Journal of Artificial Intelligence & Cloud Computing

The emergence of Generative Artificial Intelligence (GenAI) has unleashed its operational capabilities to bring about a revolution for many autonomous systems, especially those in the domain of the Internet of Things (IoT). This paper explores a new mechanism for promoting generative reasoning in autonomous IoT agents for dynamic, context-situated planning and decision-making. The agents use generative models to simulate highly intricate emerging scenarios of the environment and system and can react to them in real time. The demonstration of the framework on smart agricultural systems, where agents manage irrigation and pest control tasks autonomously on a preliminary basis, was highly encouraging in significant improvements of resource efficiency and yield productivity. The approach proposed here marries reinforcement learning, scenario simulation, and adaptive proactive mechanisms to rid most of the challenges facing the lately built reactive IoT framework. Hence, the agents imbued with generative reasoning can decide based not only on sensor data but rather also on predicted-and-anticipated outcomes, thus dealing with the changing scenarios with the appropriate strategy-making. The generative cognitive architecture shows utmost potential for transforming autonomous systems in agriculture, transportation, and energy sectors. Specific areas around multi-agent collaboration, secure deployment, and ethical issues regarding autonomous decisions in the future are elaborated in the presented study