ReusStdFlow：一种面向代理AI动态工作流构建的标准可重用框架
ReusStdFlow: A Standardized Reusability Framework for Dynamic Workflow Construction in Agentic AI
AI总结：
ReusStdFlow通过提取-存储-构建范式，实现企业代理AI中动态工作流的标准化可重用框架，提升工作流构建的准确率与效率。
摘要：
为了解决企业代理AI中的'可重用性困境'和结构幻觉问题，本文提出了ReusStdFlow框架，该框架围绕一种新的'提取-存储-构建'范式展开。该框架将异构、平台特定的领域特定语言（DSL）分解为标准化、模块化的工作流片段。它采用双知识架构——整合图数据库和向量数据库——来促进拓扑结构和功能语义的协同检索。最后，通过检索增强生成（RAG）策略智能地组装工作流。在200个真实世界n8n工作流上测试，系统在提取和构建方面均达到超过90%的准确率。该框架为企业数字资产的自动化重组和高效重用提供了标准化解决方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
