Textbook in PDF format Build a working AI agent that can reason, plan, and execute multi-step tasks! LLM-powered AI agents are the next leap in applied AI, capable of reasoning and collaboration to achieve even complex, multi-step goals. Using new protocols like MCP and A2A, agents can use software tools, retrieve relevant knowledge, and adapt to feedback. This book guides you step-by-step in creating an AI agent from the ground up, with clear, detailed explanations you can follow to build your custom assistants! In Build an AI Agent (From Scratch), you will learn how to: Implement a ReAct (Thought → Action → Observation) loop. Use MCP to integrate tool calls into your agent’s workflow. Agentic RAG for relevant responses. Create memory modules that store facts, context, and evolving goals. Enable agents to plan, reflect, and self-correct. Build specialized agents, including a code execution agent. Design multi-agent systems. In Build an AI Agent (From Scratch), best-selling author Jungjun Hur and AI expert Younghee Song guide you through creating a complete research assistant agent framework. You’ll learn how agents function under the hood — all without hidden abstractions, black boxes, or framework lock-in. You will implement each piece as you develop a mental model of how agents really work. About the book Build an AI Agent (From Scratch) is a step-by-step guide to creating a working AI agent, starting with the bare essentials and growing your AI into a full-featured, real-world system. You will connect your agent to powerful software tools, implement a reasoning loop, and then extend your agent with retrieval, memory, planning, reflection, and even multi-agent coordination. Along the way, you will work through production-grade code snippets, full prompts and configs, and “make it better” exercises that encourage you to extend and improve your system. By the final chapter, you will have built an end-to-end AI agent that you can use as a foundation for your projects