# Self-Organizing Agent Network for LLM-based Workflow Automation

arXiv: https://arxiv.org/abs/2508.13732 · HTML: https://mlsanity.com/paper/2508.13732/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 13 of its 28 references that are in the map.
- First posted on arXiv 19 Aug 2025.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 13 of 13)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://mlsanity.com/paper/2307.16789/) (2023)
- [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023)
- [Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models](https://mlsanity.com/paper/2310.04406/) (2023)
- [A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration](https://mlsanity.com/paper/2310.02170/) (2023)
- [TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs](https://mlsanity.com/paper/2303.16434/) (2023)
- [ADaPT: As-Needed Decomposition and Planning with Language Models](https://mlsanity.com/paper/2311.05772/) (2023)
- [EvoFlow: Evolving Diverse Agentic Workflows On The Fly](https://mlsanity.com/paper/2502.07373/) (2025)
- [FlowMind: Automatic Workflow Generation with LLMs](https://mlsanity.com/paper/2404.13050/) (2024)
- [MobileExperts: A Dynamic Tool-Enabled Agent Team in Mobile Devices](https://mlsanity.com/paper/2407.03913/) (2024)
- [nvAgent: Automated Data Visualization from Natural Language via Collaborative Agent Workflow](https://mlsanity.com/paper/2502.05036/) (2025)
- [AI Agentic workflows and Enterprise APIs: Adapting API architectures for the age of AI agents](https://mlsanity.com/paper/2502.17443/) (2025)

## Built on by (most cited first, 12 of 12)

- [Code as Agent Harness](https://mlsanity.com/paper/2605.18747/) (2026)
- [A Survey of Agent Memory in the Second Half: Towards Self-Evolving and Long-Horizon Agents](https://mlsanity.com/paper/2602.06052/) (2026)
- [Distributional AGI Safety](https://mlsanity.com/paper/2512.16856/) (2025)
- [Federation of Agents: A Semantics-Aware Communication Fabric for Large-Scale Agentic AI](https://mlsanity.com/paper/2509.20175/) (2025)
- [Can Agents Secure Hardware? Evaluating Agentic LLM-Driven Obfuscation for IP Protection](https://mlsanity.com/paper/2604.13298/) (2026)
- [Graph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligence](https://mlsanity.com/paper/2608.21156/) (2026)
- [Quantitative Certification of Agentic Tool Selection](https://mlsanity.com/paper/2510.03992/) (2025)
- [Tools as Continuous Flow for Evolving Agentic Reasoning](https://mlsanity.com/paper/2605.07339/) (2026)
- [Liquid Interfaces: A Dynamic Ontology for the Interoperability of Autonomous Systems](https://mlsanity.com/paper/2601.21993/) (2026)
- [Vibe AIGC: A New Paradigm for Content Generation via Agentic Orchestration](https://mlsanity.com/paper/2602.04575/) (2026)
- [Facts Without Rules: Boundary Metadata Collapse in Multi-Agent LLM Handoffs](https://mlsanity.com/paper/2608.29028/) (2026)
- [Delegated Misalignment: How Multi-Agent Structures Amplify LLM Safety Risks](https://mlsanity.com/paper/2609.27900/) (2026)

## Abstract (arXiv)

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.