VACP: Visual Analytics Context Protocol

TL;DR

VACP is a framework that enhances visual analytics systems to be more agent-friendly by explicitly exposing application states and interactions, improving AI agent performance in VA tasks.

Contribution

The paper introduces VACP, a formal specification and library implementation that makes VA applications agent-ready, enabling more accurate and efficient AI agent interactions.

Findings

VACP-enabled agents outperform current approaches in interface interpretation.

VACP reduces token consumption and latency in VA tasks.

Evaluation shows higher success rates with VACP in representative VA tasks.

Abstract

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.