Behind the Prompt: The Agent-User Problem in Information Retrieval
Abstract
User models in information retrieval rest on a foundational assumption that observed behavior reveals intent. This assumption collapses when the user is an AI agent privately configured by a human operator. For any action an agent takes, a hidden instruction could have produced identical output - making intent non-identifiable at the individual level. This is not a detection problem awaiting better tools; it is a structural property of any system where humans configure agents behind closed doors. We investigate the agent-user problem through a large-scale corpus from an agent-native social platform: 370K posts from 47K agents across 4K communities. Our findings are threefold: (1) individual agent actions cannot be classified as autonomous or operator-directed from observables; (2) population-level platform signals still separate agents into meaningful quality tiers, but a click model trained on agent interactions degrades steadily (-8.5% AUC) as lower-quality agents enter training data; (3) cross-community capability references spread endemically ( 1.26-3.53) and resist suppression even under aggressive modeled intervention. For retrieval systems, the question is no longer whether agent users will arrive, but whether models built on human-intent assumptions will survive their presence.
Cite
@article{arxiv.2603.03630,
title = {Behind the Prompt: The Agent-User Problem in Information Retrieval},
author = {Saber Zerhoudi and Michael Granitzer and Dang Hai Dang and Jelena Mitrovic and Florian Lemmerich and Annette Hautli-Janisz and Stefan Katzenbeisser and Kanishka Ghosh Dastidar},
journal= {arXiv preprint arXiv:2603.03630},
year = {2026}
}