Babysitting AI Agents: A User-Centered Taxonomy and Study of Agentic Delegation Failures

Users are increasingly delegating tasks to AI agents, allowing agents to perform long-running, multi-step workflows on their behalf. However, agents may perform these tasks in ways that depart from users' expectations for how they should be carried out, leading to what we call agentic delegation failures. We leverage a large corpus of 163,487 Reddit posts, describing AI agent use, to develop MANDATE: a taxonomy of 23 failure categories organized into seven groups. A follow-up study with 73 AI agent users examined the relationship between prevalence and perceptions of severity and noticeability, finding that failures rated as more severe are considered harder to notice, and that prior experience with a failure is associated with greater noticeability. Finally, we assess the maturity of research on each failure and identify commonly encountered failures that have received little attention (e.g., agents running without making progress). Our findings inform opportunities to make delegation easier for users to oversee and better studied by the research community.

Authors

Publication Details

Journal
arXiv (Cornell University)
Published
2026-10-26
DOI
https://doi.org/10.48550/arxiv.2610.34748
Primary Topic
Usability and User Interface Design
Type
preprint
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Babysitting AI Agents: A User-Centered Taxonomy and Study of Agentic Delegation Failures

Debarati Das, Philippe Laban, Sunnie S. Y. Kim, Jennifer Neville et al.
arXiv (Cornell University)
Usability and User Interface Design
preprint

Babysitting AI Agents: A User-Centered Taxonomy and Study of Agentic Delegation Failures

Debarati Das, Philippe Laban, Sunnie S. Y. Kim, Jennifer Neville, Scott Counts, Shravika Mittal
preprint en

Abstract

Users are increasingly delegating tasks to AI agents, allowing agents to perform long-running, multi-step workflows on their behalf. However, agents may perform these tasks in ways that depart from users' expectations for how they should be carried out, leading to what we call agentic delegation failures. We leverage a large corpus of 163,487 Reddit posts, describing AI agent use, to develop MANDATE: a taxonomy of 23 failure categories organized into seven groups. A follow-up study with 73 AI agent users examined the relationship between prevalence and perceptions of severity and noticeability, finding that failures rated as more severe are considered harder to notice, and that prior experience with a failure is associated with greater noticeability. Finally, we assess the maturity of research on each failure and identify commonly encountered failures that have received little attention (e.g., agents running without making progress). Our findings inform opportunities to make delegation easier for users to oversee and better studied by the research community.

arXiv (Cornell University)
Usability and User Interface Design
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