ReusStdFlow: A Standardized Reusability Framework for Dynamic Workflow Construction in Agentic AI

TL;DR

ReusStdFlow introduces a standardized framework for reusing and constructing enterprise workflows by deconstructing heterogeneous DSLs into modular segments and employing a dual knowledge architecture for accurate retrieval and assembly.

Contribution

The paper presents ReusStdFlow, a novel framework that standardizes workflow reusability through a new extraction-storage-construction paradigm and dual knowledge architecture.

Findings

Achieves over 90% accuracy in extraction and construction tasks.

Effectively reorganizes and reuses enterprise workflows.

Demonstrates scalability on 200 real-world workflows.

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.