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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.19101 |
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| _version_ | 1866911156963115008 |
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| author | Dhanoa, Vaishali Wolter, Anton León, Gabriela Molina Schulz, Hans-Jörg Elmqvist, Niklas |
| author_facet | Dhanoa, Vaishali Wolter, Anton León, Gabriela Molina Schulz, Hans-Jörg Elmqvist, Niklas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_19101 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Agentic Visualization: Extracting Agent-based Design Patterns from Visualization Systems Dhanoa, Vaishali Wolter, Anton León, Gabriela Molina Schulz, Hans-Jörg Elmqvist, Niklas Human-Computer Interaction 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. |
| title | Agentic Visualization: Extracting Agent-based Design Patterns from Visualization Systems |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2505.19101 |