What Is Your AI Agent Buying? Evaluation, Implications and Emerging
  Questions for Agentic E-Commerce
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.
68937517741a16f544fbcd63
Amine Allouah
claimed_verified
2025-08-06T19:13:07.961Z
64753220a855203d8fee2090
/avatars/543717791e29cdfb1475443ff01b9186.svg
Amine
user
AmineAllo
68937517741a16f544fbcd64
Omar Besbes
68937517741a16f544fbcd65
Josué D Figueroa
68937517741a16f544fbcd66
Yash Kanoria
68937517741a16f544fbcd67
Akshit Kumar
What Is Your AI Agent Buying? Evaluation, Implications and Emerging
  Questions for Agentic E-Commerce
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.
ACES, a sandbox environment, studies AI agents' shopping behavior in a mock marketplace, revealing position effects, sensitivity to sponsored tags, endorsements, prices, ratings, and reviews, and highlighting implications for seller strategies and platform design.
vision-language-model VLM ACES sandbox environment mock marketplace position effects sponsored tags endorsements price sensitivity rating sensitivity review sensitivity AI-mediated shopping market-share gains competition questions