Why Johnny Can’t Use Agents: Industry Aspirations vs. User Realities with AI Agents

There is growing imprecision about what “AI agents” are, what they can do, and how effectively they can be used by their intended users. We pose two key research questions: (i) How does the tech industry conceive and market “AI agents”? (ii) What challenges do end-users face when attempting to use commercial AI agents for their advertised uses? We first performed a systematic review of marketed use cases for 102 commercial AI agents, finding that they fall into three umbrella categories: orchestration, creation, and insight. We then evaluated whether end-users could realize these marketed capabilities in practice: we conducted a usability assessment where N = 31 participants attempted representative tasks for each of these categories on two popular commercial AI agent tools: Operator and Manus. We found that users were generally impressed with these agents but faced significant usability challenges ranging from agent capabilities that were misaligned with user mental models to agents lacking the meta-cognitive abilities necessary for effective collaboration.

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Publication Details

Published
2026-05-22
DOI
https://doi.org/10.1145/3786335.3813140
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
0.00

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article

Why Johnny Can’t Use Agents: Industry Aspirations vs. User Realities with AI Agents

Sanjay Krishnan, Sauvik Das, Pradyumna Shome
AI in Service Interactions
article

Why Johnny Can’t Use Agents: Industry Aspirations vs. User Realities with AI Agents

Sanjay Krishnan, Sauvik Das, Pradyumna Shome
article en

Abstract

There is growing imprecision about what “AI agents” are, what they can do, and how effectively they can be used by their intended users. We pose two key research questions: (i) How does the tech industry conceive and market “AI agents”? (ii) What challenges do end-users face when attempting to use commercial AI agents for their advertised uses? We first performed a systematic review of marketed use cases for 102 commercial AI agents, finding that they fall into three umbrella categories: orchestration, creation, and insight. We then evaluated whether end-users could realize these marketed capabilities in practice: we conducted a usability assessment where N = 31 participants attempted representative tasks for each of these categories on two popular commercial AI agent tools: Operator and Manus. We found that users were generally impressed with these agents but faced significant usability challenges ranging from agent capabilities that were misaligned with user mental models to agents lacking the meta-cognitive abilities necessary for effective collaboration.

Carnegie Mellon University (US)
National Science Foundation
Openalex Percentile: Top 99%
AI in Service Interactions
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