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what is ai-agent-architecture fr?

drobiazkin/ai-agent-architecture — explained in plain English

Analysis updated 2026-05-18

24HTMLAudience · developerComplexity · 3/5LicenseSetup · easy

tl;dr

A free, open engineering book and course teaching how LLM systems and AI agents actually work, from tokenization to self-hosting models.

vibe map

mindmap
  root((AI Agent Architecture))
    What it does
      Engineering book
      Companion course
      Pattern language
    Tech stack
      Markdown
      Mermaid diagrams
      Python
    Use cases
      Learn LLM internals
      Build agents
      Self-host models
    Audience
      Distributed systems engineers
      AI developers
    License
      CC BY-SA text
      MIT code

Code map

Detail Auto

An interactive map of this repo's files and how they connect — its source is parsed live in your browser. Click Visualize to build it.

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what do people make with this?

VIBE 1

Read a structured, mechanism-focused explanation of how LLMs and AI agents actually work.

VIBE 2

Follow the companion course's nine modules to build a working RAG and agent system by hand.

VIBE 3

Learn how to fine-tune and self-host an open language model on your own hardware.

VIBE 4

Build a single-file HTML or PDF version of the book locally using the included build scripts.

what's the stack?

MarkdownHTMLMermaidPython

how it stacks up fr

drobiazkin/ai-agent-architectureddekadt/my580_agentic_aiiranee/fnos-hermes-agent
Stars242424
LanguageHTMLHTMLHTML
Setup difficultyeasyeasymoderate
Complexity3/51/53/5
Audiencedeveloperresearcherops devops

Figures from each repo's GitHub metadata at analysis time.

how do i run it?

Difficulty · easy time til it works · 30min

Reading the Markdown chapters needs no setup, building HTML or PDF versions requires the included build tools.

The book's text can be shared and adapted with credit as long as any reworked version stays under the same license, while the code is fully free to reuse under MIT.

in plain english

This repository is a free, openly published engineering book and companion course about how large language model systems and AI agents actually work under the hood, written by a single author, Aleksandr Drobiazkin, in both English and Russian editions. It is not a collection of prompt writing tips. Instead it aims to teach engineers the underlying mechanisms, costs, and tradeoffs involved in building and running these systems themselves, so they are not entirely dependent on renting access to a handful of hosted AI providers. The book is organized into eight parts and twenty six chapters, covering topics like how text gets broken into tokens, how attention and different model architectures work, prompt and context design, retrieval augmented generation and its many variations, how AI agents are built as a kind of distributed system, integrating with tools through the Model Context Protocol, running these systems in production including security and cost concerns, and finally how to fine-tune, deploy, and even pre-train models on one's own hardware. Every major idea in the book follows a consistent template describing the context, the problem, the tradeoffs, the solution, a worked example, and related patterns, a structure borrowed from established software design pattern writing. The material is aimed at engineers who already understand distributed systems concepts and want to treat language models and agents as another architectural component to reason about rather than as something mysterious. Readers can browse the book chapter by chapter as plain text files in the repository, or build a single combined web page or PDF version themselves using the included build tools. A separate, practical companion course walks through building a real system across nine hands-on modules, from running a model on your own hardware through retrieval, agents, evaluation, and fine-tuning. The book's written text is released under the CC BY-SA 4.0 license, meaning it can be shared and adapted as long as credit is given and any reworked version is shared under the same terms, while the accompanying code in the repository is released separately under the MIT license. The author funds ongoing revisions through voluntary cryptocurrency donations rather than a paywall or subscription.

prompts (copy fr)

prompt 1
Summarize what Part IV of this book teaches about designing AI agents as distributed systems.
prompt 2
Help me follow the companion course module on running a local model and building basic RAG.
prompt 3
Explain the pattern template this book uses so I can read a chapter and extract its key mechanism.
prompt 4
Walk me through building the single-file HTML version of the book using the tools in build/.

Frequently asked questions

what is ai-agent-architecture fr?

A free, open engineering book and course teaching how LLM systems and AI agents actually work, from tokenization to self-hosting models.

What language is ai-agent-architecture written in?

Mainly HTML. The stack also includes Markdown, HTML, Mermaid.

What license does ai-agent-architecture use?

The book's text can be shared and adapted with credit as long as any reworked version stays under the same license, while the code is fully free to reuse under MIT.

How hard is ai-agent-architecture to set up?

Setup difficulty is rated easy, with roughly 30min to a first successful run.

Who is ai-agent-architecture for?

Mainly developer.

peek the repo → explain another one

This repo across BitVibe Labs

double-check against the repo, no cap.