Problem
Research questions and friction points this paper is trying to address.
autonomous workflows
observability
steerability
long-running agentic analysis
AdaLens: Interactive Storyline for Monitoring and Steering Long-Running Agentic Data Analysis
🤖 AI Summary
本文提出AdaLens系统，通过故事线表示和交互式指导解决长时间运行的数据分析中的可观察性和可操控性问题。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Interactive Oversight
Storyline-based Representation
Steering Interactions
Agentic Data Analysis
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