Agentic Commerce Is Here: How to Make Your Products Discoverable by AI

"If your website isn't machine-readable, you're essentially invisible to the millions of people using AI assistants to make buying decisions."

Agentic commerce is here and there is no way around it. It is the world we live in and if your business is done online, you need to pay attention.

When we talk about Agentic commerce, it isn't just about chatbots answering FAQs. It's about AI agents autonomously researching, comparing, negotiating, and purchasing on behalf of consumers. 

With Google announcing a few days ago the launch of its Universal Commerce Protocol (UCP). This is Google’s attempt to standardize how product, pricing, availability, and purchase actions are described and exchanged across systems, especially AI-driven experiences.

For users, this means they can ask an AI to find, compare, and buy a product, and the AI can complete that purchase directly using trusted product and checkout data from the brand, without the user needing to visit multiple websites.

The goal for brands is shifting from “drive traffic to site” to “optimize the on‑platform journey.” This means team will need to focus on product data quality (titles, images, variants, pricing), faster feedback loops on creative, and tighter lifecycle messaging after the purchase. 

eCommerce brands that will win in agentic commerce are the ones who treat their product feed like a landing page and test it constantly. It will be important to double down on owning your customer relationship where you can, since platform checkout reduces your control. Keep testing, Keep learning, and you’ll be fine. 

First Things First, What is Agentic Commerce?

When we say your website needs to be machine-readable, we are not talking about basic SEO or adding a chatbot to your site.

We’re talking about whether AI systems can understand, trust, and act on your data without human involvement.

The era of agentic commerce, means agents are going through the entire customer journey, from discovery to purchase. They don’t scroll through pages, skim headlines, or respond to clever copy. They parse structure. They evaluate certainty. They look for clear signals that answer very specific questions:

• What exactly is being sold?
• Is the information accurate and up to date?
• How does this product compare to alternatives?
• Can a purchase be completed safely and reliably?
• Does this product match exactly the query I was asked?

If those answers aren’t obvious, the agent doesn’t hesitate. It moves on.

How do we move from human-first websites to agent-first decision systems

The expression we eat with our eyes can be applied to ecommerce too " we shop with our eyes". Any eCommerce website you land on today, you notice that experiences were designed for humans first, visuals, persuasion, and conversion paths built around clicks.

The new game of Agentic commerce is shaking that. If the first “user” interacting with your brand is an AI that cant see and can't feel, that AI is going to need precision before persuasion.

This is why recent announcements like the launch of Universal Commerce Protocol (UCP) from Google are such a big deal. UCP is not about improving shopping interfaces. It’s about standardizing how product data, pricing, availability, and purchase actions are described so AI systems can reliably interpret and execute them.

For consumers, this means asking an AI to find, compare, and buy a product in one step. For brands, it means your product data must be clear enough for an AI to complete that transaction without ever visiting your site.

Your website traffic will tank and there is nothing you can do about it 

For years, digital strategy focused on one outcome, getting people to your website. Today, it is no longer the case. 

If an AI can research, evaluate, and complete a purchase on a user’s behalf, then visibility inside the agent’s decision process matters more than pageviews.

The key question shifts from: Did they click?  to Were we selected?

That selection happens upstream, inside systems that assess structured data, content clarity, trust signals, and historical performance. If your information is fragmented, inconsistent, or buried in marketing language, you’re excluded before the user even sees your brand.

The question is: Is your brand prepared for agentic commerce?

These are unchartered waters and being prepared in this case just means are you experimenting? Are you working on making your website clear? Are you tracking your traffic acquisition and website behavior? 

The brands that will win in agentic commerce are already treating:

• Their product catalog like a decision engine
• Their feeds like living landing pages
• Their content like structured knowledge, not just marketing copy

This is also where techniques like AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization) stop being optional. They become the foundation for being understood, trusted, and chosen by AI systems.

10 Actions to help you get your website ready for Agentic Commerce

This is not a six-month transformation plan. It’s not a replatform.

These are foundational actions to make sure AI agents can actually see, understand, and act on your products.

Before advanced AI integrations or agent-led checkout matter, you need to pass a basic test: Can an AI agent clearly identify what you sell, compare it, and decide if it can be purchased safely?

If not, start here.

Agentic Commerce Ready Mission 

Action 1: Audit your product data completeness

Priority: Critical

If your product data is vague, incomplete, or inconsistent, nothing else matters.

