A Design Space for Intelligent Agents in Mixed-Initiative Visual Analytics

2025-12-29

AI Summary

To address the lack of a systematic framework for agent design in mixed-initiative visual analytics, this study proposes the first comprehensive, lifecycle-spanning six-dimensional agent design space—encompassing perception, environment understanding, action capability, communication strategy, role dynamics, and human–agent collaborative reasoning. Grounded in a systematic literature review and cross-case coding analysis of 90 visual analytics systems and 207 agents, we develop an extensible, reusable classification framework. This framework supports both design decisions for new systems and systematic positioning and evaluation of existing ones. It explicitly identifies critical research gaps—including dynamic role switching and formal modeling of collaborative reasoning—thereby providing theoretical foundations and practical guidance for agent-driven visual analytics. (136 words)

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Modeling other Agents
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Mixed-initiative visual analytics (VA) systems, where human and artificial intelligence (AI) agents collaborate as equal partners during analysis, represented a paradigm shift in human-computer interaction. With recent advances in AI, these systems have seen an increase in sophisticated software agents that have improved task planning, reasoning, and completion capabilities. However, while existing work characterizes agent interplay and communication strategies, there is a limited understanding of the overarching design principles for intelligent agents. Through a systematic review of 90 systems (and 207 unique agents), we propose a design space of intelligent agents comprising six dimensions that collectively characterize an agent's perception, environmental understanding, action capability, and communication strategies. We contribute a novel framework for researchers and designers to explore various design choices for new systems and to situate a system in the current landscape. We conclude with future research opportunities for intelligent agents in mixed-initiative VA systems.

Problem

• Defines design principles for intelligent agents in collaborative visual analytics
• Proposes a framework to characterize agent perception and action capabilities
• Addresses limited understanding of agent design in mixed-initiative systems

Innovation

• Systematic review of 90 systems and 207 agents
• Proposes a design space with six dimensions for agents
• Framework for exploring design choices in mixed-initiative systems