ReusStdFlow: A Standardized Reusability Framework for Dynamic Workflow Construction in Agentic AI
- URL: http://arxiv.org/abs/2602.14922v1
- Date: Mon, 16 Feb 2026 16:56:53 GMT
- Title: ReusStdFlow: A Standardized Reusability Framework for Dynamic Workflow Construction in Agentic AI
- Authors: Gaoyang Zhang, Shanghong Zou, Yafang Wang, He Zhang, Ruohua Xu, Feng Zhao,
- Abstract summary: ReusStdFlow is a framework centered on a novel Extraction-Construction'' paradigm.<n>It employs a dual knowledge architecture-integrating graph and vector databases-to facilitate retrieval of both topological structures and functional semantics.<n>Tested on 200 real-world n8n, the system achieves over 90% accuracy in both extraction and construction.
- Score: 11.130786494338876
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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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