# Shopping by algorithm: How agentic AI deploys human heuristics as a surrogate consumer

arXiv: https://arxiv.org/abs/2609.28372 · HTML: https://mlsanity.com/paper/2609.28372/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use.
- Builds on 5 of its 83 references that are in the map.
- First posted on arXiv 23 Sep 2026.

## Placed in

- LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/)

## Builds on (most cited first, 5 of 5)

- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [The Bias is in the Details: An Assessment of Cognitive Bias in LLMs](https://mlsanity.com/paper/2509.22856/) (2025)
- [Does Rank Still Matter? Position Bias When AI Agents Shop on Our Behalf](https://mlsanity.com/paper/2608.22697/) (2026)
- [Every Token Counts: Exact Likert-Scale Distributions for Measuring LLM Attitudes and Biases](https://mlsanity.com/paper/2608.10503/) (2026)
- [Whom Do AI Agents Work For? Role Assignment Induces Sponsorship Bias in LLM Recommenders](https://mlsanity.com/paper/2609.17989/) (2026)

## Built on by (most cited first, 1 of 1)

- [Get In, Decide, Prove: A Merchant-Side Readiness Benchmark for Agentic Commerce](https://mlsanity.com/paper/2610.17378/) (2026)

## Abstract (arXiv)

Consumers increasingly delegate purchasing decisions to Large Language Models (LLMs) acting as surrogate consumers. Using "Tool-Lab," an adaptation of information-board process tracing that places product attributes behind costly tool calls, we examine how marketing pricing cues (i.e., just-below pricing and promotional framing) influence AI shopping agents. Across eight commercially deployed LLMs from three providers, we trace pre-choice information acquisition. Under zero cost, pricing cues rarely mislead. Imposing acquisition costs under a vague goal prompt leads LLMs to omit diagnostic attributes required to compute unit price and choose suboptimal choices resembling human heuristics. Relative to a specific goal prompt that mainly preserves diagnostic search and choice optimality, a vague goal prompt under constraints creates a search-mediated vulnerability. This research demonstrates that marketing heuristics in delegated AI shopping are governed by storefront information architecture, not necessarily immutable LLM flaws.