SwarmAgentic: Towards Fully Automated Agentic System Generation via Swarm Intelligence

• URL: http://arxiv.org/abs/2506.15672v1
• Date: Wed, 18 Jun 2025 17:54:55 GMT
• Title: SwarmAgentic: Towards Fully Automated Agentic System Generation via Swarm Intelligence
• Abstract summary: We propose SwarmAgentic, a framework for fully automated agentic system generation.<n>SwarmAgentic constructs agentic systems from scratch and jointly optimize agent functionality and collaboration.<n>We evaluate our method on six real-world, open-ended, and exploratory tasks involving high-level planning, system-level coordination, and creative reasoning.
• Score: 28.042768995386037
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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 https://yaoz720.github.io/SwarmAgentic/.

Related papers

• Swarm Skills: A Portable, Self-Evolving Multi-Agent System Specification for Coordination Engineering [50.72456732323037]
  We propose textbfSwarm Skills, a portable specification that extends the Anthropic Skills standard with multi-agent semantics.<n>Swarm Skills turns multi-agent into first-class, distributable assets that consist of roles, execution bounds, and a built-in semantic structure for self-evolution.<n>We show how Swarm Skills achieves zero-adapter cross-agent portability via progressive disclosure, enabling agent teams to self-evolve their coordination strategies without framework lock-in.
  arXiv  Detail & Related papers  (2026-05-11T06:26:58Z)
• Youtu-Agent: Scaling Agent Productivity with Automated Generation and Hybrid Policy Optimization [37.17893162265247]
  Youtu-Agent is a framework designed for the automated generation and continuous evolution of Large Language Model (LLM) agents.<n>Youtu-Agent features a structured configuration system that decouples execution environments, toolkits, and context management.<n> Experiments demonstrate that Youtu-Agent achieves state-of-the-art performance on WebWalkerQA (71.47%) and GAIA (72.8%) using open-weight models.
  arXiv  Detail & Related papers  (2025-12-31T04:17:36Z)
• Towards Efficient Agents: A Co-Design of Inference Architecture and System [66.59916327634639]
  This paper presents AgentInfer, a unified framework for end-to-end agent acceleration.<n>We decompose the problem into four synergistic components: AgentCollab, AgentSched, AgentSAM, and AgentCompress.<n>Experiments on the BrowseComp-zh and DeepDiver benchmarks demonstrate that through the synergistic collaboration of these methods, AgentInfer reduces ineffective token consumption by over 50%.
  arXiv  Detail & Related papers  (2025-12-20T12:06:13Z)
• From Agentification to Self-Evolving Agentic AI for Wireless Networks: Concepts, Approaches, and Future Research Directions [70.72279728350763]
  Self-evolving agentic artificial intelligence (AI) offers a new paradigm for future wireless systems.<n>Unlike static AI models, self-evolving agents embed an autonomous evolution cycle that updates models, tools, and in response to environmental dynamics.<n>This paper presents a comprehensive overview of self-evolving agentic AI, highlighting its layered architecture, life cycle, and key techniques.
  arXiv  Detail & Related papers  (2025-10-07T05:45:25Z)
• JoyAgent-JDGenie: Technical Report on the GAIA [27.025464023889853]
  Large Language Models are increasingly deployed as autonomous agents for complex real-world tasks.<n>We propose a generalist agent architecture that integrates planning and execution agents with critic model voting, a hierarchical memory system spanning working, semantic, and procedural layers, and a refined tool suite for search, code execution, and multimodal parsing.
  arXiv  Detail & Related papers  (2025-10-01T04:41:58Z)
• $Agent^2$: An Agent-Generates-Agent Framework for Reinforcement Learning Automation [5.325886106098561]
  Reinforcement learning (RL) agent development traditionally requires substantial expertise and iterative effort.<n>This paper introduces Agent$2$, an LLM-driven agent-generates-agent framework for fully automated RL agent design.<n>Agent$2$ translates natural language task descriptions and environment code into executable RL solutions without human intervention.
  arXiv  Detail & Related papers  (2025-09-16T02:14:39Z)
• A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems [53.37728204835912]
  Most existing AI systems rely on manually crafted configurations that remain static after deployment.<n>Recent research has explored agent evolution techniques that aim to automatically enhance agent systems based on interaction data and environmental feedback.<n>This survey aims to provide researchers and practitioners with a systematic understanding of self-evolving AI agents.
