Computing and Networks- Agentic AI

This co-authored book explains how agentic AI differs from conventional AI, how these agents are architected internally, how they collaborate in multi-agent systems, and how to design, deploy, and govern them in real-world environments. Agentic AI refers to autonomous systems driven by large language models (LLMs) that act as intelligent agents, capable of perceiving environments, setting goals, creating multi-step plans, making decisions, and executing complex, multi-step workflows with minimal human oversight. Unlike passive chatbots and traditional AI tools that simply answer prompts, agentic AI actively reasons, uses digital tools, and adapts processes across various applications. It is rapidly emerging as the next major paradigm in automation, AI engineering, and enterprise transformation. In this comprehensive and industry-aligned co-authored reference, the authors explain how agents differ from conventional AI, how they are architected internally, how they collaborate in multi-agent systems, how to design, deploy, and govern them in real-world environments. They cover agentic AI foundations, design patterns, advanced multi-agent systems architecture, deployment models, testing and governance. The book blends academic clarity with industry relevance, making it ideal for researchers exploring agentic behaviour and multi-agent design; industry professionals implementing enterprise AI workflow; advanced university students and lecturers in AI, automation, and intelligent systems; and students preparing for the next wave of AI jobs and research directions. The authors have provided a wealth of supplementary material with 60+ supporting YouTube videos for learners, 24 hands-on projects with accompanying GitHub repositories, and QR codes integrated throughout the book for seamless access.