Self-Organizing Agent Network for LLM-based Workflow Automation

2025-08-19

AI Summary

Real-world enterprise workflows often exhibit deep nesting and long execution paths, causing LLM-driven orchestration to fail due to excessively long reasoning chains and combinatorial state-space explosion. To address this, we propose a structure-driven, self-organizing agent network framework. Our approach introduces— for the first time—structured unit identification and dynamic encapsulation, enabling redundant sub-processes to be abstracted into reusable, modular agents; it further integrates formal state-transition modeling to support progressive, hierarchical workflow construction. This design significantly enhances orchestration controllability, scalability, and fault tolerance. Evaluated on multiple benchmarks and real-world enterprise datasets, our framework achieves substantially higher execution success rates than current state-of-the-art methods, demonstrating superior adaptability and robustness under complex, evolving workflow conditions.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Modeling other Agents
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Recent multi-agent frameworks built upon large language models (LLMs) have demonstrated remarkable capabilities in complex task planning. However, in real-world enterprise environments, business workflows are typically composed through modularization and reuse of numerous subprocesses, resulting in intricate workflows characterized by lengthy and deeply nested execution paths. Such complexity poses significant challenges for LLM-driven orchestration, as extended reasoning chains and state-space explosions severely impact planning effectiveness and the proper sequencing of tool invocations. Therefore, developing an orchestration method with controllable structures capable of handling multi-layer nesting becomes a critical issue. To address this, we propose a novel structure-driven orchestration framework Self-Organizing Agent Network (SOAN). SOAN incrementally builds a formalized agent network by identifying and encapsulating structural units as independent agents, enhancing modularity and clarity in orchestration. Extensive evaluations were performed using multiple benchmarks as well as a real-world enterprise workflow dataset. Experimental results demonstrate that SOAN significantly outperforms state-of-the-art methods in terms of adaptability, fault tolerance, and execution efficiency.

Problem

• Addresses complex nested workflow challenges in enterprise environments
• Overcomes state-space explosions in LLM-driven task orchestration
• Develops controllable structures for multi-layer nesting automation

Innovation

• Self-Organizing Agent Network framework
• Incrementally builds formalized agent network
• Encapsulates structural units as agents