⚠️ NOT JUST ANOTHER THEORY WORKSHOP

Build Real
Agentic AI
Systems.

Python → Azure AI Foundry → Qdrant → Agentic RAG.
This is the exact skill stack companies expect from an Azure AI Engineer in 2026.

Secure Your Spot

Starts Jan 24, 2026

Why This Workshop?

Most courses teach syntax. We build production systems.

Production-Grade

Build a full Agentic RAG system that reads HR policies, uses vector search, and runs on Azure AI Foundry.

Azure AI-102 Aligned

Directly maps to Azure AI Engineer Associate certification skills. Plan, manage, and deploy real AI solutions.

Multi-Agent Systems

Learn the "Agent-as-a-Tool" pattern. Understand when one agent should call another and how to orchestrate them.

The Stack

Python

The language of AI

Azure AI Foundry

Enterprise Scale Deployment

Qdrant

Vector Search Engine

Curriculum Workflow

Module 0-2: Foundations

Python for AI, Generative AI mental models, Embeddings & Vectors.

Module 3-4: Azure & Qdrant

Azure AI Foundry deployment, Prompt Testing, Qdrant Vector DB deep dive.

Module 5: RAG Pipeline

PDF ingestion, Chunking strategies, Embedding pipelines, Retrieval logic.

Module 6-7: Agentic RAG

Reasoning loops, Tool definitions, Multi-agent orchestration, Final Project.

What You Will Build

Agentic HR Policy Chatbot that reads PDFs & decides when to search.

Production RAG pipeline with Qdrant & Azure Models.

Multi-Agent System where agents call other agents as tools.

class Agent:
def __init__(self, tools):

                                self.tools = tools

def think(self,
                            query):
# Reasoning loop

                                plan = self.llm.generate_plan(query)
return self.execute(plan)
                        

Build Real Agentic AI Systems.