Engineering Functional Self-Awareness in AI Systems: From Metacognition to Closed-Loop Autonomy

As AI systems grow more capable, the next dimension shifts toward thequestion 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 gapbecomes increasingly consequential as systems scale.This book presents a systems-engineering framework for building autonomousintelligent systems. Here, functional self-awareness is treated as anarchitectural property arising from the integration of persistent self-models,metacognition, self-governance, and explicit uncertainty awareness. Thesemechanisms enable systems to monitor their own reasoning, constrain theiractions, and remain governable over time.Drawing on control theory, AI systems engineering, and real-world failuremodes, the book reframes self-aware AI as a practical requirement for safe,scalable intelligence. It off ers a rigorous, lifecycle-oriented approach forengineers, researchers, and product leaders designing AI systems that mustunderstand and regulate themselves.