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 Duration 7 hours

Course Outline

1. Introduction to LLM Applications and AutoGen v0.4

  • Overview of Large Language Models (LLMs): Gaining insight into their potential and use cases. 
  • Introduction to AutoGen v0.4: Delving into its features, architecture, and the ways it streamlines agentic AI system development.

2. Core Concepts and Components of AutoGen

  • Understanding the Layered Framework:
    • Core Layer: An event-driven architecture that supports dynamic workflows.
    • AgentChat API: Constructing task-oriented agents via high-level APIs.
    • Extensions: Incorporating custom agents, tools, and memory modules to expand functionality.
  • Asynchronous Messaging: Applying event-driven and request-response interaction patterns. 

3. Building Your First Multi-Agent Application

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

4. Case Studies and Best Practices

  • Real-World Applications: Reviewing successful AutoGen implementations across various sectors.
  • Best Practices: Recommended guidelines for designing efficient and scalable LLM applications with AutoGen.
  • Challenges and Solutions: Tackling typical development hurdles and their resolutions.
  • Q&A

This workshop is ideal for:

  • Software developers
  • Data scientists
  • Data engineers
  • Individuals with a programming background or strong interest who wish to explore AI programming.

Requirements

Prerequisite – Python Programming

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