Consider asking an AI agent to find “the best laptop that will work for my business at under $1,500 with good battery, fast shipping, and a good warranty”, and rather than just providing you a list of relevant results, the agent does the research, compares different products on the market, considers their features and reviews, finds the best one for you and, with your permission, even purchases it for you. This is how the concept of agentic commerce works – a quickly evolving trend in ecommerce where AI agents can go beyond the task of answering customers’ questions and start representing customers’ interests in the ecommerce environment.
This represents a radical change for business in many ways. The traditional ecommerce process has been relying on customers going to a website, browsing through categories of products, finding what they are interested in, adding it to their cart and purchasing. In an agentic commerce process, there is an additional stage where an AI system can analyze the customer’s intention, compare different products and even make purchases depending on the customer’s preferences and permissions.
The implications are significant for ecommerce brands, retailers, SaaS companies, marketplaces, payment providers, and marketers because the next important digital storefront may not always be a website. Increasingly, it may be an AI interface through which customers ask for what they need and allow an agent to find the most suitable option. Up-and-coming developments like the Agentic Commerce Protocol by OpenAI and the Universal Commerce Protocol by Google, along with emerging agentic payment features from big payment companies, prove that the infrastructure for this business model is being built right now.
As far as businesses are concerned, the important issue isn’t going to be whether their customers are going to use AI to search for their products anymore. The more relevant question is whether the AI agents are going to understand, trust, compare, and eventually select your products when they get a shopping request from your customers.
This guide describes agentic commerce, the tech and protocols powering it, product evaluation criteria of AI shopping assistants, implications of this trend for SEO and ecommerce, potential risks involved, and what businesses should do to prepare for this type of commerce.
What Is Agentic Commerce?
Agentic commerce refers to an approach to digital commerce where AI agents can do product research, product comparisons, price and policy evaluation, recommend a course of action, and, more importantly, carry out transactional actions on behalf of the consumer. Instead of the manual process of switching from Google searches to retail sites to product pages and then back again to the checkout screen, agentic commerce can achieve all these tasks through one conversation window using AI.
The difference here is that agentic commerce should not be confused with another term for AI-driven product recommendations. The recommendation engine may suggest headphones worth trying. Still, the AI shopping agent can understand your needs, research and explore options, weigh pros and cons, check availability and delivery conditions, compile a package, and even make the purchase with your permission.
According to IBM, agentic commerce refers to a kind of buying and selling where AI agents conduct research, negotiate and close sales on behalf of consumers or organizations with minimal human involvement.
That difference changes where the decision happens.
In traditional ecommerce, the retailer owns most of the shopping journey. In agentic commerce, the AI agent increasingly becomes an intermediary between the customer’s intent and the merchant’s offer.
A Simple Example of Agentic Commerce
Consider a customer saying the following:
- A conventional search engine returns pages of links.
- A traditional ecommerce site may return a product grid.
- A conversational AI may recommend three laptops.
An agentic shopping system can potentially go several steps further. It can understand the needs, search for eligible products, evaluate the specifications and prices, take into consideration the delivery and warranty terms, eliminate the products that don’t meet the needs, select the most preferable products, and even make a purchase with the customer’s permission.
This is the major difference: now the AI helps the customer shop, not only assists in the shopping process.
Why Agentic Commerce Matters in 2026?
For twenty years, “the funnel” was built for humans — search results, landing pages, product photos. Every SEO technique, every A/B tested button color, every retargeted ad was designed for a person clicking through.
Agentic commerce upends that logic: the “consumer” could now be an AI reviewing product feeds, weighing five choices against a budget, and completing the purchase without a human ever viewing the page.
The Rollout Hasn’t Been Smooth
OpenAI’s path so far:
- Instant Checkout launched with Etsy in September 2025
- Expanded initiative followed in February 2026
- OpenAI has since scaled back the original Instant Checkout approach, shifting toward helping brands build their own dedicated apps within ChatGPT
Takeaway: Agentic commerce isn’t one settled productm, it’s several companies iterating in public. The winning interaction pattern (instant in-chat purchase vs. dedicated brand apps vs. something else entirely) is still being worked out.
