Systematically Master AI Agent Development
A Complete Path from Concepts to Production

Based on top GitHub open-source projects, 6 stages, 20+ open-source project deep dives, 7 hands-on projects. Not a forget-after-watching tutorial, but a learning path with code output at every stage.

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Stages

20+

Open-source Projects

Hands-on Projects

Curated Resources

🤖 Created by AI Experts based on GitHub 20K+ Star open-source courses, upgraded from static pages to a dynamic learn-and-practice system

After CompletingWhat You Can Do

🔄

Hand-write an Agent Loop and Understand Agent Fundamentals

Start from a 50-line minimal loop, understand observe → think → act, Tool Call, structured output, and build a working Agent from scratch.

🏗️

Dissect Harness + RAG + Multi-Agent to Build Production Systems

Read and understand Claude Code / OpenClaw / DeerFlow architecture, build RAG systems, design multi-Agent collaboration pipelines, and add Eval + Trace + security protection.

🚀

Ship Your Own Agent Project with a Showcase Portfolio

Follow a 7-level project ladder, from Browser Agent to Full-stack Agent, and build complete projects others can clone and run, with README + tests + deployment.

Who Is ItFor

1-3 Year Frontend/Backend DevelopersWant to pivot to AI Agent direction and add project experience to your resume

Tech Managers / CTOsNeed to understand Agent technology boundaries and evaluate solution options

Students / Graduate StudentsNeed an Agent direction for thesis projects or want to build interview projects

Indie Developers / EntrepreneursWant to build products with AI Agents, from demo to launch

LearningOutline

Fundamentals — Hand-write a Minimal Agent Loop

Agent concepts, Tool Call principles, structured output, and error handling. Project: Calculator Agent (50-150 lines).

1 lesson1 card deck3 codex5 articles

RAG & Memory — Build a Citation-enabled Research Assistant

The full chunk → embed → retrieve → cite pipeline, short-term/conversation/long-term memory, and handling hallucinated citations. Project: Research Assistant.

1 lesson1 card deck8 codex4 articles

Agent Harness — Dissect Modern Agent Architecture

loop / tool registry / permission / session / context compaction + Browser Agent principles. Project: Run Harness and add your own tools.

2 lessons1 card deck9 codex3 articles

Collaboration & Skills — Multi-Agent Collaboration + Reusable Capability Packs

planner → writer → reviewer pipeline, circular argument drift handling, and Skill / MCP / A2A protocols. Project: Multi-Agent Writer + write a Skill.

1 lesson1 card deck3 codex7 articles

Production — Eval + Trace + Security + Deployment

Evaluation metrics, observability, Prompt Injection defense, and deployment comparison. Project: Build an Eval table (20+ tasks) + ship a project.

1 lesson1 card deck4 codex4 articles

Advanced Practice — 7-Level Project Ladder

Browser Agent → Coding Review → Nano Coding Agent → Gateway → Personal Agent → Full-stack Agent → Portfolio. Classic paper deep dives + Legacy Frameworks.

1 lesson4 card decks3 codex12 articles

How toLearn

Zero Experience / Systematic LearningFollow Stage 1 sequentially, complete a hands-on project after each Stage, 4-8 weeks total

Already Know LLM ApplicationsSkip Stage 1, start from Stage 2 or Stage 3, and focus on Harness and Eval

Want to Build ProjectsJump to the Stage 6 project ladder, build a runnable project at each level, and learn as you go

Just Looking Things UpEach Stage has curated resources (official docs + papers + blogs), look up as needed

LearningPrerequisites

✓Python or TypeScript basics (can write functions and call APIs)

✓Can use Git and command line

✓Have called at least one LLM API (OpenAI / Claude / domestic models all work)

✓Understand REST API and JSON

△Software engineering concepts (testing, logging, deployment) are a plus

△Understand Docker basics (Stage 6 will use it)

✓ Required  |  △ Bonus, can learn without

LearningPrinciples

Hands-on First — running a minimal example beats reading 10 articles
Better to build a small reliable agent than a flashy demo
Clear Interface — don't let agents guess parameter formats
Evaluation-driven — an agent without evaluation is just a toy
Observability — be able to trace back when things go wrong
Treat multi-agent as a coordination problem, not magic
Human Approval — sending emails, deleting files, and payments need human confirmation
Respect platform rules, copyright, and data access boundaries

Limited-timeOffer

🔥 First 10 free trial

Full Course + Sager AI

✓ All 6 stages unlocked
✓ 20+ open-source project deep dives
✓ Sager AI 24/7 Q&A
✓ 7 hands-on project code sandboxes

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