Abstract
Edge AI systems are emerging from the convergence of IoT, edge computing, AI, agentic AI, and embodied, physical generative edge AI delivering adaptive, autonomous behaviour under physical, cyber, and operational constraints while remaining trustworthy. This article frames edge AI as a complex system-of-systems in which hardware, software, models, and data continuously co-evolve across heterogeneous “multi-X” environments: multiple systems, modalities, and agents distributed from the edge to the cloud. The article argues that as edge AI technologies are maturing, there is a need for a standardised, application-agnostic reference architecture to provide a shared lexicon and taxonomy, reduce integration errors, and expose opportunities for reusable assets and productive interoperability and standardisation. The paper grounds this need in systems engineering and introduces a quad-optimisation paradigm for balancing competing objectives during design and operation.