CoDA: Agentic Systems for Collaborative Data Visualization
Authors: name: Zichen Chen, name: Jiefeng Chen, name: Sercan Ö. Arik, name: Misha Sra, name: Tomas Pfister, name: Jinsung Yoon
Organization: Google

Deep research has revolutionized data analysis, yet data scientists still
devote substantial time to manually crafting visualizations, highlighting the
need for robust automation from natural language queries. However, current
systems struggle with complex datasets containing multiple files and iterative
refinement. Existing approaches, including simple single- or multi-agent
systems, often oversimplify the task, focusing on initial query parsing while
failing to robustly manage data complexity, code errors, or final visualization
quality. In this paper, we reframe this challenge as a collaborative
multi-agent problem. We introduce CoDA, a multi-agent system that employs
specialized LLM agents for metadata analysis, task planning, code generation,
and self-reflection. We formalize this pipeline, demonstrating how
metadata-focused analysis bypasses token limits and quality-driven refinement
ensures robustness. Extensive evaluations show CoDA achieves substantial gains
in the overall score, outperforming competitive baselines by up to 41.5%. This
work demonstrates that the future of visualization automation lies not in
isolated code generation but in integrated, collaborative agentic workflows.