Unpacking AI Agent Participation in Issue-Centered Collaboration in Open-Source Software Development

The increasing participation of AI agents in open-source software development raises questions about their role in collaborative processes. This paper investigates how AI agent participation relates to the structure and outcomes of issue-centered collaboration in open-source software projects. We adopt a process-level perspective by modeling issue handling as sequences of events and extending dynamic issue-pr entropy to distinguish between agent-related and non-agent-related contributions. Using large-scale issue event data from 83 GitHub repositories, we construct a project–month panel dataset and analyze associations between collaboration complexity and development outcomes. Our results show that agent-related collaboration complexity is more strongly associated with development output than human-only collaboration complexity, and is associated with fewer newly introduced defects. In contrast, its relationship with issue resolution efficiency is highly context-dependent. These findings highlight the importance of considering collaboration structure when evaluating the impact of AI agents in open-source software development.