AI agents rely on hard identifiers, not brand language. They use standardized signals to compare products across retailers. If those signals are missing, your product is filtered out before a human ever gets involved.

What to do

• Export your full product catalog into a spreadsheet
• Check which products are missing:
  • GTINs (Global Trade Item Numbers): 
  • MPNs (Manufacturer Part Numbers)
• Flag descriptions that rely on phrases like:
  • “Premium quality”
  • “Best-in-class”
  • “High performance”
• Count how many products include structured specifications versus narrative copy

If you can’t measure it, an agent can’t compare it.

What difference will this make
GTINs and MPNs act as anchors for AI systems. They allow agents to line up products side by side, verify accuracy, and decide what belongs in a comparison table. No identifier means no comparison. No comparison means no recommendation.

Example

❌ Before
“Premium merino wool sweater with exceptional comfort and timeless style.”

✓ After
“Merino wool crew neck sweater | 100% merino wool | 200gsm | Temperature range: -5°C to 15°C | Machine washable | GTIN: 0123456789012 | Sizes XS–XXL | 6 colors”

This isn’t worse copy. It’s usable copy.

Action 2: Test your schema markup

Priority: Critical

If your site doesn’t clearly label what a product is, AI agents will guess. And when agents guess, they usually skip. Schema markup is how you remove ambiguity.

What to do

• Test your top product pages using Google’s Rich Results Test
• Check whether key properties are present:
  • Price
  • Availability
  • Brand
  • GTIN
  • Reviews
• Confirm your schema is written in JSON-LD, not embedded markup

If any of these are missing, your product is harder to interpret than it should be.

What difference will this make
AI systems increasingly rely on structured data to reason about products. JSON-LD is not just for search enhancements anymore. It’s becoming the foundation for how large language models understand commerce content.

Action 3: Implement complete Schema.org markup

Priority: Critical

This is where content stops being content and starts becoming infrastructure.

Up to this point, you’ve made your products clearer for humans and agents. Schema is how you make that clarity machine-readable.

Without complete schema markup, AI agents are forced to infer structure from text. Inference introduces uncertainty. Uncertainty leads to exclusion.

What to do

Implement full Schema.org markup across your product templates, not just a few fields. At a minimum, every product should expose:

• Product schema
  • Name
  • Image
  • Description
  • SKU
  • Brand
  • GTIN
  • MPN
• Offer schema
  • Price
  • Currency
  • Availability
  • Valid date ranges
• Review schema
  • Aggregate rating
  • Rating value
  • Review count
• MerchantReturnPolicy
  • Return window
  • Fees
  • Methods
• Organization schema
  • Company name
  • Contact details
  • Social profiles

If any of these are missing, your product is harder to interpret, harder to trust, and easier to skip.

What difference will this make
This is non-negotiable. AI agents prioritize sources that are explicit, structured, and verifiable. Schema markup removes ambiguity and turns your catalog into data objects agents can reason about. 

Without schema, your product page is just text. With schema, it becomes a data object an agent can act on. This is not optional.

Action 4: Make your return policy machine-readable

Priority: High

Humans might skim return policies. Agents don’t. If an AI can’t verify return terms with certainty, it treats the product as risky and moves on.

What to do

• Write your return policy in plain, explicit terms
• Add MerchantReturnPolicy schema
• Include:
  • Return window (number of days)
  • Method (mail, in-store, both)
  • Fees
  • Conditions and exclusions

No ambiguity. No buried footnotes.

What difference will this make
AI agents are risk-averse by design. They optimize to avoid liability on behalf of users. If return terms aren’t clear and verifiable, the safest decision is to skip your product.

Action 5: Check your robots.txt and AI permissions

Priority: High

This one is uncomfortable, but necessary. You may already be blocking the systems you’re trying to be discovered by.

What to do

• Visit yoursite.com/robots.txt
• Check whether you are blocking:
  • GPTBot
  • ChatGPT-User
  • Claude-Web
  • Googlebot-Extended
• Confirm product pages and images are accessible

If an agent can’t crawl you, it can’t recommend you.

Why this matters
Some platforms intentionally block AI crawlers to protect their ecosystem. That may make sense if you are Amazon. For everyone else, invisibility in AI channels is a growth problem, not a protection strategy.

Blocking agents doesn’t preserve traffic. It removes you from the conversation entirely.

