**Agentic AI: Building Autonomous AI Agents with LLMs, LangChain, LangGraph, RAG, Tools and Real-World Projects**

Artificial Intelligence is evolving from systems that simply generate responses to intelligent agents capable of reasoning, planning, using tools, remembering information, and taking actions. **Agentic AI** introduces this next generation of AI systems and provides a practical approach to designing and developing autonomous AI agents.

This book explains the fundamental concepts behind Agentic AI and gradually moves toward practical implementation using **Large Language Models (LLMs), Prompt Engineering, RAG, Tool Calling, LangChain, LangGraph, Memory, Agent Workflows, and Multi-Agent Systems**.

You will learn how AI agents can understand user objectives, break complex problems into smaller tasks, select appropriate tools, retrieve relevant information, maintain context, and execute multi-step workflows.

### What You Will Learn

* Fundamentals of Agentic AI and AI Agents
* LLMs and their role in intelligent agents
* Prompt Engineering for AI Agents
* Tool Calling and Function Calling
* Retrieval-Augmented Generation (RAG)
* Embeddings and Vector Databases
* LangChain for building AI applications
* LangGraph for stateful and multi-step agent workflows
* Agent Memory and Context Management
* Planning, Reasoning and Decision-Making
* Human-in-the-Loop AI systems
* Multi-Agent Systems and Agent Collaboration
* Building AI agents with external tools and APIs
* Designing autonomous and semi-autonomous workflows
* Agent persistence and state management
* Real-world Agentic AI applications and projects

The book is designed with a practical, hands-on approach so that learners can move from **understanding the concepts to building functional AI agents**.

Whether you are a student, developer, software professional, AI/ML enthusiast, or technology learner, this book provides a practical foundation for exploring the rapidly growing field of **Agentic AI and autonomous intelligent systems**.