Agentic Commerce Is Here: How to Make Your Brand Visible to AI Shoppers

Something fundamental shifted in retail over the past few months, and most brand teams haven’t fully processed it yet. AI agents aren’t just recommending products anymore — they’re buying them. ChatGPT now has an Instant Checkout feature. Google launched the Universal Commerce Protocol. And 45% of consumers already use AI for at least part of their buying journey, according to the IBM Institute for Business Value.

The era of agentic commerce has arrived, and it changes everything about how brand visibility works.

What Is Agentic Commerce?

Agentic commerce is exactly what it sounds like: AI agents acting as purchasing agents on behalf of consumers. Rather than browsing websites, comparing options, and clicking “Add to Cart” themselves, shoppers can now tell an AI assistant what they need and let it handle the rest — research, comparison, selection, and in some cases, checkout.

This isn’t theoretical. Here’s what’s already live:

ChatGPT Instant Checkout: U.S. users can buy directly from Etsy sellers inside the chat, with over a million Shopify merchants coming online. OpenAI has also announced partnerships with Target, Instacart, and DoorDash for in-chat purchasing.
Google’s Universal Commerce Protocol (UCP): Announced at NRF 2026 in January, UCP is an open standard that lets AI agents complete the full shopping journey — discovery, purchase, and post-purchase support — across Google AI Mode and Gemini. Backed by Shopify, Walmart, Target, Visa, Mastercard, and 20+ other partners.
Meta’s AI Commerce Tools: Meta is building AI agents that can facilitate purchases across its platforms, connecting product catalogs to conversational AI.

Morgan Stanley estimates that agentic shoppers could represent $190 billion to $385 billion in U.S. e-commerce spending by 2030. That’s 10-20% of the entire market transacted by machines.

Why This Changes Brand Visibility

Traditional e-commerce follows a well-understood path: drive traffic to your site, optimize the conversion funnel, build direct customer relationships. Agentic commerce disrupts every step.

When an AI agent shops for a consumer, there is no website visit. There is no search results page to rank on. There is no banner ad to catch someone’s eye. The AI reviews product data, evaluates options against the user’s criteria, and makes a recommendation — often completing the purchase without the consumer ever seeing your product page.

This creates a stark visibility problem: if an AI agent doesn’t know your product exists, or can’t understand your product data well enough to recommend it, you’re invisible. Not just lower in rankings — completely absent from the consideration set.

For brands that have spent years optimizing for human shoppers, this is an uncomfortable shift. The AI agent doesn’t care about your hero image, your carefully crafted product storytelling, or your influencer partnerships. It cares about structured data, clear attributes, competitive pricing signals, and whether your product genuinely matches the user’s intent.

The Two Layers of Agentic Visibility

To succeed in agentic commerce, brands need visibility at two distinct layers:

Layer 1: Discovery — Getting Into the Consideration Set

Before an AI agent can recommend your product, it needs to find you. This is where your existing AI search visibility efforts (GEO, AEO, off-site authority) remain critical. The agent’s first step is essentially an AI search query: “What are the best running shoes for flat feet under $150?”

Everything we know about AI search visibility still applies here. Your brand needs to appear in AI responses to relevant queries. The difference is the stakes: in a traditional AI search, a user might see your brand mentioned alongside competitors and do further research. In agentic commerce, the agent might narrow the field to three options and present a single recommendation. If you’re not in the initial consideration set, there’s no second chance.

What to do:

Ensure your product information is indexed by Bing (ChatGPT’s data source) and Google
Build authoritative off-site presence through reviews, editorial mentions, and third-party comparisons
Create content that directly answers the specific queries your target customers ask AI assistants
Monitor which brands AI platforms recommend for your key product categories

Layer 2: Selection — Winning the Recommendation

Getting discovered is necessary but not sufficient. The agent also needs to select your product over alternatives. This is where structured data becomes the decisive factor.

AI agents evaluate products programmatically. They’re comparing specifications, ratings, availability, price, shipping terms, and return policies across multiple options simultaneously. The brand with the cleanest, most complete, and most machine-readable product data wins.

Research from Profound’s analysis of 250M AI responses found that high-citation products contain 848% more FAQs, 103% more videos, and 36% higher ratings than low-citation products. In agentic commerce, this advantage compounds — the agent isn’t just citing your product, it’s recommending it for purchase.

