Published 4/2026
Created by Haebichan Jung | AI/ML Architect @ Snowflake | Previously @ Servicenow
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 23 Lectures ( 3h 58m ) | Size: 2.83 GB
Build and learn production-ready AI agents from zero using LangChain, LangGraph, MCP, A2A
What you’ll learn
✓ Go from zero to building AI agents in Python – no prior AI experience needed, just curiosity
✓ Understand how AI agents work at real companies – the same tools and patterns used in production today
✓ Walk into any conversation about AI agents, tools, and actually know what people are talking about
✓ Build a portfolio project: a working AI agent that connects to database, discovers tools, and reasons through problems
Requirements
● Some Python experience needed. No AI experience needed.
Description
[This course contains the use of artificial intelligence.]
This course gives you everything you need to truly understand AI Agents / Agentic AI, starting from zero.
No prior AI experience required. No machine learning background needed. Just curiosity and basic Python. We start by answering the question everyone has but rarely gets a straight answer to: what is an AI agent, really? From there, you build one piece at a time — adding tools, reasoning loops, memory, and multi-agent coordination across 15 structured sections.
Every section follows the same pattern: a visual lecture that explains the concept in plain English, then a hands-on Python notebook where you build it yourself using real data. You’ll work with the same dataset throughout the entire course, so you always understand what the agent is doing and why.
Here’s what you’ll cover
• ReAct agents that reason through problems and call tools automatically
• Multi-agent systems where specialized agents collaborate and delegate tasks
• MCP (Model Context Protocol) — the new universal standard for connecting agents to tools
• A2A (Agent-to-Agent) — how agents discover and talk to each other across services
• Memory and RAG so agents can search documents and remember past conversations
• Guardrails, evaluation, and observability for safe, debuggable agents
• FastAPI deployment so your agent becomes a real API
Whether you’re a student, a developer, or just someone who wants to deeply understand how AI agents actually work — this
course will get you there.
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