Who Did What? Designing Avatars for Explainable Multi-Agent Systems in Knowledge Work

Knowledge workers increasingly rely on multi-agent systems to solve complex problems. While these systems offer valuable support, they often obscure which agents contributed to a response, leading to a lack of transparency that may result in errors and reduced trust. To address this, we propose avatars that make agents’ expertise and contributions transparent. We iteratively co-designed avatars representing distinct expertise areas and validated them in an experiment (N=100). Building on this, we developed four multi-agent prototypes varying in explanation modality (text vs. avatars) and resolution (low vs. high). We then conducted a mixed-methods evaluation with an online experiment (N=124) and follow-up interviews (N=20). Qualitative results suggest that avatars foster clearer mental models, improve perceived explainability, and support users’ trust calibration without increasing cognitive load, although no significant quantitative differences were found. Our research contributes validated avatar designs, insights into explanation strategies, and design implications for explainable multi-agent systems.