VACP: Visual Analytics Context Protocol
This addresses the challenge of making Visual Analytics systems accessible and efficient for AI agents as users, representing an incremental improvement over existing agentic approaches.
The paper tackles the problem of AI agents performing poorly in Visual Analytics tasks due to inadequate interface perception, by introducing the Visual Analytics Context Protocol (VACP) that exposes application state and interactions, resulting in higher success rates and reduced token consumption and latency in evaluations.
The rise of AI agents introduces a fundamental shift in Visual Analytics (VA), in which agents act as a new user group. Current agentic approaches - based on computer vision and raw DOM access - fail to perform VA tasks accurately and efficiently. This paper introduces the Visual Analytics Context Protocol (VACP), a framework designed to make VA applications "agent-ready" that extends generic protocols by explicitly exposing application state, available interactions, and mechanisms for direct execution. To support our context protocol, we contribute a formal specification of AI agent requirements and knowledge representations in VA interfaces. We instantiate VACP as a library compatible with major visualization grammars and web frameworks, enabling augmentation of existing systems and the development of new ones. Our evaluation across representative VA tasks demonstrates that VACP-enabled agents achieve higher success rates in interface interpretation and execution compared to current agentic approaches, while reducing token consumption and latency. VACP closes the gap between human-centric VA interfaces and machine perceivability, ensuring agents can reliably act as collaborative users in VA systems.