🤖 AI Summary
To address the challenge of decentralized multi-agent coordination in cloud-edge heterogeneous environments lacking centralized server support, this paper proposes a decentralized multi-agent collaboration framework. The framework introduces a novel logistics-object modeling approach and an abstract agent interface, enabling dynamic service-flow orchestration, topology-aware distributed publish-subscribe communication, and many-to-many service election. It integrates plug-and-play node discovery, flexible task reconfiguration, and fault-adaptive agent replacement. By synergistically combining multi-agent systems (MAS), fault-tolerant scheduling, and dynamic topology management, the framework achieves autonomous, real-time, and scalable decision-making coordination without a central coordinator. Experimental results demonstrate significant improvements in system resilience, real-time performance, and robustness for mission-critical autonomous scenarios, confirming its capability for highly available deployment.
📝 Abstract
This paper presents AgentFlow, a MAS-based framework for programmable distributed systems in heterogeneous cloud-edge environments. It introduces logistics objects and abstract agent interfaces to enable dynamic service flows and modular orchestration. AgentFlow supports decentralized publish-subscribe messaging and many-to-many service elections, enabling decision coordination without a central server. It features plug-and-play node discovery, flexible task reorganization, and highly adaptable fault tolerance and substitution mechanisms. AgentFlow advances scalable, real-time coordination for resilient and autonomous mission-critical systems.