DA-Studio: An Agentic System for End-to-End Data Analysis

2026-06-30

AI Summary

Existing large language model (LLM)-driven data analysis tools are often confined to isolated subtasks and struggle to support end-to-end executable analytical workflows. This work proposes an autonomous, sandboxed, and auditable end-to-end system that leverages LLMs for action planning, iteratively generating structured operations, executing code in a secure environment, and integrating streaming traceability with intermediate result previews. By unifying a structured action backend, sandboxed execution, and an interactive visual interface—features integrated here for the first time—the system enables users to drive complete analytical workflows using only natural language. Users can inspect, modify, and export the entire process and its outputs directly within a web browser, ensuring full reproducibility, editability, and transparency throughout the analytical pipeline.

Abstract

Real-world data analysis is a multi-step process over heterogeneous inputs rather than merely producing a final answer. A practical system should autonomously organize multi-step workflows, execute generated code in a sandboxed and controllable environment, and remain inspectable through visible action traces and intermediate artifacts. Existing LLM-based analysis tools, however, often emphasize isolated subtasks, leaving limited support for complete execution-grounded workflows. We present DA-Studio (Data Analysis Studio), an interactive web-based demo system for end-to-end data analysis that is autonomous, sandboxed, and inspectable. DA-Studio integrates an action-structured analysis backend, a sandboxed execution workspace, and a browser interface for task setup, streamed action traces, artifact preview, code editing and rerunning, and report export. Through iterative action generation, code execution, and feedback incorporation, it incrementally constructs executable analysis steps from raw files and natural-language requests while exposing intermediate results and artifacts throughout the process.

Problem

• data analysis
• end-to-end workflow
• LLM-based tools
• executable workflow
• action trace

Innovation

• agentic system
• end-to-end data analysis
• sandboxed execution
• action trace
• interactive workflow