Qingyun Wu

Qingyun Wu

Co-creator and Co-founder

AG2

Dr. Qingyun Wu is the co-creator and co-founder of AG2 (formerly AutoGen), a leading open-source AI agent framework with a monthly downloads of over 700k and a vibrant community of over 20k AI agent developers. Qingyun is also an Assistant Professor at the College of Information Science and Technology at Penn State University. Qingyun got her Ph.D. in Computer Science from the University of Virginia in 2020. Qingyun received the 2019 SIGIR Best Paper Award, and ICLR 2024 LLM agent workshop Best Paper Award.

In this hands-on technical workshop, you'll master the fundamentals of building production-grade AI agent applications with AG2 (formerly AutoGen), a lending  open-source AI Agent framework that is adopted by millions of users and downloaded over 700k times per month. 

You'll explore essential AI agent design patterns and discover how to customize agents for specific domains using reference implementations from the AG2 team. You'll also learn production deployment strategies using FastAgency and build complete agent solutions for real business scenarios.

Through guided exercises, you'll develop AI agent systems that can tackle real-world applications like customer support, marketing research, and data analysis. By the end of the day, you'll have the knowledge to build specialized, scalable agent applications that deliver reliable results in production environments.

What You’ll Learn

  • Fundamentals of AI Agents: Understand the core concepts and architecture of agent-based AI systems
  • AG2/AutoGen Framework: Master the key components and capabilities of AG2 framework
  • AI Agentic Design Patterns:  Key AI Agent design patterns
  • Customized Agent Creation: Build specialized agents for specific tasks and domains. Learn from reference agents built by the AG2 team.
  • Integration Strategies: Connect your agent systems with external tools and APIs, and MCPs
  • Development: Build specialized agents for specific tasks and domains
  • Practical Applications: Apply agent technology to real-world use cases
  • Best Practices: Optimize agent performance and reliability in production environments

 Prerequisites

  • Basic Python programming knowledge
  • Familiarity with LLMs 
  • GitHub account for accessing workshop materials
  • Local development environment with Python 3.9+

Technical Requirements

  • Python 3.9 or higher
  • Git
  • API keys for language models (OpenAI, Anthropic, etc.)
  • Code editor of choice
  • Virtual environment management (venv, conda, etc.)
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