Decentralized Coordination Architectures for Intelligent Agent Swarms

Abstract

Large swarms of intelligent agents are useful only when coordination still works after the tidy assumptions of a laboratory test disappear. That is the practical problem behind decentralized coordination architectures. The review follows the field from graph-based modeling and consensus theory to the less tidy questions that appear in deployment: delayed messages, changing topology, faulty agents, and limited computation. The discussion treats distributed consensus, event-triggered communication, resilient control, fault-tolerant design, and cognition-inspired adaptation as parts of one architecture problem. The literature from 2020 to 2025 suggests a clear pattern. Stabilizing high-order nonlinear swarms is still difficult; robust performance under intermittent connectivity remains fragile; and adaptive decision-making is not yet fully reliable when the environment keeps changing. A combined route is needed, one in which cognitive intelligence, edge-side computing, and verification tools are developed together. Such a route is especially relevant to intelligent manufacturing, emergency response, autonomous transportation, and other settings where a centralized command chain may be too slow or too vulnerable.