Artificial Intelligence is transitioning from passive predictive models to Agentic AI--systems capable of autonomous reasoning, goal-directed behavior, and dynamic interaction with complex environments. This book provides a comprehensive examination of this paradigm, integrating conceptual foundations with architectural frameworks and diverse real-world applications. It clarifies key definitions of agency, critically analyzes agent capabilities and limitations, and details the core mechanics of autonomous reasoning that distinguish agentic systems from traditional automation.
The book surveys emerging methodologies, including the evolution from Retrieval-Augmented Generation (RAG) to Agentic RAG, multimodal curiosity mechanisms in language models, and hybrid LLM-optimizer approaches for complex scheduling and orchestration. It further bridges theory and practice through domain-specific chapters addressing deployment in healthcare--emphasizing explainable interpretation for EEG-based classification--enterprise BPM and workflow-centric systems, and cyber-physical infrastructures. Technical contributions also examine tool-based multi-agent engineering workflows, such as automated CAD-to-mesh transformation. Concluding with a critical assessment of challenges and opportunities, the volume offers a rigorous reference for researchers and practitioners advancing autonomous intelligent systems.
