As AI systems grow more capable, the next dimension shifts toward the question of the self in AI. Modern systems can reason and act across domains, yet they remain fundamentally passive, unable to evaluate their own reliability, regulate their behavior, or recognize the limits of their competence. This gap becomes increasingly consequential as systems scale. This book presents a systems-engineering framework for building autonomous intelligent systems. Here, functional self-awareness is treated as an architectural property arising from the integration of persistent self-models, metacognition, self-governance, and explicit uncertainty awareness. These mechanisms enable systems to monitor their own reasoning, constrain their actions, and remain governable over time. Drawing on control theory, AI systems engineering, and real-world failure modes, the book reframes self-aware AI as a practical requirement for safe, scalable intelligence. It off ers a rigorous, lifecycle-oriented approach for engineers, researchers, and product leaders designing AI systems that must understand and regulate themselves.