Action 6: Rewrite 10 key product descriptions for agents

Priority: High

This is where most teams get it wrong. They optimize product pages to sound good. Agents need them to be useful.

AI agents don’t infer. They extract. If critical facts are buried in paragraphs or missing entirely, your product is skipped.

What to do

• Select your 10 best-selling or highest-margin products
• Strip the description down to facts
• Lead with specifications, not storytelling
• Add comparison-ready details where possible

Think in terms of micro-facts, not paragraphs.

What this looks like

• Dimensions, weight, materials
• Performance metrics
• Compatibility and constraints
• Certifications, ratings, warranties

If two products look identical to an agent, it won’t “dig deeper.” It will choose the one that’s easier to reason about.

What difference will this make
Generative engines reward factual density. Keyword stuffing does nothing. Specificity wins.

Action 7: Monitor your AI visibility

Priority: High

You can’t optimize what you don’t observe. Agentic visibility doesn’t show up neatly in traditional analytics yet. You need to test it directly.

What to do

• Ask AI tools questions your customers would ask:
  • “Find the best [your product category]”
• Track:
  • Whether your brand appears
  • Which competitors do
  • What signals they expose (pricing, specs, reviews)
• Repeat monthly and document changes

If you’re invisible here, SEO dashboards won’t warn you.

What difference will this make
Selection happens inside AI systems, not SERPs. If you’re not monitoring those environments, you’re flying blind.

Action 8: Make your images legible to AI

Priority: Medium

Agents don’t just read anymore. They look. With multimodal models, images are now part of the decision process. If your visuals lack context, they add no value to an agent’s understanding.

What to do

• Audit your product image alt text
• Replace generic labels with descriptive, attribute-rich text
• Include material, color, form factor, and use case

Example

❌ “woman wearing sweater”
✓ “Woman wearing navy blue merino wool crew neck sweater, 200gsm, fitted cut, shown in office setting”

This isn’t about accessibility alone. It’s about machine comprehension.

What difference will this make
Agents use images to validate claims, understand form, and reduce uncertainty. Poor image context makes your product harder to trust.

Action 9: Prepare for Model Context Protocol (MCP)

Priority: Medium

Search-based discovery is slow for agents. Direct access is faster. Model Context Protocol (MCP), led by Anthropic, is emerging as the standard way for AI systems to query live business data directly.

What to do

• Understand what MCP enables (real-time catalog, pricing, inventory access)
• Check whether your platform supports MCP or has it on the roadmap
• Plan for API-first data access, not page scraping
• Evaluate partners that already operate MCP servers

You don’t need to implement this today. You do need to design for it.

What difference will this make
MCP allows agents to pull live data into their reasoning layer. That means no stale pricing, no outdated inventory, no guessing. When agents can query your systems directly, traditional search becomes optional.

Action 10: Enable API-first checkout

Priority: Medium

Discovery without execution is useless. If an agent can’t complete a transaction on behalf of a user, it will recommend a brand that can.

What to do

• Audit your checkout flow
• Confirm key steps can be completed programmatically:
  • Cart creation
  • Address validation
  • Payment authorization
• Support secure delegated access (tokens, OAuth)

Explore emerging standards from players like Stripe and Google that are formalizing agent-authorized payments.

What difference will this make
Agentic commerce introduces the concept of delegated intent. A user authorizes an agent to act. Your systems need to honor that safely. If checkout requires a human click at every step, you’re not agent-ready.

After the actions, your platform decides how far you get

The actions above are table stakes. Whether you can execute them cleanly depends on your eCommerce platform.

Agentic commerce doesn’t work well with plugins, patches, or workarounds. It rewards platforms that treat data, structure, and orchestration as core infrastructure. This is where a platform stops being a storefront and starts being the system agents interact with.

AI agents don’t need more content. They need clear signals. They need structured product data, real-time access to pricing and inventory, and the ability to complete a transaction without guessing.

That means:

• Structured data needs to be native, not bolted on
• APIs need to expose live catalog and commerce data
• Checkout needs to support delegated actions, not just human clicks

If any of those break, the journey breaks.

This is why platforms like Core dna are built differently. Schema, APIs, and orchestration aren’t add-ons. They’re part of the foundation, which makes it easier to keep product data consistent, expose it cleanly to AI systems, and adapt as protocols like MCP or Google’s UCP evolve.

In an agent-driven world, flexibility matters more than feature lists.

If your platform fights clarity, agents won’t wait. They’ll move on.