To address the ``reusability dilemma'' and structural hallucinations in enterprise Agentic AI,this paper proposes ReusStdFlow, a framework centered on a novel ``Extraction-Storage-Construction'' paradigm. The framework deconstructs heterogeneous, platform-specific Domain Specific Languages (DSLs) into standardized, modular workflow segments. It employs a dual knowledge architecture-integrating graph and vector databases-to facilitate synergistic retrieval of both topological structures and functional semantics. Finally, workflows are intelligently assembled using a retrieval-augmented generation (RAG) strategy. Tested on 200 real-world n8n workflows, the system achieves over 90% accuracy in both extraction and construction. This framework provides a standardized solution for the automated reorganization and efficient reuse of enterprise digital assets.

翻译：为应对企业级智能体AI中的“可复用性困境”与结构幻觉问题，本文提出ReusStdFlow框架，其核心是一种新颖的“抽取-存储-构建”范式。该框架将异构的、平台特定的领域特定语言（DSL）解构为标准化的模块化工作流片段，并采用融合图数据库与向量数据库的双重知识架构，以协同检索拓扑结构与功能语义。最终，工作流通过检索增强生成（RAG）策略进行智能组装。在200个真实n8n工作流上的测试表明，系统在抽取与构建环节均达到超过90%的准确率。该框架为企业数字资产的自动化重组与高效复用提供了标准化解决方案。