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 open and dynamic environments 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：利用大型语言模型，自主智能体能力显著提升，能够处理多种任务。在开放式环境中，为优化协作效率与效能，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程，忽视了以智能体为中心的组织结构。受人类组织行为启发，我们提出一种自组织智能体系统，包含用于动态工作流的“智能体树”结构、用于平衡信息优先级的“沙漏型智能体架构”，以及允许智能体异步执行任务的“非阻塞协作”方法。该系统无需人工干预即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能在Minecraft环境中熟练执行协作建造任务与资源收集任务，验证了其有效性。