Course Description

Master the next frontier of AI with “Agentic RAG using AutoGen,” a practical, hands-on course designed to teach you how to build intelligent, multi-agent retrieval-augmented generation (RAG) systems. Through real-world projects and advanced frameworks like AutoGen and ChromaDB, you’ll learn to orchestrate AI agents that retrieve, reason, and collaborate effectively. From setting up vector databases to running asynchronous group chats between agents, this course gives you the skills to build dynamic AI systems,

Course curriculum

  • 1
    Introduction to Autogen and AI Agents
    • Instructor Introduction
    • Course Handouts
    • What are AI Agents?
    • What is Autogen?
    • Why are we using OpenAI models?
    • A Simple Example
    • Quiz
  • 2
    Setting up Chroma DB
    • What is ChromaDB?
    • Setting up ChromaDB
    • Loading Records into ChromaDB
    • Creating a Search Function
    • Quiz
  • 3
    Setting Up Autogen Agents
    • Creating Search Assistant Agents
    • Creating Search Execution Agents
    • Creating GroupChat and GroupChat Manager
    • Kicking Off a GroupChat
    • Quiz
  • 4
    Adding Search to Agents
    • Adding Search Function to the Assistant Agents
    • Run Asynchronous Query
    • Adding a Writer Agent
    • Quiz
  • 5
    Testing & Evaluating Agents
    • Running Evaluations
    • User Feedback and Prompt Tuning
    • Enterprise RAG Scaling Retrieval Augmented Generation
    • Quiz

Who Should Enroll

  • AI/ML Engineers who want to build intelligent, multi-agent systems beyond standard LLM use cases.

  • Developers looking to create practical RAG pipelines using AutoGen, ChromaDB, and OpenAI models.

  • Data Scientists aiming to integrate smarter retrieval and reasoning into AI workflows.

  • Tech Enthusiasts excited to explore the cutting edge of autonomous AI and agent orchestration.

Key Takeaways

  • Learn how agentic AI differs from traditional LLMs and why it enables more flexible, intelligent systems.

  • Gain hands-on experience building assistant and user proxy agents using the AutoGen framework.

  • Set up and use ChromaDB to power vector-based retrieval in your RAG pipeline.

  • Create multi-agent group chats that communicate asynchronously to complete tasks collaboratively.

  • Run evaluations and refine prompts to continuously improve your agent system's performance.

About the Instructor

Tyler Suard - Senior AI Researcher & Developer @ Parker Hannifin. Author of "Enterprise RAG: Scaling Retrieval Augmented Generation". Ex-Apple | Ex-Meta |Stanford affiliate

​Tyler Suard is a seasoned AI researcher and developer with a rich background at leading tech companies, including Apple and Meta. He is the author of "Enterprise RAG: Scaling Retrieval Augmented Generation," published by Manning on March 27. He has built scalable RAG systems for enterprises with over 50,000 employees and is a contributor to leading AI libraries like PyTorch, TensorFlow, AutoGen, and Hugging Face. Tyler’s code is actively used by Fortune 500 companies including PepsiCo, Boeing, eBay, and LinkedIn.
About the Instructor

FAQ

  • Do I need prior experience with AutoGen or agent frameworks?

    No prior experience is required. The course introduces AutoGen from the ground up and walks you through building agents step by step.

  • What programming skills do I need for this course?

    Basic proficiency in Python is recommended, as the course includes hands-on coding exercises involving agent setup, vector databases, and asynchronous functions.

  • Will I learn how to use vector databases like ChromaDB?

    Yes, you’ll get practical experience setting up, populating, and querying ChromaDB to power retrieval in your RAG system.

  • Is this course suitable for enterprise use cases?

    Absolutely. The course is designed with scalable, real-world applications in mind, including examples relevant to enterprise RAG deployments.