Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks
By Adam Fourney, Principal Researcher; Gagan Bansal, Senior Researcher; Hussein Mozannar, Senior Researcher; Victor Dibia, Principal Research Software Engineer; Saleema Amershi, Partner Research Manager
Contributors: Adam Fourney, Gagan Bansal, Hussein Mozannar, Cheng Tan, Eduardo Salinas, Erkang (Eric) Zhu, Friederike Niedtner, Grace Proebsting, Griffin Bassman, Jack Gerrits, Jacob Alber, Peter Chang, Ricky Loynd, Robert West, Victor Dibia, Ahmed Awadallah, Ece Kamar, Rafah Hosn, Saleema Amershi
The future of AI is agentic. AI systems are evolving from having conversations to getting things done—this is where we expect much of AI’s value to shine. It’s the difference between generative AI recommending dinner options to agentic assistants that can autonomously place your order and arrange delivery. It’s the shift from summarizing research papers to actively searching for and organizing relevant studies in a comprehensive literature review.
Modern AI agents, capable of perceiving, reasoning, and acting on our behalf, are demonstrating remarkable performance in areas such as software engineering, data analysis, scientific research, and web navigation. Still, to fully realize the long-held vision of agentic systems that can enhance our productivity and transform our lives, we need advances in generalist agentic systems. These systems must reliably complete complex, multi-step tasks across a wide range of scenarios people encounter in their daily lives.
