Research Report 6: Agentic Shopping is Complicated and Contingent

This is the sixth in a series of short reports that help business, education, and policy leaders understand the technical details of working with AI through rig

What Is Your AI Agent Buying? Evaluation, Biases, Model Dependence, & Emerging Implications for Agentic E-Commerce

Online marketplaces will be transformed by autonomous AI agents acting on behalf of consumers. Rather than humans browsing and clicking, AI agents can parse webpages or leverage APIs to view, evaluate and choose products. We investigate the behavior of AI agents using ACES, a provider-agnostic framework for auditing agent decision-making. We reveal that agents can exhibit choice homogeneity, often concentrating demand on a few ``modal'' products while ignoring others entirely. Yet, these preferences are unstable: model updates can drastically reshuffle market shares. Furthermore, randomized trials show that while agents have improved over time on simple tasks with a clearly identified best choice, they exhibit strong position biases -- varying across providers and model versions, and persisting even in text-only "headless" interfaces -- undermining any universal notion of a ``top'' rank. Agents also consistently penalize sponsored tags while rewarding platform endorsements, and sensitivities to price, ratings, and reviews vary sharply across models. Finally, we demonstrate that sellers can respond: a seller-side agent making simple, query-conditional description tweaks can drive significant gains in market share. These findings reveal that agentic markets are volatile and fundamentally different from human-centric commerce, highlighting the need for continuous auditing and raising questions for platform design, seller strategy and regulation.

Agentic Taste Modeling | lab notes #8

We built a forecasting benchmark to test how well agents predict what you like.

A Framework for Studying AI Agent Behavior: Evidence from Consumer Choice Experiments

Environments built for people are increasingly operated by a new class of economic actors: LLM-powered software agents making decisions on our behalf. These decisions range from our purchases to travel plans to medical treatment selection. Current evaluations of these agents largely focus on task competence, but we argue for a deeper assessment: how these agents choose when faced with realistic decisions. We introduce ABxLab, a framework for systematically probing agentic choice through controlled manipulations of option attributes and persuasive cues. We apply this to a realistic web-based shopping environment, where we vary prices, ratings, and psychological nudges, all of which are factors long known to shape human choice. We find that agent decisions shift predictably and substantially in response, revealing that agents are strongly biased choosers even without being subject to the cognitive constraints that shape human biases. This susceptibility reveals both risk and opportunity: risk, because agentic consumers may inherit and amplify human biases; opportunity, because consumer choice provides a powerful testbed for a behavioral science of AI agents, just as it has for the study of human behavior. We release our framework as an open benchmark for rigorous, scalable evaluation of agent decision-making.

Agents First

Every product is getting a second customer — the human who pays, and the agent who decides. A design framework for building products that AI agents can use as primary consumers.

A guide to the anatomy of effective commerce agents | Claude by Anthropic

The architecture, latency & cost techniques, and eval practices for agents that make it easier to buy and sell online.

Can AI responses be influenced? The SEO industry is trying

Marketing firms are going all in on AI search.

LukeW | Agent Management Interface Patterns

As an increasing number of AI applications evolve to agents doing work for people, agent management becomes a critical part of these product's design. How can p...

Content Independence Day, one year on- building the business model for the agentic Internet

One year after declaring Content Independence Day, a dynamic market for monetized content has officially emerged. In this report, we examine how the rise of autonomous AI agents is upending traditional search referrals and detail the new infrastructure required to support a sustainable web economy.

commerce-agents/plugins/commerce-builder/skills/commerce-evals/SKILL.md at fd4d59224ab96b43c6dc6888207c67b3bd5a24cf · anthropics/commerce-agents

Reference blueprint for building shopping and merchant agents with Claude. Examples in retail, commerce, telecom, and entertainment included. - anthropics/commerce-agents

AI Agents Are Taking Over: And That’s Good For Business

Autonomous AI societies are transforming commerce with self-governing, agent-driven systems, redefining the future of business through collaborative intelligence.

commerce-agents/shopping-agent/core/shopping_agent/gates.py at fd4d59224ab96b43c6dc6888207c67b3bd5a24cf · anthropics/commerce-agents

Reference blueprint for building shopping and merchant agents with Claude. Examples in retail, commerce, telecom, and entertainment included. - anthropics/commerce-agents

Agentic Search for Dummies — Benjamin Anderson

A simple, effective baseline for building AI search agents.