# From Product Search to Preference Articulation: The Economics of Agentic Commerce

arXiv: https://arxiv.org/abs/2608.08395 · HTML: https://mlsanity.com/paper/2608.08395/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 7 of its 41 references that are in the map.
- First posted on arXiv 9 Aug 2026.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 7 of 7)

- [An Economy of AI Agents](https://mlsanity.com/paper/2509.01063/) (2025)
- [The Agentic Economy](https://mlsanity.com/paper/2505.15799/) (2025)
- [Generative AI and Copyright: A Dynamic Perspective](https://mlsanity.com/paper/2402.17801/) (2024)
- [On the Fragility of AI Agent Collusion](https://mlsanity.com/paper/2603.20281/) (2026)
- [Artificial Intelligence Clones](https://mlsanity.com/paper/2501.16996/) (2025)
- [A Solicit-Then-Suggest Model of Agentic Purchasing](https://mlsanity.com/paper/2603.20972/) (2026)
- [The Economics of AI Foundation Models: Openness, Competition, and Governance](https://mlsanity.com/paper/2510.15200/) (2025)

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

- [Who Keeps the Gains from Personal AI Assistants? Seller Adaptation and the Unassisted in a Language-Model Market Simulation](https://mlsanity.com/paper/2610.05823/) (2026)

## Abstract (arXiv)

Generative AI is shifting digital commerce from browsing toward agentic search, in which consumers delegate product discovery to AI agents. We compare manual search, which accurately evaluates a limited product set, with agentic search, which screens a broad catalog through noisy representations of preferences and products. Preference complexity is the number of satisfaction-relevant dimensions that are difficult to articulate before search but readily evaluated upon inspection. Consumers have finite attention and choose search intensity: products inspected manually or preference-refinement depth with an agent. We obtain three findings. First, manual search collapses beyond a finite complexity threshold: inspection ceases, mismatch reaches the no-search benchmark, and platform revenue falls to zero. Agentic search avoids this collapse. Once refinement becomes worthwhile, it remains worthwhile as complexity rises; mismatch stays below the no-search benchmark and revenue remains positive, although articulation effort and mismatch may increase. Second, platforms rank the regimes by conversion revenue, whereas consumers also bear search expenditure. When manual inspection is sufficiently inexpensive, agentic search becomes revenue-superior before consumers voluntarily adopt it, creating an adoption lag in which consumers rationally continue manual search. Third, conditional on agentic participation, platforms may assign lower fidelity to consumers with larger attention budgets because they can offset noisier representations through additional refinement, yielding an inverted fidelity allocation. Agentic commerce thus shifts scarcity from product inspection to preference articulation, making consumers' willingness and ability to interact central to voluntary use and platform fidelity design.