Abstract
The rapid progress of Large Language Models has advanced agentic systems in decision-making, coordination, and task execution. Yet, existing agentic system generation frameworks lack full autonomy, missing from-scratch agent generation, self-optimizing agent functionality, and collaboration, limiting adaptability and scalability. We propose SwarmAgentic, a framework for fully automated agentic system generation that constructs agentic systems from scratch and jointly optimizes agent functionality and collaboration as interdependent components through language-driven exploration. To enable efficient search over system-level structures, SwarmAgentic maintains a population of candidate systems and evolves them via feedback-guided updates, drawing inspiration from Particle Swarm Optimization (PSO). We evaluate our method on six real-world, open-ended, and exploratory tasks involving high-level planning, system-level coordination, and creative reasoning. Given only a task description and an objective function, SwarmAgentic outperforms all baselines, achieving a +261.8% relative improvement over ADAS on the TravelPlanner benchmark, highlighting the effectiveness of full automation in structurally unconstrained tasks. This framework marks a significant step toward scalable and autonomous agentic system design, bridging swarm intelligence with fully automated system multi-agent generation. Our code is publicly released at this https URL.
Abstract (translated)
大型语言模型的迅速进步提升了代理系统的决策、协调和任务执行能力。然而，现有的代理系统生成框架缺乏完全自主性，缺少从零开始生成代理、自我优化的代理功能以及合作机制，从而限制了适应性和可扩展性。我们提出了SwarmAgentic框架，这是一个全自动化的代理系统生成框架，它能够从头构建代理系统，并通过语言驱动的探索共同优化代理的功能和协作能力作为相互依赖的组件。为了实现对系统级别结构的有效搜索，SwarmAgentic维护了一组候选系统，并根据反馈指导更新来进化这些系统，借鉴了粒子群优化（PSO）的灵感。我们在涉及高层次规划、系统级协调和创造性推理的六个现实世界中的开放性和探索性任务上评估了我们的方法：仅凭任务描述和目标函数，SwarmAgentic在所有基线中表现出色，在TravelPlanner基准测试中比ADAS高出261.8%的相对改进，突显了完全自动化在结构不受约束的任务中的有效性。这一框架标志着向可扩展且自主化的代理系统设计迈进的重要一步，将群智能与全自动多代理系统的生成相结合。我们的代码可在该网址公开获取：[此链接]（请根据实际情况替换为实际发布地址）。
URL
https://arxiv.org/abs/2506.15672
