The software engineering landscape is undergoing a fundamental shift as AI coding agents transition from passive assistants to active contributors. While agents now autonomously initiate thousands of pull requests, the sociotechnical dynamics of how humans verify, trust, and integrate agentic contributions remain a “black box”. This paper presents the first large-scale empirical investigation into the human-agent collaborative efforts. By mining a dataset of 212,297 PRs authored by 12 distinct AI agents on GitHub, we characterize the emerging interactions around AI-generated code. We analyze 30,729 longitudinal interaction timelines to map how developers, stratified by seniority, engage with “AI-first” codebases. Our findings reveal a landscape of rapid but passive integration: while human triage is often near-instantaneous (median 0.2 hours), a striking 73.9% of agent PRs are merged without modification, suggesting a high degree of “silent reliance”. Beyond metrics, we explore the sentiment in human-agent discourse, uncovering how developer experience shapes the rigor of AI oversight. By summarizing comment sentiment across developer seniority strata, we find that “expert” developers have a more neutral sentiment towards agent PRs, while others have a more positive take. Lastly, we conclude by outlining future research directions for how to build on this work.