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

TL;DR
SwarmAgentic introduces a fully automated framework for generating and optimizing agentic systems from scratch using swarm intelligence, significantly improving performance on complex, open-ended tasks.
Contribution
It presents a novel, fully automated system generation method combining swarm intelligence with language-driven exploration for scalable agentic system design.
Findings
Achieved +261.8% improvement over ADAS on TravelPlanner benchmark.
Successfully generated agentic systems from scratch for complex tasks.
Demonstrated effective joint optimization of agent functionality and collaboration.
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
Taxonomy
TopicsMulti-Agent Systems and Negotiation · Modular Robots and Swarm Intelligence · Evolutionary Algorithms and Applications
