@inproceedings{zhang-etal-2025-swarmagentic,
title = "{S}warm{A}gentic: Towards Fully Automated Agentic System Generation via Swarm Intelligence",
author = "Zhang, Yao and
Lin, Chenyang and
Tang, Shijie and
Chen, Haokun and
Zhou, Shijie and
Ma, Yunpu and
Tresp, Volker",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.93/",
pages = "1778--1818",
ISBN = "979-8-89176-332-6",
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**, the *first framework that fully automates agentic system generation, optimization, and collaboration*, constructing agents from scratch and jointly refining functionality and coordination via 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."
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<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**, the *first framework that fully automates agentic system generation, optimization, and collaboration*, constructing agents from scratch and jointly refining functionality and coordination via 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.</abstract>
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%0 Conference Proceedings
%T SwarmAgentic: Towards Fully Automated Agentic System Generation via Swarm Intelligence
%A Zhang, Yao
%A Lin, Chenyang
%A Tang, Shijie
%A Chen, Haokun
%A Zhou, Shijie
%A Ma, Yunpu
%A Tresp, Volker
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F zhang-etal-2025-swarmagentic
%X 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**, the *first framework that fully automates agentic system generation, optimization, and collaboration*, constructing agents from scratch and jointly refining functionality and coordination via 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.
%U https://aclanthology.org/2025.emnlp-main.93/
%P 1778-1818
Markdown (Informal)
[SwarmAgentic: Towards Fully Automated Agentic System Generation via Swarm Intelligence](https://aclanthology.org/2025.emnlp-main.93/) (Zhang et al., EMNLP 2025)
ACL