.png){kind=icon}
.png){kind=icon}
Return
Agentic Visualization: Extracting Agent-Based Design Patterns From Visualization Systems
.png){kind=icon}
PRE
AI
DOI：10.1109/MCG.2025.3607741.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Autonomous agents powered by large language models are transforming artificial intelligence (AI), creating an imperative for the visualization area. However, our field's focus on a human in the sensemaking loop raises critical questions about autonomy, delegation, and coordination for such agentic visualization that preserve human agency while amplifying analytical capabilities. This article addresses these questions by reinterpreting existing visualization systems with semiautomated 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.
Keywords:
Visualization
Data visualization
Artificial intelligence
Cognition
Data mining
Training
Lenses
Large language models
Computer architecture
Visual analytics
Journal
I
IF:
1.4
Papers:
57
Citations:
0