AgentGUI: An Interface for Observing and Steering Long-Running AI Agents

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[Submitted on 28 Jul 2026]

Title:AgentGUI: An Interface for Observing and Steering Long-Running AI Agents

View a PDF of the paper titled AgentGUI: An Interface for Observing and Steering Long-Running AI Agents, by Xuan Zhao and 3 other authors

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Abstract:AI agents are increasingly adept at tackling complex, long-running tasks. With the rapid surge of autonomous capabilities, human oversight is systematically lagging behind due to limited human-centered interfacing. Aiming to address this, we introduce AgentGUI, a user-friendly, locally hosted GUI for seamlessly observing and steering AI agents amid multiple concurrent, long-running sessions. AgentGUI features 1) rich agent trajectory visualizations, 2) effective manual and automated steering, and 3) integration with and coordination between open-source and frontier agent frameworks. A controlled user study demonstrates statistically significant reduction in the time it takes to identify key elements from agent traces (38% faster, p = 0.023). In a preliminary experiment, AgentGUI's automated drift prevention feature raises the task completion rate of small local agents by as high as 34pp across a 0.8B--9B model ladder (N=50 runs per model). AgentGUI is publicly available through its project website (this https URL) and open-source repository (this https URL), along with a demo video (this https URL).

Subjects:

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)

Cite as: arXiv:2607.26300 [cs.CL]

(or arXiv:2607.26300v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.26300

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Xuan Zhao [view email] [v1] Tue, 28 Jul 2026 21:47:22 UTC (2,842 KB)

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