What to do:

Implement comprehensive schema markup (Product, Offer, AggregateRating, FAQ) using JSON-LD
Ensure product feeds include complete attributes: stock status, dimensions, materials, compatibility, shipping details
Maintain accurate, up-to-date pricing across all channels — AI agents will cross-reference
Build a rich review profile, as aggregate ratings heavily influence AI recommendations

A Practical Playbook for Agentic Commerce Readiness

Here’s a prioritized checklist for brand and marketing teams:

1. Audit Your Structured Data (This Week)

Run your key product pages through Google’s Rich Results Test and Schema.org’s validator. Check for:

Product schema with complete attributes (name, description, brand, SKU, price, availability, condition)
Offer schema with current pricing, currency, seller information, and shipping details
AggregateRating schema with review count and average rating
FAQ schema on product and category pages answering common purchase questions

Pages with proper schema markup have a 2.5x higher chance of appearing in AI-generated answers. For agentic commerce, this multiplier is likely even higher, since agents need structured data to make purchase decisions.

2. Clean Up Your Product Feeds

If you’re on Shopify, you’re already eligible for ChatGPT Instant Checkout and Google’s UCP — no custom integration required. But eligibility doesn’t guarantee visibility. Your product data quality determines whether AI agents surface your products.

Review your feeds for:

Completeness: Every field filled, not just the required ones. Include materials, care instructions, dimensions, compatibility notes.
Accuracy: Prices, stock levels, and shipping estimates must match reality. AI agents that recommend out-of-stock products or wrong prices create bad user experiences, and platforms will deprioritize unreliable merchants.
Specificity: “Blue cotton t-shirt, men’s medium, crew neck, pre-shrunk” beats “Men’s T-Shirt” every time an agent is matching against a natural language query.

3. Build Your Off-Site Authority

AI agents don’t just look at your product data. They cross-reference external sources to validate claims and assess trustworthiness. This means your off-site presence matters more, not less, in agentic commerce.

Priority actions:

Cultivate genuine reviews on platforms AI agents reference (Google, Amazon, specialized review sites in your vertical)
Pursue editorial mentions and comparisons in publications that AI platforms frequently cite
Maintain consistent NAP (Name, Address, Phone) and product information across all directories and marketplaces
Consider your presence on Reddit, YouTube, and LinkedIn — Perplexity in particular weights user-generated content heavily

4. Adopt Commerce Protocols Early

Google’s UCP is open source and designed to work with existing retail infrastructure. If you’re a Shopify merchant, enabling UCP is a settings toggle. For custom platforms, integration uses REST and JSON-RPC transports with compatibility for the Agent2Agent (A2A) protocol and Model Context Protocol (MCP).

Early adopters will have a structural advantage. As AI agents learn which merchants provide reliable, complete data and good transaction experiences, they’ll develop implicit preferences — just as Google’s algorithm developed preferences for sites with good user experience signals.

5. Monitor Your AI Shopping Visibility

This is the critical piece most brands are missing. You need to know:

Which products AI agents recommend when users ask about your category
Which competitors appear in AI shopping recommendations
How your product data is being interpreted by different AI platforms
Whether your brand sentiment in AI responses supports or undermines purchase decisions

Manual spot-checking doesn’t work at scale. When AI agents are making split-second decisions across thousands of product queries, you need systematic monitoring to understand your visibility and identify gaps.

The Brands That Will Win

The transition to agentic commerce won’t happen overnight, but it’s happening faster than most teams expect. The brands that will thrive are those that treat AI agents as a primary audience alongside human shoppers.

This means investing in data quality over creative storytelling (at least for this channel). It means ensuring your product information is structured, complete, accurate, and accessible to machines. It means building the kind of authoritative off-site presence that AI agents use to validate recommendations. And it means monitoring your AI visibility systematically so you can adapt as platforms and protocols evolve.

The uncomfortable truth is that in agentic commerce, the buying decision may be made before a human ever sees your product. The question isn’t whether this shift is coming — it’s already here. The question is whether your brand is ready for it.

Ready to monitor your AI search visibility? Start your free trial with RivalHound today.