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

Conference Paper (2026)
Author(s)

Philipp Spitzer (Karlsruhe Institut für Technologie)

Matthias Baldauf (Eastern Switzerland University of Applied Sciences)

Philippe Palanque (CRTC (Inserm and Paul Sabatier University), Toulouse)

Virpi Roto (Aalto University)

Katelyn Morrison (Carnegie Mellon University)

Garoa Gomez-Beldarrain (TU Delft - Industrial Design Engineering)

Monika Westphal (IE University Segovia)

Joshua Holstein (Karlsruhe Institut für Technologie)

Research Group
DesIgning Value in Ecosystems
DOI related publication
https://doi.org/10.1145/3772363.3778732 Final published version
More Info
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Publication Year
2026
Language
English
Research Group
DesIgning Value in Ecosystems
Article number
921
Publisher
ACM
ISBN (electronic)
9798400722813
Event
2026 CHI Conference on Human Factors in Computing Systems, CHI 2026 (2026-04-13 - 2026-04-17), Barcelona, Spain
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Abstract

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.

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