解决企业AI工作流复用难题，实现跨平台流程自动重组。
ReusStdFlow: A Standardized Reusability Framework for Dynamic Workflow Construction in Agentic AI
- 将不同平台的DSL拆解为标准化模块，统一管理
- 在200个真实n8n流程上提取与构建准确率超90%
- 适合需要自动化流程复用的企业AI系统开发者
为解决企业级智能体AI中的“复用困境”和结构幻觉问题，本文提出ReusStdFlow框架，采用全新的“提取-存储-构建”范式。该框架将异构、平台特定的领域专用语言（DSL）解构为标准化、模块化的工作流片段，并利用图数据库与向量数据库相结合的双知识架构，协同检索拓扑结构与功能语义。最终通过检索增强生成（RAG）策略智能组装工作流。在200个真实n8n工作流上的测试表明，该系统在提取与构建任务中均达到90%以上的准确率。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。
原文摘要 · Abstract (English)
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.
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