What’s new in AutoGen?

CHI WANG: Hi, everyone. My name is Chi. I’m from Microsoft Research AI Frontiers. I’m excited to share with you the latest news about AutoGen. AutoGen was motivated by two big questions: what are the future AI applications like, and how do we empower every developer to build them? Last year, I worked with my colleagues and collaborators from Penn State University and University of Washington on a new multi-agent framework.

We have been building AutoGen as a programing framework for agent AI, like PyTorch for deep learning. We developed AutoGen inside an open-source project, FLAML, and in last October, we moved it to a standalone repo on GitHub. Since then, we’ve got new feedback from users every day, everywhere. Users have shown really high recognition of the power of AutoGen, and they have deep understanding of the values in different dimensions like flexibility, modularity, simplicity. 

Let’s check one example use case.

[Beginning of pre-recorded testimonial.] 

Sam Khalil, VP, Data Insights & FounData, Novo Nordisk: In our data science department, AutoGen is helping us develop a production ready multi-agent framework.  

Rasmus Sten Andersen, AI engineer lead, Novo Nordisk: Our first target is to reduce the barriers to technical data analytics and to enable our broader community to derive insights.  

Georgios Ilias Kavousanos, data engineer, AI Labs, Novo Nordisk: We are also extending AutoGen with the strict requirements from our industry given the sensitive nature of our data.  

[End of pre-recorded testimonial.]

WANG: That is one example use case from the pharmacy vertical. We have seen big enterprise customers’ interest like this from pretty much every industry vertical. AutoGen is used or contributed [to] by companies, organizations, universities from A to Z, all over the world. We have seen hundreds of example applications, and some organizations use AutoGen as a backbone to build their own agent platform, and others use AutoGen for diverse scenarios, including research and investment to novel and creative applications of multiple agents. AutoGen has a large community—very active—of developers, researchers, AI practitioners. They are so active and passionate. I’m so amazed by that, and I appreciate all the recognition that was received by AutoGen in such a short amount of time. For example, we have been selected, our paper is selected by TheSequence as one of the top favorite AI papers in 2023. To quickly share our latest news, last Friday, our initial multi-agent experiment on the challenging GAIA benchmark turned out to achieve the No. 1 accuracy in the leaderboard in all the three levels. That shows the power of AutoGen in solving complex tasks and the bigger potential. 

This is one example of our effort in answering a few open hard questions, such as how to design an optimal multi-agent workflow. AutoGen is under active research and development and is evolving at a very fast pace. Here are examples of our exciting new features or ongoing research. First, for evaluation, we are making agent-based evaluation tools or benchmarking tools. Second, we are making rapid progress in further improving the interface to make it even easier to build agent applications. Third, the learning capability allows agents to remember teachings from users or other agents long term and improve over time. And fourth, AutoGen is integrated with new technologies like OpenAI assistant and multimodality. Please check our blog post from the website to understand more details.  

I appreciate the huge amount of support from everyone in the community, and we need more help in solving all the challenging problems. You’re all welcome to join the community and define the future of AI agents together.

Thank you very much.