Multi-agent LLMs on the Cloud-Edge-IoT Continuum: A Systematic Mapping Study on Architectures, Deployment, and Evaluation

Mahyar Tourchi Moghaddam, Tiziano Santilli, Leo Wilhelm Lierse, Sebastian Werner, Francesco Daghero

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,…