Uploaded December 2024 | Updated September 2026, 3 days ago
Abstract: Modern AI agents, driven by advances in large foundation models, promise to enhance our productivity and transform our lives by augmenting our knowledge and capabilities. To achieve this vision, AI agents must effectively plan, perform multi-step reasoning and actions, respond to novel observations, and recover from errors, to successfully complete complex tasks across a wide range of scenarios. In this work, we introduce Magentic-One, a high-performing open-source agentic system for solving such tasks. Magentic-One uses a multi-agent architecture where a lead agent, the Orchestrator, plans, tracks progress, and re-plans to recover from errors. Throughout task execution, the Orchestrator also directs other specialized agents to perform tasks as needed, such as operating a web browser, navigating local files, or writing and executing Python code. Our experiments show that Magentic-One achieves statistically competitive performance to the state-of-the-art on three diverse and challenging agentic benchmarks: GAIA, AssistantBench, and WebArena. Notably, Magentic-One achieves these results without modification to core agent capabilities or to how they collaborate, demonstrating progress towards the vision of generalist agentic systems. Moreover, Magentic-One’s modular design allows agents to be added or removed from the team without additional prompt tuning or training, easing development and making it extensible to future scenarios. We provide an open-source implementation of Magentic-One and AutoGenBench, a standalone agentic evaluation tool. AutoGenBench provides built-in controls for repetition and isolation to run agentic benchmarks where actions may produce side-effects, in a rigorous and contained way. Magentic-One, AutoGenBench and detailed empirical performance evaluations of Magentic-One, including ablations and error analysis are available at https://aka.ms/magentic-one
Bio: Hussein Mozannar is a Senior Researcher at Microsoft Research AI Frontiers. He obtained his PhD from MIT in Social & Engineering Systems in 2024. His research focuses on augmenting humans with AI to help them complete tasks more efficiently. Specifically, he focuses on building AI models that complement human expertise and designing interaction schemes to facilitate human-AI interaction. Applications of his research include software development, web navigation and healthcare.
Abstract: Modern AI agents, driven by advances in large foundation models, promise to enhance our productivity and transform our lives by augmenting our knowledge and capabilities. To achieve this vision, AI agents must effectively plan, perform multi-step reasoning and actions, respond to novel observations, and recover from errors, to successfully complete complex tasks across a wide range of scenarios. In this work, we introduce Magentic-One, a high-performing open-source agentic system for solving such tasks. Magentic-One uses a multi-agent architecture where a lead agent, the Orchestrator, plans, tracks progress, and re-plans to recover from errors. Throughout task execution, the Orchestrator also directs other specialized agents to perform tasks as needed, such as operating a web browser, navigating local files, or writing and executing Python code. Our experiments show that Magentic-One achieves statistically competitive performance to the state-of-the-art on three diverse and challenging agentic benchmarks: GAIA, AssistantBench, and WebArena. Notably, Magentic-One achieves these results without modification to core agent capabilities or to how they collaborate, demonstrating progress towards the vision of generalist agentic systems. Moreover, Magentic-One’s modular design allows agents to be added or removed from the team without additional prompt tuning or training, easing development and making it extensible to future scenarios. We provide an open-source implementation of Magentic-One and AutoGenBench, a standalone agentic evaluation tool. AutoGenBench provides built-in controls for repetition and isolation to run agentic benchmarks where actions may produce side-effects, in a rigorous and contained way. Magentic-One, AutoGenBench and detailed empirical performance evaluations of Magentic-One, including ablations and error analysis are available at https://aka.ms/magentic-one
Bio: Hussein Mozannar is a Senior Researcher at Microsoft Research AI Frontiers. He obtained his PhD from MIT in Social & Engineering Systems in 2024. His research focuses on augmenting humans with AI to help them complete tasks more efficiently. Specifically, he focuses on building AI models that complement human expertise and designing interaction schemes to facilitate human-AI interaction. Applications of his research include software development, web navigation and healthcare.
