What you'll learn:
- Build autonomous AI agents using Python & LangChain
- Multi-agent orchestration — three agents collaborating and making decisions autonomously
- RAG with ChromaDB — give agents access to real documents so they never hallucinate
- Live streaming agent activity — watch every tool call and decision stream in real time
- Turn Django ORM queries into AI tools — your existing skills become superpowers
- Learn LangChain by building real AI agents — understand exactly what the framework does under the hood
Are you a Python/Django developer who wants to stay ahead in the AI era?
This course teaches you how to build real AI Employees — autonomous AI agents that work for your business 24/7, handle customer queries, collaborate with each other, assess fraud risk, and make decisions — all without human intervention.
What You Will Build
A complete AI-powered customer support system for a fictional AC company called CoolBreeze AC. By the end of this course, you will have built:
Maya — an AI support agent that handles customer queries, checks orders, verifies delivery status, and decides when to escalate
A manager agent that reviews escalated cases and makes refund decisions
A risk agent that assesses fraud patterns and gives the manager a verdict
A RAG system that gives agents access to real company documents and policies — no hallucination, real answers
A live streaming support dashboard where staff can monitor every agent decision in real time
Full deployment on Railway — your system live on a real URL
What You Will Learn
How Agentic AI actually works — the agent loop, tool calling, and decision making under the hood
How to turn Django ORM queries into AI agent tools
How to write effective tool schemas that guide an LLM's decision making
How to build multi-agent systems where agents collaborate and hand off tasks autonomously
How to implement RAG using ChromaDB and pypdf so agents answer from real company documents
How embeddings and vector similarity search work
How to stream live agent activity to a dashboard using Server Sent Events (SSE)
Prompt engineering techniques for controlling agent behavior and tone
How to deploy a production Django + AI application on Railway
Why We Start Without Frameworks
Most AI courses jump straight into LangChain or AutoGen. We don't — and it's intentional.
LangChain, AutoGen, and CrewAI are all built on the same agent loop, tool calling, and multi-agent orchestration you'll learn in this course. If you learn the framework first, you're memorizing syntax without understanding what's happening underneath.
We build everything from scratch first. Then, in the LangChain edition, you'll see the exact same CoolBreeze project rebuilt using the LangChain framework. You'll understand immediately what they're doing for you, when to use them, and why.
Build the foundation first. The frameworks will make complete sense after.
Who This Course Is For
Django developers who want to add Agentic AI to their skill set
Backend developers who want to understand how AI agents actually work
Developers frustrated with tutorials that never explain what's happening internally
Anyone who wants to build a real, resume-worthy AI project from scratch
Requirements
Basic to intermediate Python knowledge
Basic Django experience — models, views, URLs, templates
No AI or machine learning experience needed
Anthropic API key (new accounts may receive $5 free credits — enough to complete the course)
