This article presents a study exploring how designers consider and approach user or customer mood in real-world projects. Because explicit mood-focused practice is difficult to identify and often masked by overlapping terminology, we conducted retrospective interviews with twenty experienced practitioners across the field of experience design. While participants tended to conflate mood with other affective constructs, many had nevertheless incorporated it—directly or indirectly—into their work. From their accounts, we identified five approaches to addressing mood in design: treating it as (1) an end in itself, (2) a means to enhance engagement, (3) a means to enrich experience, (4) a means to create differentiation or advantage, and (5) a means to facilitate user research. These findings advance understanding of mood-focused design by highlighting practitioners’ implicit engagement with mood and their pragmatic considerations, which extend beyond intrinsic well-being goals to instrumental, outcome-oriented goals. At the same time, we identified four categories of challenges practitioners face, three types of knowledge they regard as essential, and four obstacles that discourage them from bringing mood into practice or organizational contexts. Building on these insights, we outline research and educational opportunities to better support future mood-focused design practice.

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.