What happens when your next customer never visits your website? Not because they didn't find you, but because an AI agent found you, evaluated your product, compared it against three competitors, and completed the purchase, all without a human ever opening a browser tab. This isn't a thought experiment. It's happening now, and it's about to reshape how brands think about commerce.
AI agents in commerce are autonomous software systems that research, compare, and buy products on behalf of consumers. Unlike recommendation engines that suggest options for humans to pick, these agents make the full decision loop: identify a need, gather options, evaluate tradeoffs, and execute a purchase. Amazon Rufus answers product questions and steers buying decisions within Amazon's ecosystem. Google Shopping AI synthesizes reviews, pricing, and availability across retailers. And a growing category of third-party agents (like Perplexity's shopping features and standalone purchase bots) operate across the open web with no loyalty to any single platform.
How AI Agents Are Changing the Purchase Path
The traditional purchase funnel assumes a human at every stage: awareness, consideration, decision, purchase. AI agents collapse that funnel. A consumer says "order me running shoes under $120 with good arch support" and the agent handles the rest. The human never sees your product page, never reads your reviews, never clicks your ad.
This matters because the signals AI agents use to select products are different from the signals humans use. Agents don't respond to emotional branding, lifestyle photography, or urgency-driven copy ("Only 3 left!"). They process structured data: specifications, ratings, price, availability, return policies, and shipping speed. If your product data is incomplete or inconsistent, the agent skips you entirely.
Gartner predicts that by 2028, 60% of brands will use agentic AI to deliver streamlined one-to-one interactions, and AI agents will become a standard part of the online purchase process. Salesforce's 2025 Connected Shoppers report found that 39% of consumers have used an AI tool to help with a purchase decision. The shift is happening faster in categories where comparison is straightforward: electronics, household goods, supplements, and commodity products.
The Three Patterns of Agentic Commerce
1. Platform-native agents
Amazon Rufus is the clearest example. It sits inside Amazon's app and website, answering product questions with conversational AI. A shopper asks "what's the best protein powder for someone with a dairy allergy?" and Rufus pulls from Amazon's product catalog, reviews, and Q&A sections to recommend specific products. Rufus influenced over 250 million shoppers in its first year (Amazon Q4 2024 earnings call). Brands that don't optimize their Amazon listings for Rufus are invisible to a growing share of Amazon's shoppers.
2. Search-integrated agents
Google Shopping AI and Google AI Mode are merging traditional search with agentic behavior. When a user asks Google a product question, the AI doesn't just show a list of links. It synthesizes information from product feeds, reviews, and merchant sites to present a curated answer. If your product feed is missing key attributes (material, dimensions, compatibility) or your reviews are sparse, Google's AI won't include you in its synthesis.
3. Independent purchase agents
This is the category to watch. Companies are building AI agents that operate on behalf of a consumer across the entire web. These agents can browse multiple retailers, compare shipping and pricing, evaluate return policies, and check inventory. Some can even complete checkout autonomously. Perplexity launched shopping features in late 2024 that let users buy products directly from AI-generated recommendations. Others, like Rabbit R1 and various "AI personal shopper" startups, aim to handle recurring purchases entirely without human intervention.
Is your brand visible to AI shopping agents? AI agents pull from product feeds, structured data, and brand entity signals to make purchase recommendations. AI Radar tracks how ChatGPT represents your brand and products, so you can see exactly what agents see when they evaluate you. Request a demo.
What AI Agents Look For (And What They Ignore)
AI agents are not humans browsing a website. They don't care about your hero banner, your founder story, or your Instagram aesthetic. Here's what they actually process:
- Structured product data: Title, description, price, availability, dimensions, weight, materials, compatibility, SKU. Missing fields mean missing from results. Google's Merchant Center documentation emphasizes that products with complete structured data receive preferential placement in AI-powered shopping surfaces, including Google Shopping's AI features and product recommendations.
- Reviews and ratings: Both aggregate scores and the text content of reviews. AI agents parse review text to understand product strengths and weaknesses, not just the star rating. A product with a 4.2 rating and detailed reviews mentioning "great arch support" will beat a 4.5-rated product with generic "great product!" reviews for an arch-support query.
