The paradigm of Smart Environments (SE) has transitioned from simple remote automation to proactive, context-aware ecosystems driven by Artificial Intelligence. However, orchestrating heterogeneous AI subsystems—which range from high-frequency sensor streams to computationally intensive Large Language Models (LLMs)—imposes significant challenges regarding latency, interoperability, and data privacy. In this paper, we propose \textbf{Argus}, a distributed, event-driven software architecture designed to orchestrate intelligent decision-making across the Edge-Cloud continuum. We validate the architecture through a reference implementation and performance evaluation, demonstrating that Argus effectively accommodates heavy computational workloads, such as generative AI and recommendation systems, to create intelligent environments that dynamically adapt to user needs.