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ux designers for automation tools

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Users face hard to spot failures when delegating tasks to ai agents

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

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.

Mon 26 OctHuman-Computer InteractionArtificial Intelligence
The gist
People are letting AI helpers do complicated tasks for them, but these AI agents sometimes do things differently than expected, causing problems called delegation failures. The authors studied over 160,000 online posts to identify 23 types of these failures grouped into seven categories. They found that some bad mistakes are serious but also difficult for users to notice, especially if users haven't experienced them before. This work helps guide improvements for making AI helpers easier to watch over and more reliable.
Open → 2610.34748v1