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. 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;
- 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 data access capability and higher-level analytical quality, offering a practical foundation for reliable, traceable, and continuously improving enterprise data agents.
Blogger's Review: The design of QwenPaw-Data significantly enhances enterprise decision-making capabilities in dynamic environments by modularizing various aspects of data analysis. Its self-evolving mechanism provides robust support for the continuous optimization of data agents, heralding a major transformation in the field of data analytics.