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Course Outline

1. Introduction to LLM Applications and AutoGen v0.4

  • Overview of Large Language Models (LLMs): Gaining insight into their capabilities and potential applications. 
  • Introduction to AutoGen v0.4: Discovering its key features, architecture, and how it streamlines the creation of agentic AI systems.

2. Core Concepts and Components of AutoGen

  • Understanding the Layered Framework:
    • Core Layer: An event-driven architecture designed to support dynamic workflows.
    • AgentChat API: Enabling the development of task-oriented agents via high-level APIs.
    • Extensions: Connecting custom agents, tools, and memory modules to boost functionality.
  • Asynchronous Messaging: Implementing event-driven and request-response interaction patterns. 

3. Building Your First Multi-Agent Application

  • Defining Agents: Setting up Assistant and User Proxy agents. 
  • Establishing Agent Communication: Configuring asynchronous messaging protocols between agents. 
  • Implementing a Sample Application: Creating a basic multi-agent system to address a specific task. 
  • Observability and Debugging Tools: Leveraging built-in metrics tracking and message tracing for real-time system monitoring. 

4. Case Studies and Best Practices

  • Real-World Applications: Analyzing successful AutoGen implementations across various industries.
  • Best Practices: Guidelines for designing efficient and scalable LLM applications using AutoGen.
  • Challenges and Solutions: Tackling common development hurdles and exploring effective solutions.
  • Q&A

This workshop is designed for:

  • software developers
  • data scientists
  • data engineers
  • individuals with a programming background or inclination who wish to learn AI programming.

Requirements

Prerequisites: Knowledge of Python programming

 7 Hours

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