Behind Agentic Pull Requests: An Empirical Study on Developer Interventions in AI Agent-Authored Pull Requests

AI coding agents are increasingly being adopted to autonomously author pull requests (PRs). While these agents can perform a wide range of tasks, little is known about how much human intervention is required to collaborate with them and integrate AI Agents contributions in practice. In this paper, we conduct an empirical study on human intervention in agent-authored PRs (APRs) as a measure of human effort and oversight. Using the AIDev dataset, we first compare how often humans intervene in APRs vs human-authored PRs (HPRs), as well as the resulting outcomes of this intervention. We then conduct a qualitative thematic analysis of human interventions in APRs and derive a taxonomy including 4 high-level categories and 42 intervention actions. Our results show that, human interventions occur less frequently in APRs than in HPRs (52.17% vs. 83.59%), but when it occurs in APRs, it requires higher review effort, including larger code churn and longer durations. Our taxonomy results show that most human effort is spent on guidance-level interventions with 58.02%, focusing on restricting the agent's actions and enforcing project-conventions, followed by decision-level interventions at 21.16%, direct code changes at 17.05% and operational-level intervention at 3.69%. Which indicates that, collaboration with coding agents, is shifting developer work from implementation to supervision, guidance and quality control.