A Systematic Review of Autonomous Agentic AI Architectures and Orchestration Strategies for Edge-to-Cloud Internet of Things Deployments
Autonomous Agentic AI extends conventional Internet of Things (IoT) intelligence from isolated inference toward goal-directed systems that perceive, maintain state, plan, invoke services or actuators, coordinate with other agents, and adapt from feedback. Deploying such capabilities across device, edge, fog, and cloud tiers creates a coupled systems problem: cognitive architecture, organizational topology, workload placement, state management, and trust controls must be designed jointly. This systematic review maps and synthesizes peer-reviewed research on agentic and multi-agent architectures, edge intelligence, computation offloading, and continuum orchestration. A reproducible search of OpenAlex used 20 concept queries and 19 foundational-title lookups, followed by metadata screening and DOI-level Crossref verification. From 765 unique records, 251 entered detailed screening, 155 passed topical and quality criteria, and 153 peer-reviewed studies published between 2008 and 2026 formed the final corpus. The evidence was coded by agent paradigm, continuum tier, orchestration mechanism, evaluation maturity, and primary contribution. Agentic or multi-agent architecture was the largest evidence stream (67 studies; 43.8%), while optimization (82; 53.6%), reinforcement learning (44; 28.8%), and multi-agent reinforcement learning (40; 26.1%) were the most frequent orchestration mechanisms. However, 62 studies (40.5%) relied primarily on simulation, whereas only one (0.7%) reported an operational or pilot deployment. The synthesis yields a four-tier reference architecture with a cross-cutting control and assurance plane, a five-family orchestration taxonomy, and a deployment decision framework. The main conclusion is that practical Agentic IoT depends less on placing an entire agent at one tier than on partitioning perception, memory, reasoning, and action under explicit latency, privacy, reliability, and safety constraints. Standardized benchmarks, stateful-agent migration protocols, interoperable capability descriptions, and longitudinal operational evidence remain urgent research priorities.