GitHub - bryanyzhu/agentic-ai-system-course: Use agent to learn agent - A skeleton course on how to design, build, and operate production AI agents · GitHub
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Agentic System Course - Use Agent to Learn Agent
Join the discord channel if you want to learn and build together!
This is a 22-chapter skeleton course on how to design, build, and operate production AI agents — written to be read with your own AI partner at your side. An agentic system is an AI system that can autonomously pursue goals by planning, making decisions, using tools, adapting based on feedback, having memory, etc — instead of only responding to a single prompt. Similar to Andrej Karpathy's idea file on LLM-wiki, this course is giving you the skeleton and your agent will help you put the muscles on it .
This course is :
A skeleton — load-bearing topics, patterns, and decisions, with trade-offs.
Written to age slowly. Framework specifics rot fast; architectural patterns do not.
A file pair (course + AGENTS.md) designed for AI consumption as much as human reading.
This course is not :
A step-by-step tutorial. There is no walked-through project.
Tied to one stack. The course never says "use LangChain" or "use Pydantic AI." Your AI partner suggests the stack that fits your project.
A reference manual. When you need an exact API signature, ask your AI or read the docs.
How to start
Clone the repo, open it in your usual IDE to view course content. At the same time, point your AI agent (Claude code/Codex) at the project root, and try one of these prompts when you study a chapter:
"Give me three real-world examples of where this matters."
"Suppose you are interviewing me, quiz me on this topic with five follow-up questions, easy to hard."
"What's a question I should be asking that I haven't?"
"I just read about [pattern X]. I am building [your project]. Translate the pattern into the smallest version that works in whatever language and tools fit, and explain each piece as you write it."
"Forget my project for a moment — show me how OpenCode (or Hermes Agent, or any leading coding agent) handles this, and what we should borrow from it."
You can also just point your agent at Ch.22's design canvas and walk through it with your specific project in mind — that's the fastest path from "I have an idea" to "I have a spec."
Built-in skills
agentic-system-reviewer
Reviews PRDs, design docs, implementation plans, or agent code against the course. You can run this skill on any agentic system to get course-grounded feedback — your own project, an open-source agent you're studying, a PRD before any code exists, or a coworker's repo you want a second opinion on. The skill calibrates scope first (hobby / team tool / customer-facing), picks the chapters that matter for your archetype, reads them, and produces a findings-first report with severity, evidence, course citations, and concrete fixes — not a generic "looks good" or "add safety" review.
In Claude Code, just describe what you want — "review this against the course", "is this agent design good?", "what chapters does this miss?" — while the agent is pointed at the target repo or doc. The skill auto-loads when its description matches your intent.
Codex users: use Codex's official skill-creator skill to port this skill over.
Course structure
Chapters<br>Theme
Ch.00<br>How to use this course with your AI partner
Ch.01–04<br>Foundations: one tool call → the loop → tools as contract → prompts & cache
Ch.05–08<br>Memory and state: short-term → long-term → writing & curation →...