide novel evidence of the evolving role of artificial intelligence in the initial phases of software development.

Abstract

In previous work, we investigated the adoption of coding agents in GitHub projects, finding that it was very significant. This study follows this line of work, but analyses new projects, that were created after the previous study. In this new sample, we find that the adoption of coding agents is more than twice as high. We also find that the adoption is significantly more intensive, as the proportion of AI-assisted commits is sensibly higher, despite strong signs that we do not detect all of it.

Problem

• coding agent
• GitHub projects
• AI adoption
• software development
• AI-assisted commits

Innovation

• coding agent
• GitHub projects
• AI-assisted commits
• adoption rate
• software development