SwarmAgentic: Towards Fully Automated Agentic System Generation via Swarm Intelligence

ACL ARR 2025 May Submission1256 Authors

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 functionality, and collaboration, limiting adaptability and automation. We propose **SwarmAgentic**, a framework for fully automated agentic system generation, extending Particle Swarm Optimization (PSO) into a language-driven search space for structure-level optimization. SwarmAgentic instantiates agents from scratch and jointly optimizes agent functionality and collaboration as interdependent components. 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.

Paper Type: Long

Research Area: Generation

Research Area Keywords: NLP Applications, Dialogue and Interactive Systems, AI Agent, Agentic Workflow

Contribution Types: NLP engineering experiment, Publicly available software and/or pre-trained models

Languages Studied: English

Keywords: NLP Applications, Dialogue and Interactive Systems, AI Agent, Agentic Workflow

Submission Number: 1256