AI Agents Submit Overlapping Code to GitHub; Cross-Agent Conflicts Spike to 41.7%

The paper addresses a gap in understanding how concurrent AI coding agents interact when working on shared repositories. Prior research on AI-generated code has largely treated agents in isolation, but as these tools become standard in development workflows, the question of what happens when two or more agents submit changes to the same codebase simultaneously becomes urgent. The study's use of the AIDev-pop dataset—a large empirical corpus of agent-authored pull requests—allows for the first systematic measurement of this phenomenon.

The distinction between intra-agent and cross-agent conflicts is striking. When the same AI agent submits multiple pull requests to a repository (accounting for the vast majority of co-active pairs), merge conflicts occur in 19.8% of cases. However, when two different agents submit overlapping pull requests, the conflict rate jumps to 41.7%—more than double. This gap suggests that individual agents tend to work in consistent patterns and modify overlapping code in ways that can be reconciled, whereas different agents may not have compatible approaches to the same code regions. The finding that nearly 42% of observed conflicts were structural (modify/delete or add/add operations) rather than simple textual misalignments indicates that the friction goes beyond simple line-level collision; agents are making incompatible edits to code structure itself.