- Price and value signals: Current price, historical pricing patterns, price relative to competitors, and bundle or subscription options. AI agents are better at price comparison than any human shopper.
- Availability and fulfillment: In-stock status, shipping speed, return policy, and warranty. An agent optimizing for its user won't recommend a product that ships in 14 days when a comparable option ships in 2.
- Brand entity signals: Does the agent recognize your brand? Consistent brand entity optimization across the web helps AI agents associate your brand with specific product categories and quality tiers.
How to Prepare Your Brand for Agentic Commerce
Clean up your product data
Audit every product feed you publish: Google Merchant Center, Amazon Seller Central, your own structured data markup. Fill every attribute field. Use consistent units and terminology. If a field is optional, fill it anyway. AI agents use every available data point.
Optimize for conversational queries
AI agents process natural language requests, not keyword strings. "Best lightweight laptop for travel under $1,000 with 16GB RAM" is a real agent query. Your product data needs to match these conversational, multi-attribute queries. Add FAQ schemas to your product pages. Include use-case descriptions alongside technical specs.
Build review volume and quality
AI agents weight review content heavily. Encourage detailed reviews from customers, especially reviews that mention specific use cases and product attributes. "This backpack fits a 15-inch laptop, holds a water bottle in the side pocket, and survived a week of rain in Portland" gives AI agents far more useful signal than "Love it! Five stars."
Implement schema markup aggressively
Product schema, Review schema, Offer schema, FAQ schema. Schema markup for AI is the language AI agents understand best. Test your implementation with Google's Rich Results Test and monitor for errors in Search Console.
Think beyond your own website
AI agents don't just visit your site. They aggregate information from Amazon, Google Shopping, review sites, comparison platforms, and third-party blogs. Your brand's representation across all these touchpoints affects how agents evaluate you. An inconsistent price between your site and Amazon, or conflicting product specs between your listing and a review site, creates a trust signal problem for AI agents.
Example: Fixing Invisible Product Data
A supplement brand noticed their protein powder was never recommended when they asked ChatGPT "what's the best protein powder for muscle recovery under $40?" Despite strong Amazon reviews (4.6 stars, 3,200+ ratings), the AI consistently recommended three competitors. The problem: their product feed was missing key structured attributes — grams of protein per serving, amino acid profile, certifications, and allergen information. Competitors had complete feeds. After a structured data overhaul across Amazon, Google Merchant Center, and their own website (adding Product schema, FAQ schema, and filling every attribute field), the brand started appearing in ChatGPT recommendations within six weeks. Salesforce's 2025 Connected Shoppers report found that 39% of consumers have already used an AI tool to help with a purchase decision. Brands with incomplete product data are invisible to the agents those consumers rely on.
Frequently Asked Questions
Will AI agents replace human shopping entirely?
Not for everything. High-consideration purchases (cars, homes, luxury goods) will continue to involve human judgment. But for repeat purchases, commodity goods, and products where specifications matter more than subjective preference, AI agents will handle an increasing share. The shift is already visible in categories like electronics and household essentials.
Do I need to change my entire e-commerce strategy?
You need to add an AI-agent layer to your existing strategy, not replace it. Humans still shop. But your product data, structured markup, and review strategy need to serve both human browsers and AI agents. The good news: most of what makes your products visible to AI agents (clean data, detailed specs, strong reviews) also improves the human shopping experience.
How do I know if AI agents are already sending me traffic?
Check your analytics for referrals from chat.openai.com, perplexity.ai, and other AI platforms. Look at your Google Search Console for queries that match conversational patterns. And use AI Radar to test how ChatGPT represents your brand and products in response to purchase-intent queries.
What about AI agents making bad purchase decisions for consumers?
This is a real concern and a reason the space is evolving carefully. AI agents that make poor purchasing decisions lose user trust quickly. The market pressure favors agents that are accurate, transparent about tradeoffs, and aligned with user preferences. For brands, this means quality products with honest data win in an agentic commerce world. Misleading product descriptions that fool human shoppers won't fool AI agents comparing your claims against reviews and competitor data.