In this episode, I'm joined by Chi Wang, a principal researcher at Microsoft and the creator of AutoGen, an open-source framework that allows developers to combine LLMs, tools, and human input to build multi-agent AI systems.
By enabling AI agents to collaborate, learn from each other, and contribute their unique skills, AutoGen is unlocking a new frontier of AI capabilities. It's quickly gained traction among both academics and enterprises and is currently powering a wide range of use cases, including synthetic data generation, code generation, and pharmaceutical data science.
In our conversation, Chi breaks down the core concepts behind multi-agent AI, the pros and cons of multi-agent architectures, and the real-world use cases enabled by AutoGen. He also shares some of the open research challenges he's tackling, his perspective on the future of AI, and what excites him most about where the field is headed.
Read takeaways from our conversation here: https://foundationcapital.com/the-promise-of-multi-agent-ai/