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Computer Science > Artificial Intelligence

arXiv:2507.22358 (cs)
[Submitted on 30 Jul 2025]

Title:Magentic-UI: Towards Human-in-the-loop Agentic Systems

Authors:Hussein Mozannar, Gagan Bansal, Cheng Tan, Adam Fourney,
Victor Dibia, Jingya Chen, Jack Gerrits, Tyler Payne, Matheus Kunzler
Maldaner, Madeleine Grunde-McLaughlin, Eric Zhu, Griffin Bassman,
Jacob Alber, Peter Chang, Ricky Loynd, Friederike Niedtner, Ece Kamar
, Maya Murad, Rafah Hosn, Saleema Amershi
View a PDF of the paper titled Magentic-UI: Towards Human-in-the-loop
Agentic Systems, by Hussein Mozannar and 19 other authors
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    Abstract:AI agents powered by large language models are
    increasingly capable of autonomously completing complex,
    multi-step tasks using external tools. Yet, they still fall short
    of human-level performance in most domains including computer
    use, software development, and research. Their growing autonomy
    and ability to interact with the outside world, also introduces
    safety and security risks including potentially misaligned
    actions and adversarial manipulation. We argue that
    human-in-the-loop agentic systems offer a promising path forward,
    combining human oversight and control with AI efficiency to
    unlock productivity from imperfect systems. We introduce
    Magentic-UI, an open-source web interface for developing and
    studying human-agent interaction. Built on a flexible multi-agent
    architecture, Magentic-UI supports web browsing, code execution,
    and file manipulation, and can be extended with diverse tools via
    Model Context Protocol (MCP). Moreover, Magentic-UI presents six
    interaction mechanisms for enabling effective, low-cost human
    involvement: co-planning, co-tasking, multi-tasking, action
    guards, and long-term memory. We evaluate Magentic-UI across four
    dimensions: autonomous task completion on agentic benchmarks,
    simulated user testing of its interaction capabilities,
    qualitative studies with real users, and targeted safety
    assessments. Our findings highlight Magentic-UI's potential to
    advance safe and efficient human-agent collaboration.

Subjects: Artificial Intelligence (cs.AI); Human-Computer Interaction
          (cs.HC)
Cite as:  arXiv:2507.22358 [cs.AI]
          (or arXiv:2507.22358v1 [cs.AI] for this version)
          https://doi.org/10.48550/arXiv.2507.22358
          Focus to learn more
          arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Hussein Mozannar [view email]
[v1] Wed, 30 Jul 2025 03:49:14 UTC (1,865 KB)
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