Google Has Moved Faster on Infrastructure
- January 2026: Google announced the Universal Commerce Protocol (UCP) — an open standard letting AI agents communicate with commerce systems. Already powers native checkout in Google AI Mode and Gemini for a limited set of eligible US merchants.
- March 2026: UCP expanded with multi-item cart support, real-time catalog access, and identity linking to preserve loyalty benefits.
- May 2026 (Google I/O): Evolved further into Universal Cart — an intelligent cart working across merchants and Google surfaces (Search, Gemini, YouTube, even Gmail), tracking price drops and restock alerts in the background. Backed by the Agent Payments Protocol (AP2), which creates tamper-proof digital mandates for a verifiable record of what the agent was authorized to do.
- Shopify’s role: Co-developed UCP and now activates it, along with native MCP servers, by default for merchants — making them discoverable across ChatGPT, Copilot, Google AI Mode, and Gemini with no extra configuration needed.
The Practical Question Is No Longer Hypothetical
It’s live, in a limited but real way:
If a shopper types “find me a waterproof jacket under $150” into Gemini or ChatGPT — which brand’s product gets surfaced, and does that brand even have a checkout path enabled?
The Actionable Half: Building the AI-Readable Shelf
This is the part worth stressing most. Having a clean product feed isn’t enough, the real work is anticipating the kinds of questions an agent will ask on a shopper’s behalf, which tend to be far more specific than a typed search query.
Why the standard feed falls short:
- Traditional Shopping feeds (title, description, GTIN, category, price) were built for keyword discovery
- Agents in AI Mode need to answer conversational questions like “is this suitable for dry, sensitive skin” or “will this fit a 6ft 2in frame with a long inseam”
- These are questions no human ever typed into a search box — but they come up naturally in conversation
Google’s response:
- Rolling out dozens of new Merchant Center data attributes built specifically for conversational commerce
- Goes beyond keywords to include answers to common product questions, compatible accessories, and substitutes
- In short: structured Q&A data, fit guides, and compatibility info are becoming ranking inputs — not just support content buried on a help page
The Risk: Invisibility, Not Just Lower Rank
Industry commentary flags a sharper danger than most brands expect:
- Brands with weak product data, poor merchant systems, or messy feed hygiene may not just rank lower
- They may be excluded from consideration entirely, because the agent simply can’t parse or trust the data well enough to include them
The Trust Layer Goes Beyond the Product Page
Agents also need information that a typical merchandising page rarely states outright:
- Specific return policy terms
- Whether the listing is the primary seller or a reseller
- Delivery guarantees and timelines
This matters because when ranking multiple merchants selling the same product, these systems weigh factors like availability, price, quality, whether the merchant is the primary seller, and whether checkout integration itself is enabled, meaning trust signals are now competitive ranking factors, not just trust-building copy.
The $3–5 Trillion Opportunity
The McKinsey figure is worth treating exactly as the original text frames it, directional, not a forecast to bank on. What’s more concrete is the infrastructure spend already happening around it: Stripe building payment rails, Google building AP2 and Universal Cart, Shopify building Agentic Storefronts, PayPal joining OpenAI’s protocol as a payment server for smaller merchants. That’s real capital committing to the plumbing regardless of whether the $3–5T figure proves accurate.
The more useful reframing for an individual business, building on the original point: the relevant question isn’t “when does the market hit its projected size” but “when do agents start meaningfully influencing purchase decisions in my category.” That will vary enormously, apparel and gifting (Etsy, Glossier, SKIMS were the early movers) is already seeing real transaction volume through these channels, while more considered or high-touch categories (furniture, B2B equipment, anything requiring in-person evaluation) will lag well behind the aggregate numbers for years.
How AI Shopping Agents Actually Work?
Agentic shopping always goes through the same general pattern that human shopping does, but the agent manages to combine many different tasks into one process.
