AdaLens: Interactive Storyline for Monitoring and Steering Long-Running Agentic Data Analysis
Authors: Yangtian Liu, Yan Miao, Shuhan Liu, Yunfan Zhou, Dae Hyun Kim, Di Weng, Yingcai Wu
Organizations: State Key Lab of CAD&CG, Zhejiang University, Hangzhou, Zhejiang, China · Department of Computer Science and Engineering, Yonsei University, Seoul, Republic of Korea · School of Software Technology, Zhejiang University, Ningbo, Zhejiang, China
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.
Figures & tables
Fig. 1: AdaLens interface for monitoring and steering long-running agentic data analysis through three coordinated views. (A) The chat view records user and system messages, dispatched plans (A1), and stage/final reports (A2). (B) The storyline view serves as the primary workspace, combining steering tools (B1), insight-type legends (B2), report anchors (B3), plan/summary cards (B4, B7), data-column traces (B5), atomic insight glyphs (B6), on-demand detail popovers (B8), and the alternation between finding regions (B9) and converge regions (B10) to reveal stepwise progression and data-grounded analytical lineage over time. (C) The inspector view summarizes coverage across columns and insight types (C1) and reveals the selected task or finding with linked evidence such as plots, code, and outputs (C2).
Fig. 2: AdaLens framework for interactive oversight in long-running agentic data analysis. Analysts steer the run through intention-level actions and execution-level controls (A). The orchestrator manages context integration, plan creation, report generation, and progress evaluation (B). The orchestrator dispatches plans to parallel workers whose analyzer and summarizer produce summaries and atomic insights with supporting evidence (C), and these intermediate analytical elements are surfaced through the chat, inspector, and storyline views (D).
Fig. 3: Case 1 (Stages 1–2): using AdaLens to initiate the investigation of the ambiguous hot_degree metric in a Twitter dataset and regulate candidate plans. (A) ED starts from a focused chat request asking which columns relate to hot_degree . (B) When two candidate plans appear in parallel with their execution states, ED pauses both, reviews them, terminates the categorical/boolean branch, and launches the branch on numerical variables.
Fig. 4: Case 1 (Stages 3–5): using AdaLens to inspect and extend the analysis of the ambiguous hot_degree metric and synthesize the resulting findings. (C) Column label sizes convey the extent of each variable’s involvement, highlighting hot_degree as the analytical focus; the resulting summary identifies statuses_count , media_count , and followers_count as its strongest related variables. (D) Based on these visible results, ED creates a targeted follow-up plan to model hot_degree from these variables; the same four column characters continue into the follow-up step, which yields a log-linear model for hot_degree . (E) AdaLens returns a final report that consolidates both the correlation findings and the resulting predictive model.
Fig. 5: Case 2 (Stages 1–3): using AdaLens to explore an NBA spatiotemporal tracking dataset in an open-ended manner and steer the analysis toward a promising matchup. (A) EE begins with a broad exploration request, after which the storyline presents early summaries, atomic insights, and the columns involved in them. (B) EE notices two frame-level ball-coordinate columns that are not of particular interest and applies Ignore to ball_zone and ball_z to deprioritize them. (C) He then applies Focus to a promising summary card about Bogut and James, steering subsequent plans toward this matchup.
Fig. 6: Case 2 (Stages 4–5): using AdaLens to elaborate a key insight and generate a final report. (D) Within the resulting summaries, an atomic insight reveals that blocked-shot interactions are more frequent in the first period than in the third, prompting EE to apply Elaborate to it for a deeper explanation of this temporal pattern. (E) AdaLens finally generates a report that consolidates the exploratory findings into a coherent interpretation of the game.
Fig. 7: Usability results from a 5-point SUS questionnaire [ 83 ] . Negative questions (Q2, Q4, Q6, Q8, and Q10) are shown inverted for consistency.