What Is Your AI Agent Buying? Evaluation, Implications and Emerging
  Questions for Agentic E-Commerce
Authors: name: Amine Allouah, name: Omar Besbes, name: Josué D Figueroa, name: Yash Kanoria, name: Akshit Kumar

Online marketplaces will be transformed by autonomous AI agents acting on
behalf of consumers. Rather than humans browsing and clicking,
vision-language-model (VLM) agents can parse webpages, evaluate products, and
transact. This raises a fundamental question: what do AI agents buy, and why?
We develop ACES, a sandbox environment that pairs a platform-agnostic VLM agent
with a fully programmable mock marketplace to study this question. We first
conduct basic rationality checks in the context of simple tasks, and then, by
randomizing product positions, prices, ratings, reviews, sponsored tags, and
platform endorsements, we obtain causal estimates of how frontier VLMs actually
shop. Models show strong but heterogeneous position effects: all favor the top
row, yet different models prefer different columns, undermining the assumption
of a universal "top" rank. They penalize sponsored tags and reward
endorsements. Sensitivities to price, ratings, and reviews are directionally
human-like but vary sharply in magnitude across models. Motivated by scenarios
where sellers use AI agents to optimize product listings, we show that a
seller-side agent that makes minor tweaks to product descriptions, targeting AI
buyer preferences, can deliver substantial market-share gains if AI-mediated
shopping dominates. We also find that modal product choices can differ across
models and, in some cases, demand may concentrate on a few select products,
raising competition questions. Together, our results illuminate how AI agents
may behave in e-commerce settings and surface concrete seller strategy,
platform design, and regulatory questions in an AI-mediated ecosystem.