A useful way to understand it is:
- Intent → Research → Filtering → Comparison → Decision → Authorization → Transaction → Fulfillment → Support
The customer may only see the beginning and end of that process.
Step 1: The Customer Gives the Agent an Objective
The interaction often begins with a goal rather than a product name.
For example:
- Search for a hotel for three days of business travel.
- Buy running shoes under $150.
- Order my usual coffee when the price falls below $20.
- Find a CRM that will work with my 20-person sales team.
- Purchase a new laptop charger that is compatible with my laptop.
- Purchase a birthday gift for a food lover.
This matters because agentic systems can work from constraints and preferences, not just keywords.
A customer does not necessarily need to know the exact product they want.
Step 2: The Agent Converts Intent Into Criteria
The AI translates the request into decision criteria.
For example, a request for a business laptop might become:
| Customer Requirement | Agent Criterion |
|---|---|
| Under $1,500 | Maximum price |
| Lightweight | Weight threshold |
| Business travel | Battery, durability, portability |
| 16GB RAM | Minimum specification |
| Friday delivery | Delivery deadline |
| Strong warranty | Warranty requirements |
This is where agentic shopping becomes more sophisticated than a conventional search query.
The agent is not simply looking for pages containing the words ‘laptop’ and ‘business travel’. It is attempting to understand what the customer means by the request.
Step 3: The Agent Searches Available Commerce Data
The agent can use product feeds, merchant systems, marketplaces, APIs, structured information, commerce protocols, and other available sources to identify relevant products.
The quality of this stage is highly dependent on the quality of the information provided by businesses.
If the product has obsolete pricing, unclear specifications, unavailable or contradictory shipping details, it might become hard for the agent to evaluate the product correctly.
Step 4: Comparing the Options
The agent can then evaluate products against the customer’s requirements.
This can involve:
- Price
- Product specifications
- Availability
- Delivery time
- Shipping costs
- Return policies
- Warranty
- Ratings and reviews
- Seller reputation
- Brand preferences
- Sustainability criteria
- Compatibility
- Promotions
- Total cost
This is one of the most important changes for marketers.
A product does not have to be universally considered the best.
It has to be the best match for a particular customer’s objective.
Step 5: The Agent Explains the Trade-Off
Good agentic commerce should not simply produce a winner without context.
Suppose three products qualify.
The agent might determine:
- Product A is the cheapest.
- The best battery life is exhibited by Product B.
The slight increase in cost of Product C is more than compensated by its fast delivery and better warranty.
Now the customer can make his/her decision intelligently.
Trust plays a very crucial role here.
An AI agent that tells the user why it chose that particular product is easier to trust than one which tells the customer to buy this.
Step 6: The Agent Requests or Uses Purchase Authorization
Not every purchase should happen automatically.
The level of autonomy can depend on the customer’s permissions, the transaction value, the merchant, and the type of product.
A customer might authorize an agent to automatically reorder household essentials under a certain price while requiring explicit approval for a $2,000 computer.
This creates a concept that businesses need to understand:
Agentic commerce is not necessarily human-free commerce. It is permission-based commerce.
Step 7: The Transaction Is Completed
Once authorized, the agent can communicate the required transaction information to the merchant or payment infrastructure.
For instance, the Agentic Commerce Protocol of OpenAI enables interaction between AI agents, consumers, and merchants in a transaction process where the merchant handles everything, from delivery of goods to the handling of complaints and relationships with the consumers.
This is important since businesses can utilize the current commerce framework without making changes.
Agentic Commerce vs. Traditional Ecommerce and Conversational Commerce
Agentic commerce is often confused with conversational commerce, ecommerce personalization, chatbots, and recommendation engines. They overlap, but they are not interchangeable.
| Model | Main Function | Who Takes Action? | Typical Example |
|---|---|---|---|
| Traditional ecommerce | Product discovery and purchase | Customer | Customer browses a store and checks out |
| Recommendation engine | Suggest products | Customer | You may also like this product |
| Conversational commerce | Help customers through conversation | Customer with AI assistance | Chatbot answers product questions |
| AI shopping assistant | Research and compare | Customer with AI assistance | AI recommends three suitable products |
| Agentic commerce | Research, decide, and transact within permissions | AI agent | Agent finds and purchases the best option |
| Agent-to-agent commerce | AI systems interact with one another | AI agents | Buyer agent communicates with merchant agent |
The progression is not simply from less AI to more AI.
