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This research demonstrates that marketing heuristics in delegated AI shopping are governed by storefront information architecture, not necessarily immutable LLM flaws.
It is argued that an agent's evaluation of a sponsored listing should not depend on which party deployed it, and shown that disclosure mandates designed for human consumers cannot by themselves protect consumers in AI-mediated commerce.
An analytically exact framework for the controlled behavioral evaluation of Large Language Models is introduced, demonstrating how this approach isolates systemic country-of-origin biases that aggregate benchmarks otherwise obscure.
Randomizing the order of one hundred hotel listings across 5,000 AI agent sessions, a comparison of four large language models against human field data shows that AI agents search more deeply than humans and never decline to buy.
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