When Should Users Check? Modeling Confirmation Frequency inMulti-Step Agentic AI Tasks
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918487281106944 |
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| author | Zhou, Jieyu Roy, Aryan Gupta, Sneh Weitekamp, Daniel MacLellan, Christopher J. |
| author_facet | Zhou, Jieyu Roy, Aryan Gupta, Sneh Weitekamp, Daniel MacLellan, Christopher J. |
| contents | Existing AI agents typically execute multi-step tasks autonomously and only allow user confirmation at the end. During execution, users have little control, making the confirm-at-end approach brittle: a single error can cascade and force a complete restart. Confirming every step avoids such failures, but imposes tedious overhead. Balancing excessive interruptions against costly rollbacks remains an open challenge. We address this problem by modeling confirmation as a minimum time scheduling problem. We conducted a formative study with eight participants, which revealed a recurring Confirmation-Diagnosis-Correction-Redo (CDCR) pattern in how users monitor errors. Based on this pattern, we developed a decision-theoretic model to determine time-efficient confirmation point placement. We then evaluated our approach using a within-subjects study where 48 participants monitored AI agents and repaired their mistakes while executing tasks. Results show that 81 percent of participants preferred our intermediate confirmation approach over the confirm-at-end approach used by existing systems, and task completion time was reduced by 13.54 percent. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_05307 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | When Should Users Check? Modeling Confirmation Frequency inMulti-Step Agentic AI Tasks Zhou, Jieyu Roy, Aryan Gupta, Sneh Weitekamp, Daniel MacLellan, Christopher J. Human-Computer Interaction Existing AI agents typically execute multi-step tasks autonomously and only allow user confirmation at the end. During execution, users have little control, making the confirm-at-end approach brittle: a single error can cascade and force a complete restart. Confirming every step avoids such failures, but imposes tedious overhead. Balancing excessive interruptions against costly rollbacks remains an open challenge. We address this problem by modeling confirmation as a minimum time scheduling problem. We conducted a formative study with eight participants, which revealed a recurring Confirmation-Diagnosis-Correction-Redo (CDCR) pattern in how users monitor errors. Based on this pattern, we developed a decision-theoretic model to determine time-efficient confirmation point placement. We then evaluated our approach using a within-subjects study where 48 participants monitored AI agents and repaired their mistakes while executing tasks. Results show that 81 percent of participants preferred our intermediate confirmation approach over the confirm-at-end approach used by existing systems, and task completion time was reduced by 13.54 percent. |
| title | When Should Users Check? Modeling Confirmation Frequency inMulti-Step Agentic AI Tasks |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2510.05307 |