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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 (https://agent-gui-project.github.io/) and open-source repository (https://github.com/eth-medical-ai-lab/agent-gui), along with a demo video (https://youtube.com/watch?v=GSDyxN1gTF0).</abstract>
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%0 Conference Proceedings
%T AgentGUI: An Interface for Observing and Steering Long-Running AI Agents
%A Zhao, Xuan
%A Sohn, Jiwoong
%A Zheng, Qinyue
%A Moor, Michael
%Y Agrawal, Sweta
%Y Abad, Alberto
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-461-3
%F zhao-etal-2026-agentgui
%X 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 (https://agent-gui-project.github.io/) and open-source repository (https://github.com/eth-medical-ai-lab/agent-gui), along with a demo video (https://youtube.com/watch?v=GSDyxN1gTF0).
%U https://aclanthology.org/2026.emnlp-demos.84/
%P 892-900

Markdown (Informal)

[AgentGUI: An Interface for Observing and Steering Long-Running AI Agents](https://aclanthology.org/2026.emnlp-demos.84/) (Zhao et al., EMNLP 2026)

• AgentGUI: An Interface for Observing and Steering Long-Running AI Agents (Zhao et al., EMNLP 2026)

ACL

• Xuan Zhao, Jiwoong Sohn, Qinyue Zheng, and Michael Moor. 2026. AgentGUI: An Interface for Observing and Steering Long-Running AI Agents. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 892–900, Budapest, Hungary. Association for Computational Linguistics.