This book offers a practical and comprehensive guide to building intelligent, autonomous AI systems using Retrieval-Augmented Generation (RAG), LangChain, and multi-agent architectures. It bridges the gap between large language model theory and real-world implementation, enabling developers to design reliable, scalable, and production-ready agentic AI applications.

The book begins by explaining the foundations of agentic AI and how it differs from traditional chatbot architectures. Readers will explore the core components of agent-based systems, including memory, reasoning loops, tool usage, and dynamic planning. It then dives into Retrieval-Augmented Generation (RAG), demonstrating how to integrate vector databases, embeddings, and knowledge grounding to reduce hallucinations and improve reliability.

Through practical examples using LangChain and vector databases such as Chroma and FAISS, the book guides readers in building single-agent and multi-agent systems capable of collaboration, task delegation, and workflow automation. Real-world use cases, including enterprise knowledge assistants, AI-driven customer support systems, and autonomous research agents, illustrate how these concepts translate into scalable applications.

Special attention is given to evaluation metrics, optimization strategies, cost control, and deployment considerations to make sure that readers can move from experimentation to production with confidence.

By the end of the book, readers will be able to architect, implement, and deploy robust agentic AI systems tailored to real-world requirements.

What you will learn:

• Build autonomous AI agents using LangChain and Python
• Implement Retrieval-Augmented Generation (RAG) for grounded AI reasoning
• Design and deploy multi-agent systems for collaboration and debate
• Add memory and planning capabilities to AI systems
• Evaluate, secure, and scale agentic AI applications for production

Who this is for:

The primary target audience consists of AI/ML engineers, data scientists, and software developers who already have foundational experience with Python, machine learning concepts, and large language models.

Dileep Kumar G is an Assistant Professor at Malla Reddy University, Hyderabad, India, with over 17 years of combined experience — 8 years in the IT industry and 9 years in academia. His professional background spans software development, data science, and AI-driven systems, giving him a unique ability to bridge real-world implementation with research-based innovation.

He specializes in Natural Language Processing (NLP), Large Language Models (LLMs), and AI-driven cybersecurity. He has authored and co-authored multiple books with international publishers, including IGI Global and Narosa Publications, and has published more than 20 national and international research papers in AI, data science, and cybersecurity.

Beyond academia, Dileep is also an active YouTuber and educator, creating accessible video content on Data Science, Machine Learning, and Artificial Intelligence, aimed at helping students, researchers, and professionals upskill in the fast-evolving AI landscape. His combined industry, academic, and community experience positions him as a practical voice in making cutting-edge AI concepts approachable for developers and researchers alike.