Agentic AI + Prompt Engineering: Next-Gen Machine Learning for IoT Intrusion Detection

Abstract

The growing use of Internet of Things (IoT) devices is expanding the attack surface. This compels the need for smarter and more dynamic intrusion detection systems (IDS). Standard machine learning methods face challenges related to scalability, location sensitivity, and adaptability. In this paper, we present a next-generation platform that combines Agentic Artificial Intelligence (AI) with prompt engineering to improve IoT intrusion detection. Agentic AI supports autonomous learning, decision-making, and adaptation. Prompt engineering dynamically guides model behavior using contextual instructions. We compare our approach with state-of-the-art machine learning and deep learning methods on benchmark IoT security datasets. Findings indicate improved accuracy, enhanced resistance to various attack models, and reduced false positives. These results suggest that Agentic AI and prompt engineering may lead to the creation of interpretable, scalable, and resilient IDS for real-world IoT applications. We discuss practical implications for smart cities, health, and industrial IoT implementations.