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[2602.14922] ReusStdFlow: A Standardized Reusability Framework for Dynamic Workflow Construction in Agentic AI
Summary
The paper presents ReusStdFlow, a framework designed to enhance the reusability of workflows in Agentic AI by standardizing Domain Specific Languages into modular segments, achieving over 90% accuracy in workflow construction.
Why It Matters
As enterprises increasingly rely on AI for dynamic workflows, the challenge of reusability becomes critical. ReusStdFlow addresses this by providing a structured approach to workflow construction, which can lead to more efficient use of digital assets and improved AI performance.
Key Takeaways
- ReusStdFlow tackles the reusability dilemma in enterprise AI.
- The framework uses a novel Extraction-Storage-Construction paradigm.
- It integrates graph and vector databases for enhanced data retrieval.
- The system has been tested on real-world workflows with over 90% accuracy.
- Standardization of DSLs can lead to more efficient AI workflow management.
Computer Science > Artificial Intelligence arXiv:2602.14922 (cs) [Submitted on 16 Feb 2026] 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 View a PDF of the paper titled ReusStdFlow: A Standardized Reusability Framework for Dynamic Workflow Construction in Agentic AI, by Gaoyang Zhang and 4 other authors View PDF HTML (experimental) 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. Subjects: Artificial Intelligence (cs.AI); Software Engineering (cs.SE) Cite as: arXiv:2602.14922 [cs.AI] (or arXiv:2602.14922v1 [cs.AI] for this v...
