Agentic Automation Experiences—Rethinking the Interaction of Humans and AI Agents

Abstract

Recent advances in Artificial Intelligence (AI) have enabled agenticAI systems that coordinate multiple, specialized agents behind uni-fied interfaces. These systems can independently initiate actionsand solve complex problems. In traditional automation systemswithin organizations, workers maintained clear oversight—theycould see which system handled each task and trace outcomes tospecific processes. The integration of agentic AI, however, obscuresthis relationship and makes it more difficult for humans to identifywhich agent is responsible for a given outcome. This creates novelresearch challenges in the field of “Automation Experience”, par-ticularly in terms of transparency, human agency, and long-termhuman-AI collaboration dynamics. This workshop focuses on thesethree critical research dimensions. First, multi-agent transparencyand attribution explore how humans understand decision-makingwhen responsibility is shared across multiple coordinating agents.Second, human agency examines how workers can keep controlwhen collaborating with proactive AI systems that act on their own.Third, long-term temporal evolution looks at human skills changeover time, including how skills are maintained and how dependen-cies form. Through real-life organizational cases, presentations, andcollaborative activities, workshop participants will advance theirunderstanding of human experience with agentic AI and establisha research agenda for organizational contexts.