Autonomous agents powered by Large Language Models are transforming AI, creating an imperative for the visualization field to embrace agentic frameworks. However, our field's focus on a human in the sensemaking loop raises critical questions about autonomy, delegation, and coordination for such \textit{agentic visualization} that preserve human agency while amplifying analytical capabilities. This paper addresses these questions by reinterpreting existing visualization systems with semi-automated or fully automatic AI components through an agentic lens. Based on this analysis, we extract a collection of design patterns for agentic visualization, including agentic roles, communication and coordination. These patterns provide a foundation for future agentic visualization systems that effectively harness AI agents while maintaining human insight and control.
@article{arxiv.2505.19101,
title = {Agentic Visualization: Extracting Agent-based Design Patterns from Visualization Systems},
author = {Vaishali Dhanoa and Anton Wolter and Gabriela Molina León and Hans-Jörg Schulz and Niklas Elmqvist},
journal= {arXiv preprint arXiv:2505.19101},
year = {2025}
}