It is a progression from human-led action to machine-assisted action and eventually machine-executed action under human-defined rules.
Agentic AI Is Broader Than Agentic Commerce
Agentic AI can perform many tasks unrelated to shopping.
An agent could:
- Schedule meetings
- Manage software workflows
- Analyze documents
- Monitor systems
- Reconcile invoices
- Manage inventory
- Detect anomalies
- Book travel
Agentic commerce applies these capabilities to buying and selling.
- Agentic Commerce Is Not Just a Chatbot
- A chatbot typically responds to an interaction.
- An agent can be designed to pursue an objective.
That distinction becomes obvious when the task involves multiple steps.
A chatbot may reply:
This order qualifies for free shipping.
An agentic system may calculate:
There are three sellers for this item. Seller B charges $8 less after factoring in shipping, while Seller C delivers one day earlier. Based on the preference you mentioned, fast delivery is better. Shall I buy it?
The Technology Powering Agentic Commerce
Agentic commerce requires more than a sophisticated language model. The AI needs a way to understand products, communicate with businesses, manage permissions, and initiate transactions safely.
Several layers are developing around this ecosystem.
Large Language Models and AI Agents
Large language models provide much of the reasoning and natural-language interaction behind modern agents.
But a model alone cannot safely complete commerce.
The agent needs access to tools, structured information, authentication, payment mechanisms, business rules, and transaction systems.
This is why agentic commerce is better understood as a system architecture rather than a single AI feature.
Product Data and Commerce APIs
The agent needs accurate product information.
This can include:
- Product name
- Description
- SKU
- Price
- Currency
- Inventory
- Variants
- Dimensions
- Materials
- Compatibility
- Shipping
- Returns
- Warranty
- Images
- Reviews
- Availability
The better structured and maintained this information is, the easier it becomes for an agent to evaluate the offer.
Agentic Commerce Protocol
OpenAI and Stripe introduced the Agentic Commerce Protocol as an open standard designed to connect buyers, AI agents, and businesses during commerce transactions. The protocol is maintained as an open specification and is currently in beta.
Its significance is not simply technical.
Protocols reduce the need for every AI platform and every merchant to build a completely different integration.
That is essential if agentic commerce is going to operate at scale.
Universal Commerce Protocol
Google has also introduced the Universal Commerce Protocol, designed to provide a standardized way for AI agents and merchants to communicate during commerce experiences across platforms. Google has connected UCP with its broader AI shopping and merchant ecosystem.
The existence of multiple protocols is worth watching because the agentic commerce ecosystem is still evolving.
Businesses should therefore avoid making the assumption that one protocol will necessarily become the only standard.
Agentic Payments
Payments represent one of the hardest parts of agentic commerce because traditional payment systems were largely designed around humans directly presenting credentials and authorizing transactions.
AI agents introduce different questions:
- Who authorized the payment?
- What exactly was the agent permitted to buy?
- What happens if the agent makes a mistake?
- How can the transaction be authenticated?
- How can fraud systems distinguish an authorized agent from a malicious bot?
- Who is liable if an agent exceeds its authority?
Visa’s 2026 work on agentic payments highlights that agents are already beginning to interact with payment systems for activities such as booking travel, purchasing compute, and reordering inventory, while also examining the protocols and infrastructure required for autonomous payments.
Identity, Permissions, and Trust
The future of agentic commerce will depend heavily on permissions.
A useful mental model is:
Identity + Intent + Permission + Context + Payment + Audit Trail
A customer’s agent should not have unlimited purchasing authority merely because the customer once added a credit card.
Instead, the ecosystem needs ways to establish what the agent can do, for whom it can do it, under which conditions, and within what limits.
