SwarmAgentic: Towards Fully Automated Agentic System Generation via
  Swarm Intelligence
Authors: name: Yao Zhang, name: Chenyang Lin, name: Shijie Tang, name: Haokun Chen, name: Shijie Zhou, name: Yunpu Ma, name: Volker Tresp

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 https://yaoz720.github.io/SwarmAgentic/.