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.
@article{arxiv.2602.14922,
title = {ReusStdFlow: A Standardized Reusability Framework for Dynamic Workflow Construction in Agentic AI},
author = {Gaoyang Zhang and Shanghong Zou and Yafang Wang and He Zhang and Ruohua Xu and Feng Zhao},
journal= {arXiv preprint arXiv:2602.14922},
year = {2026}
}