Cognitive Foundations of Agentic AI: From Theory to Practice explores the conceptual and technical underpinnings of AI systems that act with autonomy, proactivity, and social intelligence. Drawing from cognitive science, artificial intelligence, and systems theory, this book provides a structured view of how intelligent agents perceive, learn, reason, and interact in dynamic environments.
Beginning with a detailed exploration of what defines Agentic AI, the book delves into the cognitive processes that support agency—perception, learning, reasoning, memory, and decision-making. It bridges classical symbolic models with modern deep learning and neuro-symbolic systems to illustrate how hybrid architectures can enable generalizable, goal-driven behavior. Emphasis is placed on modeling real-world complexity, social cognition, and human-like interaction through language, emotional awareness, and theory of mind.
The text also critically examines challenges such as generalization, ethical alignment, uncertainty, and explainability. Through illustrative case studies in robotics, healthcare, digital assistants, and multi-agent systems, it highlights the real-world implications and limitations of agentic systems.
The final chapters outline practical pathways to building cognitive agents, including architecture design, training environments, and evaluation methods. It encourages a collaborative AI-human future where agents not only support but enhance human decision-making, learning, and creativity.
Ideal for AI practitioners, researchers, and graduate students, the book offers both a theoretical framework and practical insights into creating autonomous systems that think, learn, and act intelligently. It invites readers to rethink intelligence not as a fixed trait but as an emergent, contextual process deeply rooted in cognition.
