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Build an AI Agent (From Scratch)
List Price:
$59.99
| Expected release date is Aug 25th 2026 |
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Product Details
Author:
Jungjun Hur, Younghee Song
Format:
Paperback
Pages:
375
Publisher:
Manning (August 25, 2026)
Imprint:
Manning
Release Date:
August 25, 2026
Language:
English
ISBN-13:
9781633434615
ISBN-10:
1633434613
Weight:
15.84oz
Dimensions:
7.375" x 9.25"
File:
Eloquence-SimonSchuster_04022026_P9912986_onix30_Complete-20260402.xml
Folder:
Eloquence
List Price:
$59.99
Pub Discount:
37
Series:
From Scratch
As low as:
$56.99
Publisher Identifier:
P-SS
Discount Code:
H
Overview
Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.
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 own custom assistants!
In this book, bestselling 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.
In Build an AI Agent (From Scratch) you will learn how to:
• Implement a ReAct (Thought → Action → Observation) loop
• Use MCP to integrate tools 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
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 own projects.
About the reader
For Python developers and AI practitioners. All examples run on a standard laptop.
About the author
Jungjun Hur is an AI and data engineer with experience in e-commerce and AI industries, where he has built production-ready AI applications and LLM-powered features. He is the author of the bestselling book Practical AI Application Development Using LLMs.
Younghee Song is an AI consultant at PwC. She graduated from Hanyang University with a degree in Economics and Finance and holds a Master’s in Human Factors Engineering from Seoul National University.
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 own custom assistants!
In this book, bestselling 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.
In Build an AI Agent (From Scratch) you will learn how to:
• Implement a ReAct (Thought → Action → Observation) loop
• Use MCP to integrate tools 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
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 own projects.
About the reader
For Python developers and AI practitioners. All examples run on a standard laptop.
About the author
Jungjun Hur is an AI and data engineer with experience in e-commerce and AI industries, where he has built production-ready AI applications and LLM-powered features. He is the author of the bestselling book Practical AI Application Development Using LLMs.
Younghee Song is an AI consultant at PwC. She graduated from Hanyang University with a degree in Economics and Finance and holds a Master’s in Human Factors Engineering from Seoul National University.
