AdaLens: Interactive Storyline for Monitoring and Steering Long-Running Agentic Data Analysis

Abstract:Large language models are pushing data science toward increasingly autonomous and agentic workflows, with recent systems already supporting multi-step and long-running analyses. As these workflows become more autonomous, conventional interfaces no longer provide adequate support for two critical requirements: observability for understanding an agent's evolving reasoning and evidence, and steerability for redirecting low-value directions or deepening promising ones during execution. Existing interactive approaches improve process visibility and open intervention points, but they remain largely designed for discrete, turn-by-turn exchanges rather than the parallel branches and evolving decision structures of long-running agentic analysis. We study this need as interactive oversight in long-running agentic data analysis and present AdaLens, an interactive system for monitoring and steering ongoing runs. AdaLens combines a storyline-based representation that unifies analytical plans, execution progress, intermediate findings, and data-column involvement with steering interactions grounded in these analytical elements for directional guidance and execution control. We evaluate AdaLens through two case studies and a user study, examining how it supports analysts in monitoring and steering long-running agentic data analysis.

Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)

arXiv:2608.17834 [cs.HC]

(or arXiv:2608.17834v1 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2608.17834 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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