A Dynamic Spawner-Agent Framework for Scalable Autonomous Distributed Multi-Agent System

Abstract

Autonomous Multi-Agent Systems (MAS) have gained significant traction for addressing complex, distributed computational tasks through intelligent agent collaboration. However, most current architectures rely on static agent configurations and centralized coordination, which severely limits scalability, fault tolerance, and adaptability under dynamic workloads. This paper presents a Dynamic Spawner-Agent (DSA) framework that supports on-demand agent creation, decentralized task coordination, and adaptive lifecycle management. The proposed system comprises four core components: a spawner controller, a dynamic agent factory, a task coordination manager, and a shared knowledge repository. A bidding-driven task allocation mechanism allows agents to self-assess task feasibility, enabling fair workload distribution and minimized execution bottlenecks. Asynchronous communication via Redis and containerized deployment using Docker provide elastic scalability and rapid fault recovery. The system is validated through a distributed implementation demonstrating measurable improvements in coordination efficiency, scalability, and fault recovery over static baseline architectures. These results underscore the necessity of dynamic, decentralized coordination for deploying MAS in real-world applications such as robotics, logistics, smart infrastructure, and large-scale distributed systems.

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Graphical Abstract

Novelty Statement

Unlike existing static MAS architectures, the proposed DSA framework uniquely combines run-time agent spawning, a bidding-based decentralized task allocation mechanism, LLM-integrated decision support, and containerized fault recovery within a single cohesive backend system. This integration enables controlled autonomy, elastic scalability, and robust coordination for complex distributed environments.