TL;DR
This paper surveys 55 agentic visual analytics systems, introduces a co-evolutionary framework analyzing how increasing agent autonomy reshapes workflows and human roles, and provides design guidelines and future research directions.
Contribution
It presents a novel co-evolutionary framework linking agent autonomy with human role adaptation in visual analytics, supported by a comprehensive taxonomy and analysis.
Findings
Identified trade-offs between autonomy levels and human roles.
Mapped agentic roles to VA pipeline stages.
Provided actionable design guidelines for agentic VA systems.
Abstract
Agentic visual analytics (VA) represents an emerging class of systems in which large language model (LLM)-driven agents autonomously plan, execute, evaluate, and iterate across the full visual analytics pipeline. By shifting users from low-level tool operations to high-level analytical goals expressed through natural language, these systems are fundamentally transforming how humans interact with data. However, the rapid proliferation of such systems in recent years has outpaced our understanding of their design landscape. Two intertwined problems remain open: how do autonomous agents reshape the traditional VA pipeline, and how must human involvement adapt as agent autonomy increases? To address these questions, this paper presents a comprehensive survey of 55 primary agentic VA systems and introduces a co-evolutionary framework. This framework is essential because it jointly analyzes…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
