@Xudong07452910: Recommending a free AI book: "Agentic AI Wandering Guide". I just started reading it, and it feels quite different from many "AI beginner's guides". Although it covers basic knowledge, the author clearly does not focus on concepts that have been repeatedly discussed, but instead goes all the way to reinforcement learning RL, reasoning Reason…
Summary
Recommending a free AI book "Agentic AI Wandering Guide", which delves into concepts like reinforcement learning, reasoning, evaluation, etc. Unlike ordinary beginner's guides, it helps understand how AI works. This book is from an arXiv preprint.
View Cached Full Text
Cached at: 06/30/26, 03:35 AM
Recommend a free AI book: Agentic AI 漫游指南 (The Hitchhiker’s Guide to Agentic AI).
I just started reading it, and I find it quite different from many “AI introductory guides.” Although it covers basics, the author clearly doesn’t spend most of the book on concepts that have been repeated over and over. Instead, it goes all the way through Reinforcement Learning (RL), Reasoning, and Evaluation before finally getting to Agentic AI. So it’s more like a book that helps you understand how AI works, rather than a manual teaching you “how to use AI tools.”
If you’re no longer satisfied with just using ChatGPT, Claude, or Cursor, and want to go deeper into why AI can reason, how it is trained, how it is evaluated, and how Agents actually run, this book is well worth keeping.
https://arxiv.org/pdf/2606.24937
The Hitchhiker’s Guide to Agentic AI: From Foundations to Systems
Source: https://arxiv.org/abs/2606.24937 Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Code, Data, Media
Code, Data and Media Associated with this Article
Demos
Demos
Related Papers
Recommenders and Search Tools
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv’s community?Learn more about arXivLabs (https://info.arxiv.org/labs/index.html).
Similar Articles
@Jolyne_AI: Recommending a free machine learning and AI learning book on GitHub: Machine Learning Q and AI. The book covers 30 core questions about machine learning and AI, from neural networks to model deployment, explaining key knowledge points clearly. GitHub: http://…
Recommending the free machine learning and AI book Machine Learning Q and AI, systematically explaining neural networks, deep learning, computer vision, NLP, and model deployment around 30 core questions, with GitHub and online reading links.
@Xudong07452910: Free Open-Source Book Recommendation: 'How to Build a 7×24 AI Agent from Scratch' This book deeply deconstructs a real AI digital employee platform with 300,000 lines of code, systematically explaining: - Agent Engine & Context Engineering - Digital Human Protocol - AI Browser Implementation - Production-Grade Scheduling System - 7×24 Stable...
Recommends a free open-source technical book 'How to Build a 7×24 AI Agent from Scratch', systematically explaining AI Agent engine, digital human protocol, AI browser, production-grade scheduling and other practical content, based on the real 300,000-line open-source project Halo, and written in a human-machine collaboration manner.
@Xudong07452910: Free and Open-Source High-Quality Tutorial Recommendation: 'Building an Agent from Scratch' - A systematic tutorial on Agent principles and practice from zero to advanced, covering: 1. Basic concepts and mainstream paradigms (ReAct, Plan-and-Solve, Reflection, etc.) 2.…
Recommend the free and open-source tutorial 'Building an Agent from Scratch', which systematically explains AI Agent principles and practice, covering mainstream frameworks such as ReAct, AutoGen, LangGraph, and multiple hands-on projects. It has received 53,000+ stars.
@yibie: Recommended article: Eugene Yan from Anthropic (former Amazon/Alibaba ML team lead) writes a practical guide to his personal AI workflow. Not abstract ideas, but concrete methods you can replicate tomorrow: how to organize directories for easier model retrieval, how to write C…
This is a tweet recommending Eugene Yan's AI workflow guide, detailing how to efficiently collaborate with AI and achieve compound work gains by organizing context, configuring CLAUDE.md, creating skills, and more.
@Russell3402: A friend wanted to learn AI engineering, but I couldn't come up with a good learning path for a while. Here I recommend an open-source AI engineering learning curriculum! It aims to take you from the ground up, covering the complete AI engineering stack: from math, machine learning, deep learning, Transformers, LLMs, Agents, MCP, multi-agent…
Recommends an open-source AI engineering learning course, containing 20 stages and 503 lessons, covering from math fundamentals to production deployment, including Python and other languages, aiming to build a complete AI engineering system from scratch.