Buying agents don't search. They recall. Here's how to be what they remember.

Key takeaways

• Buying agents shortlist brands before any human search. The selection happens in the agent's consideration set, not on a search results page.
• Agents pull from three sources: training data, tool-augmented retrieval, and user preference history. Each requires a different optimization strategy.
• Training data optimization requires consistent, attributed, factual brand content published over time. There is no shortcut — it is a 6–12 month accumulation play.
• Retrieval optimization is faster: structured product data, schema markup, and high-citation content that agents can pull at query time can show impact in 4–8 weeks.
• Brands that do not appear in agent consideration sets are not given a chance to compete. The shortlisting is invisible and happens before the user is involved.

Agentic commerce has changed where brand selection happens. It no longer happens at the search results page — it happens inside an AI agent's consideration set, before the user types a query.

What a buying agent actually does

A buying agent receives a task — 'find me the best project management tool for a 3-person agency under $100/month' — and executes it without returning a search results page. It queries its training data, runs retrieval-augmented lookups, checks user preference history, and shortlists 2–4 options. The human sees only the shortlist. Every brand not on it is invisible.

Three sources, three strategies

Buying agents pull from three distinct sources. Knowing which one is moving for your brand determines where to invest:

1. Training data — what the model learned during pretraining. This reflects the body of content that existed about your brand before the model's knowledge cutoff. It changes slowly. Optimization: publish consistent, factual, attributed content across authoritative sources over time.
2. Tool-augmented retrieval — real-time lookups the agent runs during task execution. This is query-time retrieval from structured data sources. Optimization: schema markup, structured product feeds, high-citation content on pages the agent can crawl.
3. User preference history — data from prior user sessions. If a user has interacted positively with your brand in past agent sessions, the agent weights you higher. Optimization: ensure your brand delivers on any previous agent-mediated recommendation — user satisfaction feeds the loop.

Why the window to get in is narrowing

Early agentic commerce benchmarks (Commercetools, McKinsey 2026) suggest that the brands in agent consideration sets skew heavily toward those with 12+ months of attributed, structured content. The compounding effect is real: early entrants into agent training data and retrieval sources are increasingly difficult to displace. The brands investing now are building a lead that will be hard to close in 2027.

The measurement problem

You cannot see your agent consideration set rank. There is no dashboard for 'how often does Perplexity Shopping recommend my brand.' The closest proxy: query AI shopping agents with your target use case and record whether your brand appears, in what position, and what language the agent uses to describe you. That is your current agent consideration set status.

Avakata's engine tracks agent consideration set appearances weekly across ChatGPT, Perplexity, and Google AI Mode — and ships the content changes that move the needle. Ask us what we're seeing for your category.

Frequently asked questions

What is agentic commerce?

Agentic commerce is the use of AI agents to complete purchasing tasks on behalf of users — from product discovery and comparison to checkout. The agent makes shortlisting decisions before the human is involved, fundamentally shifting where and how brand selection happens.

How is agentic commerce different from traditional ecommerce SEO?

Traditional ecommerce SEO optimizes for human users scanning a results page. Agentic commerce optimization targets AI agents that never show the user a results page — they shortlist and recommend directly. The signals are different: structured data and training data citation matter more than keyword rank.

How do I know if my brand is in an AI agent's consideration set?

Query commercial AI agents (ChatGPT, Perplexity, Google AI Mode with Shopping enabled) with your target customer's buying task. Record whether your brand is mentioned, how it is described, and what objections or endorsements the agent surfaces. Do this monthly across 10–15 representative queries.