LLM applications are increasingly built as systems of cooperating components rather than as single prompt-response pipelines. IoT applications add constraints of their own, because sensing, reasoning, control, and actuation are spread across devices, edge nodes, and cloud services to meet latency, privacy, and bandwidth requirements. How multi-agent LLM systems are organized and deployed across this cloud-edge-IoT continuum has not been mapped systematically. We therefore conducted a systematic mapping study. From 831 records retrieved up to March 2026, we selected 45 primary studies and classified each one by architecture family, deployment pattern, application domain, and evaluation practice. We also recorded how each system was evaluated and whether its code or data are public. Five architecture families recur in the corpus. Specialized agent teams run next to sensors, planners delegate subtasks to workers, and small local models cooperate with large cloud models. A fourth group federates model adaptation across devices, and a large fifth group builds the supporting infrastructure for offloading, communication, memory, and serving. Deployment relies on six recurring patterns, ranging from local-only execution to control loops gated by policy checks or human approval. Twenty-four of the 45 studies target IoT, industrial, smart-home, healthcare, vehicular, or robotics settings, and 29 evaluate on physical hardware, where open models below 8B parameters dominate. Based on the mapping, we list the reporting items needed to make future evaluations comparable, give deployment guidance for common IoT constraints, and identify open problems.

翻译：暂无翻译