ReusStdFlow: A Standardized Reusability Framework for Dynamic Workflow Construction in Agentic AI
This provides a standardized solution for automated reorganization and efficient reuse of enterprise digital assets, addressing a domain-specific problem in Agentic AI.
The paper tackles the reusability dilemma and structural hallucinations in enterprise Agentic AI by proposing ReusStdFlow, a framework that deconstructs heterogeneous DSLs into standardized workflow segments and assembles them using RAG, achieving over 90% accuracy in extraction and construction on 200 real-world n8n workflows.
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.