This book provides a comprehensive exploration of Agentic AI and its role in transforming modern enterprise systems. It explains the evolution from traditional AI to autonomous AI agents, highlighting their architecture, core components, and applications in regulated industries such as finance, healthcare, and defense. The book discusses secure enterprise AI architectures, scalable infrastructure, data flow design, and integration with legacy systems. It also emphasizes trustworthy AI principles including explainability, transparency, bias mitigation, model validation, ethical governance, and human-in-the-loop decision frameworks to ensure responsible AI adoption in enterprises.

In addition, the book covers AI security, threat management, regulatory compliance, and governance standards required for deploying secure and compliant AI systems. Readers will gain insights into adversarial attacks, runtime monitoring, autonomous AI pipelines, self-optimization mechanisms, DevSecOps practices, MLOps integration, and automated security testing. The final chapters focus on the future of Agentic AI, including autonomous enterprises, multi-agent collaboration frameworks, AI-driven decision intelligence, and industry transformation across banking, manufacturing, and defense sectors. This book serves as a valuable resource for students, researchers, AI engineers, cybersecurity professionals, and enterprise technology leaders interested in next-generation intelligent systems.