Course Outline

1. Introduction to LLM Applications and AutoGen v0.4

  • Overview of Large Language Models (LLMs): Understanding their capabilities and applications.​ 
  • Introduction to AutoGen v0.4: Exploring its features, architecture, and how it simplifies the development of agentic AI systems.  

2. Core Concepts and Components of AutoGen

  • Understanding the Layered Framework:
    • Core Layer: Event-driven architecture supporting dynamic workflows.
    • AgentChat API: Building task-driven agents with high-level APIs.
    • Extensions: Integrating custom agents, tools, and memory modules for enhanced functionality.
  • Asynchronous Messaging: Implementing event-driven and request-response interaction styles.​ 

3. Building Your First Multi-Agent Application

  • Defining Agents: Creating Assistant and User Proxy agents.​ 
  • Establishing Agent Communication: Setting up asynchronous messaging between agents. 
  • Implementing a Sample Application: Developing a simple multi-agent system to solve a specific task.​ 
  • Observability and Debugging Tools: Utilizing built-in metric tracking and message tracing for real-time monitoring.​ 

4. Case Studies and Best Practices

  • Real-World Applications: Examining successful implementations of AutoGen in various industries.​
  • Best Practices: Guidelines for designing efficient and scalable LLM applications using AutoGen.​
  • Challenges and Solutions: Addressing common challenges faced during development and their solutions.​
  • Q&A

The workshop is intended for:

  • software developers
  • data scientists
  • data engineers
  • people with programming background/inclination who want to learn about AI programming.

Requirements

. Prerequisites - Python programming

 7 Hours

Number of participants


Price per participant

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