What this is: one coherent path from reading how agents work to building one yourself—without hiding behind black-box APIs. The companion site How AI agents work publishes longer fragments and chapter-style notes; this page is the contract: what you will understand, how the build track stages fit together, and where to read next.
On this site vs. the companion site
| Here (this book site) | On howaiagentswork.com |
|---|---|
| The map: promises, roadmap, and the build track in order | Deeper dives: models, loops, tools, memory, planning—written to stand alone for search and rereading |
| Steps 0–10 as a single narrative you will implement | Fragments you can enter from Google without reading everything else first |
| Stays short enough to skim in one sitting | Lets us go long where nuance matters |
Use this page to decide whether to commit; use the companion site when you want explanation density on a single topic.
Three reasons to read
1) Understand how agents work
Agents are not “a chatbot with extra vibes.” They are loops that couple a model to state, tools, and (when needed) memory and planning. This book builds that mental model in plain technical language—no prior ML coursework assumed—so you can reason about failure modes, costs, and design trade-offs.
2) Build your own agent locally
You will follow a tutorial that produces a working agent on your machine or VM using open-source pieces. No paid “agent API” is required for the core path.
What you will learn to wire together:
- a model (local, private, or hosted—same code shape)
- an agent loop (iteration and feedback)
- tools (actions that touch the real world)
- memory + retrieval (scaling beyond a single context window)
Hosted APIs (ChatGPT, Claude, …) are optional accelerators; the default stance is reproducible, inspectable Python.
3) Conclusions from recent model leak fallout
We include an explicit “so what”: what leaks and real-world releases suggest about reliability, evaluation, and system design—without turning the book into gossip. The point is engineering judgment, not headlines.
Chapter roadmap (draft status)
- Chapter 0 — Introduction
- Chapter 1 — Foundations
- Chapter 2 — How Models Work
- Chapter 3 — System Architecture
- Chapter 4 — Environment & Setup
- Chapter 5 — The Agent Loop
- Chapter 6 — Tool Systems
- Chapter 7 — Memory & Retrieval
- Chapter 8 — Planning & Decomposition
- Chapter 9 — Multi-Agent Systems
- Chapter 10 — Advanced Systems & Production
Companion reading for several of these themes (same project, different surface):
- Models & tokens — intuition for what the model can and cannot see in one pass
- The loop — why “one-shot” vs “multi-step” changes everything
- Tools — parsing, safety, and the boundary between language and execution
- Memory & retrieval — selection, not storage
- Planning — plans as hypotheses that must survive contact with reality
Explore those threads on the companion site when you want detail beyond this overview.
Build track (build-your-own-agent)
The hands-on exercise track (steps 0–10) lives on its own page so this home stays a light contract and roadmap.
Open the exercise track → — jump links and the full write-up.
Where to read next
Start with the companion fragments when you want chapter-length depth:
- English — How AI agents work — fragments and long-form notes
- Español — same project, Spanish entry point
Email capture and donations can layer on later; for now, progress is visible through the companion site and this roadmap.
Optional editorial pass: tighten each step into a single blueprint block (Goal / Result / Tools / Knowledge / Ready-to-move-on) for Chapter 0 or the companion repo README.