This workshop invites researchers and practitioners to share innovative ideas, explore questions, and discuss strategies to transform the impact of VIS for a future where human and AI agents co-exist.
Abstract
Recent advances in agents (i.e., autonomous, goal-driven AI systems that iteratively observe, act, and learn from their environments) offer a fundamentally different approach from traditional AI models that passively respond to input. These AI agents are rapidly reshaping how we approach data-intensive tasks and providing new opportunities for the VIS community. Imagine an agent autonomously generating visualizations to analyze complex data, discovering patterns collaboratively, testing hypotheses, and communicating visual insights at a speed and scale beyond human capability. Yet, the emergence of these powerful systems raises critical questions that the VIS community must address: Could autonomous agents eventually replace human data scientists, and if not, how might they best collaborate? Are current visualization techniques and interfaces, originally designed for human analysts, suitable for agent interactions? How can VIS designers effectively integrate agents into their workflows without compromising human agency? And to what extent should agents help shape and educate the next generation of visualization researchers? Through a mix of keynote talks, paper presentations, and an agentic VIS challenge, this workshop invites researchers and practitioners to share innovative ideas, explore these questions, and discuss strategies to transform the impact of VIS for a future where human and AI agents co-exist.
This work defines collaborative AI as a class of systems that combine generative exploration with autonomous action and calibrate between them based on context, uncertainty, and task demands, and identifies four required capabilities: metacognition, contextual mode-switching, uncertainty-aware action, and adaptive huma...
Nalan Karunanayake, Savindu Nanayakkara, Kasun Gayashan Hettihewa et al.· International Journal of Net...· 0 citations
AgentGUI is introduced, a user-friendly, locally hosted GUI for seamlessly observing and steering AI agents amid multiple concurrent, long-running sessions and features rich agent trajectory visualizations, effective manual and automated steering, and integration with and coordination between open-source and frontier a...
Xuan Zhao, Jiwoong Sohn, Qinyue Zheng et al.· arXiv.org· 0 citations
This panel will convene leading researchers and practitioners to discuss the future of data agents, which is defined as an autonomous system capable of perceiving data in various forms, planning and executing complex data manipulation and analysis tasks, and interacting with humans through natural language or other int...
Guo-Liang Li, Yu-Yu Luo· Proceedings of the VLDB Endo...· 0 citations
A unified, taxonomy-driven, and deployment-oriented survey of agentic AI systems, synthesizing recent advances through a modular reference architecture and a four-dimensional taxonomy that characterizes agents along the axes of autonomy, tool use, collaboration, and safety–governance is presented.
Sparsh Bajoria, Shreyanshu Ranjan, Adhitya M et al.· Cognitive Computation· 1 citation
This work argues that studying AI Scientists as human-agent systems (HAS) is both underexplored and undervalued, and calls for new research that adopts the HAS lens to develop mathematical frameworks for understanding and fostering human-AI synergy in scientific discovery.
P. Emami, Sameera Horawalavithana, T. Nguyễn et al.· 0 citations
This primer draws on fieldwork in a computational biology laboratory to examine what human oversight of AI agents requires in practice and shows that effective oversight has four components: adequate knowledge of system capabilities and limitations, sufficient observation of system actions, meaningful control of system...
Samir Passi, Ranjit Singh· 0 citations
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