QwenPaw-Data: Bridging Facts, Methodology, and Execution for Autonomous Enterprise Data Analytics

Jul 16, 2026

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Enterprise data analysis is emerging as a frontier for autonomous agents, operating in open and continuously evolving environments where business concepts must map to correct entities, analytical procedures remain reproducible under fuzzy feedback, and long-horizon workflows execute on real enterprise data while preserving artifacts and provenance. These requirements motivate a data-agent architecture that treats semantics, methodology, execution, and evolution as first-class concerns. QwenPaw-Data unifies heterogeneous enterprise assets into reusable analysis resources and converts natural-language requests into end-to-end workflows for understanding, retrieval, analysis, reporting, and decision support.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages23

Reading Time58 min

Introduction

Background

Distinctions and core boundaries for data agents

Proposed solution and system overview

arXiv :2607 . 1 10 19v 1 [ cs .AI] 13 Jul 2026 QwenPaw-Data: Bridging Facts, Methodology, and Execution for Autonomous Enterprise Data Analytics (Technical Report) Tianjing Zeng†, Yuntao Hong†, Zhongjun Ding∗ , Dandan Liu∗ , Yinan Mei∗ , Yunxiang Su∗ , Yiming Wang∗ , Xiaojian Zhang∗ , Jingyu Zhu∗ , Junhao Zhu∗ , Zhuowen Liang, Jiazhen Peng, Lianggui Weng, Zhihao Ding, Kerui Yi, Qifeng Wang, Rong Zhu‡, Bolin Ding‡, Liyu Mou‡, Jingren Zhou‡ Alibaba Group Abstract Enterprise data analysis is emerging as a distinct frontier for autonomous agents. Compared with general-purpose interaction and software engineering, it operates in an open, ambiguous, and continuously evolving environment: business concepts must be grounded to the right entities, analytical procedures must be reproducible despite fuzzy feedback, and long-horizon workflows must execute over real enterprise data while preserving artifacts, provenance, and opportunities for human intervention. These characteristics call for a data-agent architecture that treats semantics, methodology, execution, and evolution as first-class system concerns. To this end, we introduce QwenPaw-Data, an agentic data system designed for enterprise intelligent data analysis. QwenPaw-Data consolidates heterogeneous assets from warehouses, dashboards, documents, interaction logs, and historical tasks into reusable, governable, and evolvable analysis assets, then turns natural-language requests into end-to-end analytical workflows spanning data understanding, retrieval, analysis, report generation, and decision support. Its architecture decomposes the problem into three collaborative subsystems: DataBridge provides trustworthy semantic grounding through interconnected metadata, knowledge, and trace graphs; Skill-Hub codifies expert analytical methodology into reusable and verifiable skills; and Host materializes these evidence and method assets into controllable, artifact-centric runtime execution. Across these subsystems, semantics, methods, traces, and feedback are continuously deposited back into the system, forming a self-evolving asset flywheel. Experiments on public benchmarks and real-world industrial BI workloads show that QwenPaw-Data improves both verifiable dataaccess capability and higher-level analytical quality, offering a practical foundation for reliable, traceable, and continuously improving enterprise data agents. 1 Introduction 1.1 Background In recent time, the rapid evolution of Large Language Models (LLMs) has profoundly driven the development of autonomous agents, along with their underlying supporting frameworks, commonly referred to as harnesses. Both general-purpose agents (e.g., chatting agents) and specialized agents (e.g., coding) have achieved remarkable progress in autonomy, expanded task scope, and engineering practicality. Modern coding agents have transcended the early limitations of generating isolated † ∗ ‡ Co-first authors. Equal contribution (listed in alphabetical order) . Corresponding authors. Primary contact email: [red.zr@alibaba-inc.com](red.zr@alibaba-inc.com) (Rong Zhu). code snippets, acquiring the capability to independently manage task decomposition and debugging across the software development life cycle. Representative examples, such as OpenAI’s CodeX, Anthropic’s Claude Code, and frameworks like SWE-agent 2, demonstrate the ability to navigate complex codebases and resolve real-world issues. Currently, agent engineering has transitioned to scalable deployment, with industry surveys indicating that more than half of organizations have integrated agents into production environments [LangChain, 2026] . In the realm of data science, data agents have emerged as vital instruments for assisting data analysts (DA) and data scientists (DS) . As an emerging “blue ocean” in the AI landscape, this field is increasingly attracting widespread attention for its capacity to drive profound business impact and strategic

FAQs

What makes autonomous enterprise data analysis different from general-purpose agent tasks?

It operates in open, ambiguous, continuously evolving environments where semantics must align with the right entities, procedures must be reproducible under fuzzy feedback, and workflows must run over real enterprise data while preserving artifacts and provenance.

What is QwenPaw-Data and what problem does it target?

QwenPaw-Data is an agentic data system for enterprise intelligent data analysis, designed to ground semantics, codify reusable analytical methodology, and execute end-to-end workflows over enterprise data with continuous evolution.

How does QwenPaw-Data structure its system to support reliable analytics?

It decomposes the solution into three collaborative subsystems: DataBridge for trustworthy semantic grounding via metadata and trace graphs, Skill-Hub for reusable and verifiable analysis skills, and Host for controllable, artifact-centric runtime execution, with feedback continuously deposited back into the system.

Jul 16, 2026

Upload by Eden