  arXiv  Detail & Related papers  (2025-08-10T16:07:32Z)
• MetaAgent: Automatically Constructing Multi-Agent Systems Based on Finite State Machines [23.407716896592383]
  We propose a finite state machine based framework that can automatically generate a multi-agent system.<n>Given a task description, MetaAgent will design a multi-agent system and polish it through an optimization algorithm.<n>When the multi-agent system is deployed, the finite state machine will control the agent's actions and the state transitions.
  arXiv  Detail & Related papers  (2025-07-30T12:22:30Z)
• Neural Orchestration for Multi-Agent Systems: A Deep Learning Framework for Optimal Agent Selection in Multi-Domain Task Environments [0.8287206589886881]
  We propose MetaOrch, a neural orchestration framework for optimal agent selection in multi-domain task environments.<n>A novel fuzzy evaluation module scores agent responses along completeness, relevance, and confidence dimensions, generating soft supervision labels for training the orchestrator.<n>Experiments in simulated environments with heterogeneous agents demonstrate that our approach achieves 86.3% selection accuracy.
  arXiv  Detail & Related papers  (2025-05-03T02:58:25Z)
• Toward Super Agent System with Hybrid AI Routers [19.22599167969104]
  Super agents can fulfill diverse user needs, such as summarization, coding, and research.<n>To make such an agent viable for real-world deployment and accessible at scale, significant optimizations are required.<n>This paper presents a design of the Super Agent System.
  arXiv  Detail & Related papers  (2025-04-11T00:54:56Z)
• AutoAgent: A Fully-Automated and Zero-Code Framework for LLM Agents [4.57755315319748]
  Large Language Model (LLM) Agents have demonstrated remarkable capabilities in task automation and intelligent decision-making.<n>These frameworks predominantly serve developers with extensive technical expertise.<n>Only 0.03 % of the global population possesses the necessary programming skills.
  arXiv  Detail & Related papers  (2025-02-09T16:53:56Z)
• Agent-as-a-Judge: Evaluate Agents with Agents [61.33974108405561]
  We introduce the Agent-as-a-Judge framework, wherein agentic systems are used to evaluate agentic systems. This is an organic extension of the LLM-as-a-Judge framework, incorporating agentic features that enable intermediate feedback for the entire task-solving process. We present DevAI, a new benchmark of 55 realistic automated AI development tasks.
  arXiv  Detail & Related papers  (2024-10-14T17:57:02Z)
• Gödel Agent: A Self-Referential Agent Framework for Recursive Self-Improvement [112.04307762405669]
  G"odel Agent is a self-evolving framework inspired by the G"odel machine.<n>G"odel Agent can achieve continuous self-improvement, surpassing manually crafted agents in performance, efficiency, and generalizability.
  arXiv  Detail & Related papers  (2024-10-06T10:49:40Z)
• Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence [79.5316642687565]
  Existing multi-agent frameworks often struggle with integrating diverse capable third-party agents. We propose the Internet of Agents (IoA), a novel framework that addresses these limitations. IoA introduces an agent integration protocol, an instant-messaging-like architecture design, and dynamic mechanisms for agent teaming and conversation flow control.
  arXiv  Detail & Related papers  (2024-07-09T17:33:24Z)
• EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms [55.77492625524141]
  EvoAgent is a generic method to automatically extend specialized agents to multi-agent systems.<n>We show that EvoAgent can significantly enhance the task-solving capability of LLM-based agents.
  arXiv  Detail & Related papers  (2024-06-20T11:49:23Z)
• AgentScope: A Flexible yet Robust Multi-Agent Platform [66.64116117163755]
  AgentScope is a developer-centric multi-agent platform with message exchange as its core communication mechanism. The abundant syntactic tools, built-in agents and service functions, user-friendly interfaces for application demonstration and utility monitor, zero-code programming workstation, and automatic prompt tuning mechanism significantly lower the barriers to both development and deployment.
  arXiv  Detail & Related papers  (2024-02-21T04:11:28Z)
• Model-based Reinforcement Learning for Decentralized Multiagent Rendezvous [66.6895109554163]
  Underlying the human ability to align goals with other agents is their ability to predict the intentions of others and actively update their own plans. We propose hierarchical predictive planning (HPP), a model-based reinforcement learning method for decentralized multiagent rendezvous.
  arXiv  Detail & Related papers  (2020-03-15T19:49:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.