Leveraging large language models (LLMs), autonomous agents have significantly improved, gaining the ability to handle a variety of tasks. In open-ended settings, optimizing collaboration for efficiency and effectiveness demands flexible adjustments. Despite this, current research mainly emphasizes fixed, task-oriented workflows and overlooks agent-centric organizational structures. Drawing inspiration from human organizational behavior, we introduce a self-organizing agent system (S-Agents) with a "tree of agents" structure for dynamic workflow, an "hourglass agent architecture" for balancing information priorities, and a "non-obstructive collaboration" method to allow asynchronous task execution among agents. This structure can autonomously coordinate a group of agents, efficiently addressing the challenges of an open and dynamic environment without human intervention. Our experiments demonstrate that S-Agents proficiently execute collaborative building tasks and resource collection in the Minecraft environment, validating their effectiveness.

翻译：借助大型语言模型（LLMs），自主智能体能力显著提升，可处理多种任务。在开放环境中，优化协作以实现高效性与有效性需要灵活调整。然而当前研究主要侧重于固定任务导向的工作流，忽视了以智能体为中心的组织结构。受人类组织行为启发，我们提出了一种自组织智能体系统（S-Agents），该系统采用"智能体树"结构实现动态工作流，"沙漏型智能体架构"平衡信息优先级，并通过"非阻塞式协作"方法支持智能体间异步任务执行。该结构无需人工干预，可自主协调智能体群体，高效应对开放动态环境的挑战。实验表明，S-Agents系统能在Minecraft环境中熟练执行协作建造任务与资源收集工作，验证了其有效性。