Recent advances in Artificial Intelligence (AI) have enabled agentic AI systems that coordinate multiple, specialized agents behind unified interfaces. These systems can independently initiate actions and solve complex problems. In traditional automation systems within organizations, workers maintained clear oversight-they could see which system handled each task and trace outcomes to specific processes. The integration of agentic AI, however, obscures this relationship and makes it more difficult for humans to identify which agent is responsible for a given outcome. This creates novel research challenges in the field of “Automation Experience”, particularly in terms of transparency, human agency, and long-term human-AI collaboration dynamics. This workshop focuses on these three critical research dimensions. First, multi-agent transparency and attribution explore how humans understand decision-making when responsibility is shared across multiple coordinating agents. Second, human agency examines how workers can keep control when collaborating with proactive AI systems that act on their own. Third, long-term temporal evolution looks at human skills change over time, including how skills are maintained and how dependencies form. Through real-life organizational cases, presentations, and collaborative activities, workshop participants will advance their understanding of human experience with agentic AI and establish a research agenda for organizational contexts. ... Read more