Agentic AI Second Edition by Aravind choudhary

Agentic AI is a practical and comprehensive guide to understanding and building intelligent AI agents capable of reasoning, planning, using tools, interacting with external systems, and completing complex tasks. The book begins with the fundamentals of Artificial Intelligence, Generative AI, and Large Language Models (LLMs) and gradually progresses to advanced Agentic AI concepts. Readers will learn how AI agents use models, tools, memory, knowledge, workflows, and decision-making processes to perform tasks with greater autonomy. The book provides practical coverage of LangChain and LangGraph, including state management, nodes, edges, conditional routing, ToolNode, tool calling, human-in-the-loop workflows, middleware, and multi-agent systems. With practical examples, real-world use cases, and hands-on projects, this book helps readers develop modern AI-powered applications and understand the architecture behind intelligent and autonomous AI systems. What You Will Learn Introduction to Artificial Intelligence Introduction to Generative AI Generative AI vs Agentic AI Large Language Models (LLMs) How LLMs work Tokens and context windows Model parameters and temperature Prompt Engineering Prompt design and optimization Zero-shot, one-shot, and few-shot prompting Structured output generation Introduction to AI Agents What is Agentic AI? Agentic AI architecture Components of an AI Agent LLMs as reasoning engines Planning and decision-making Reasoning and task decomposition Agent memory Short-term and long-term memory Tool usage and external systems Tool Calling Function Calling Custom tools API and database integration Web search and external information retrieval Introduction to RAG Retrieval-Augmented Generation architecture Document processing Text splitting and chunking Embeddings and vector databases Semantic search Retrievers Building knowledge-based AI agents Introduction to LangChain LangChain architecture and components Prompt templates Chains and Runnables LangChain agents Tools and tool integration Agent memory Introduction to LangGraph LangGraph architecture Graph-based AI workflows Nodes and edges State management Conditional routing Loops and iterative workflows ToolNode Human-in-the-loop workflows Middleware Agent control and customization Multi-step AI workflows Multi-Agent Systems Agent collaboration Agent-to-agent communication Supervisor and worker agent patterns Autonomous AI workflows AI Agent planning and execution Error handling and recovery Agent evaluation and monitoring LangSmith and AI application observability Hugging Face and LLM integration Working with modern AI APIs Database and API integration AI-powered chatbots Research agents Customer support agents Data analysis agents Coding agents Document analysis agents Automation agents Security and responsible Agentic AI Prompt injection and tool security Controlling agent permissions Hallucination reduction Cost and performance optimization Deploying Agentic AI applications Real-world Agentic AI projects