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    Area of Science:

    • Artificial Intelligence
    • Data Visualization
    • Human-Computer Interaction

    Background:

    • Large language models (LLMs) are enabling autonomous agents, necessitating advancements in visualization.
    • Current visualization practices often center on human sensemaking, posing challenges for autonomous agent integration.
    • Key questions arise regarding autonomy, delegation, and coordination in agentic visualization systems.

    Purpose of the Study:

    • To re-examine existing visualization systems through an agentic lens, considering AI components.
    • To address critical questions about maintaining human agency within autonomous visualization.
    • To develop design patterns for effective agentic visualization systems.

    Main Methods:

    • Analysis of existing visualization systems with semiautomated or fully automatic AI.
    • Reinterpretation of these systems through an agentic framework.
    • Extraction of design patterns for agentic visualization.

    Main Results:

    • Identified design patterns for agentic visualization, encompassing agent roles, communication, and coordination.
    • Demonstrated how existing systems can be understood via an agentic lens.
    • Provided a foundation for developing future agentic visualization tools.

    Conclusions:

    • Agentic visualization systems are crucial for harnessing AI capabilities.
    • Design patterns are essential for creating effective agentic visualization that balances AI and human control.
    • Future systems should prioritize human insight and agency alongside AI-driven analysis.