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[2602.16844] Overseeing Agents Without Constant Oversight: Challenges and Opportunities
Summary
This article explores the challenges and opportunities in overseeing AI agents without constant human oversight, focusing on user studies that evaluate the effectiveness of action traces in enhancing human verification.
Why It Matters
As AI systems become more autonomous, understanding how to effectively oversee them is crucial for ensuring safety and reliability. This research highlights the balance between providing sufficient information for human oversight and avoiding information overload, which is vital for the development of trustworthy AI.
Key Takeaways
- Current oversight practices for AI agents are often cumbersome.
- Proposed design improvements can reduce the time needed for error detection.
- User confidence in decision-making may increase without a corresponding improvement in accuracy.
- Challenges include managing user assumptions and evolving criteria for correctness.
- Effective communication of an agent's reasoning process is essential for oversight.
Computer Science > Human-Computer Interaction arXiv:2602.16844 (cs) [Submitted on 18 Feb 2026] Title:Overseeing Agents Without Constant Oversight: Challenges and Opportunities Authors:Madeleine Grunde-McLaughlin, Hussein Mozannar, Maya Murad, Jingya Chen, Saleema Amershi, Adam Fourney View a PDF of the paper titled Overseeing Agents Without Constant Oversight: Challenges and Opportunities, by Madeleine Grunde-McLaughlin and 5 other authors View PDF HTML (experimental) Abstract:To enable human oversight, agentic AI systems often provide a trace of reasoning and action steps. Designing traces to have an informative, but not overwhelming, level of detail remains a critical challenge. In three user studies on a Computer User Agent, we investigate the utility of basic action traces for verification, explore three alternatives via design probes, and test a novel interface's impact on error finding in question-answering tasks. As expected, we find that current practices are cumbersome, limiting their efficacy. Conversely, our proposed design reduced the time participants spent finding errors. However, although participants reported higher levels of confidence in their decisions, their final accuracy was not meaningfully improved. To this end, our study surfaces challenges for human verification of agentic systems, including managing built-in assumptions, users' subjective and changing correctness criteria, and the shortcomings, yet importance, of communicating the agent's process...