How AI Agents Decide Which Products to Recommend?
This is one of the most commercially important questions in agentic commerce.
Traditional SEO often focuses on visibility for a query.
Agentic commerce introduces a different problem:
Will the AI agent consider your product a good answer to the customer’s objective?
Product Relevance
A product must first satisfy the customer’s requirements.
If one requests a waterproof hiking jacket that costs less than $200, then a $700 leather jacket can be considered a great product, but not a good answer.
Agentic discovery therefore rewards contextual relevance.
Data Completeness
AI systems cannot reliably recommend information they cannot find or interpret.
A product page that says premium quality without explaining materials, dimensions, compatibility, warranty, or use cases gives an agent less useful information than a well-structured product record.
Price and Total Value
Agents can compare prices more efficiently than humans who would otherwise open multiple tabs.
That does not necessarily mean the cheapest product wins.
An agent may consider shipping, warranty, return policies, durability, compatibility, or other constraints.
This creates an important strategic lesson:
Competing on price alone may become less sustainable when AI can instantly expose the full trade-off between competing products.
Availability and Fulfillment
If a customer needs a product tomorrow, a product that arrives next week may be irrelevant regardless of its quality.
Inventory and fulfillment data therefore become part of discovery.
Reputation and Trust
Trust signals may include:
- Reviews
- Seller history
- Return policies
- Warranty information
- Brand reputation
- Product consistency
- Transparent business practices
- Reliable fulfillment
The precise signals used by different AI systems will vary, and businesses should avoid assuming that there is one universal ranking formula.
Customer-Specific Fit
This may ultimately be the biggest differentiator.
An agent does not need to identify the product that is best for everyone.
It needs to identify the product that best fits this customer, right now.
What Agentic Commerce Means for Brands and Retailers?
One-sentence summary: Agentic commerce alters the role of the vendor from capturing the attention of a consumer to convincing the agent that a product is worth recommending — thereby altering the very definition of good product information, shopfronts, and brands.
The following are the top four implications in order of increasing operational complexity.
1. Your Product Data Becomes Your New Sales Team
This is the highest-priority shift, because it’s the foundation everything else depends on. An AI agent can’t “look” at your product the way a human shopper can — it reads structured evidence and reasons from it. If the data is thin, the agent has nothing to work with, no matter how good the product actually is.
Weak product data tells the agent almost nothing useful:
“High-quality running shoes designed for comfort.”
Strong product data gives the agent real decision-making material:
| Attribute | Why it matters to an agent |
|---|---|
| Intended running surface | Matches product to use case (road, trail, track) |
| Cushioning system | Differentiates comfort/performance tier |
| Heel-to-toe drop | Matches biomechanical needs |
| Weight | Relevant for speed vs. support trade-offs |
| Available sizes & foot width | Filters out non-fits before recommending |
| Recommended use | Confirms fit-for-purpose |
| Weather suitability | Adds contextual relevance (e.g., “for rainy climates”) |
| Materials | Signals durability, breathability, sustainability |
| Return conditions | Reduces agent’s perceived risk in recommending |
| Warranty | Builds trust into the recommendation |
| Current inventory | Prevents recommending out-of-stock items |
The takeaway: every missing or vague attribute is a missed opportunity for an agent to choose your product over a competitor’s. Treat your product feed like a briefing document for a very literal, very well-informed salesperson.
2. Your Website May No Longer Be the Only — or First — Storefront
This is the second-biggest shift because it changes where the “sale” actually happens, and therefore where merchants need to invest.
The traditional funnel: Search → Website → Product Page → Cart → Checkout
The Agentic Funnel of the Future: Customer Intent -> AI Agent -> Product Selection -> Merchant System -> Transaction
The implication: Your customer might not ever visit your homepage, your category pages, or your blog. Instead, the agent does all the searching and comparing for the customer and conducts transactions directly with your systems.
What this means operationally:
- Discovery no longer requires a site visit. Your product needs to be “findable” and evaluable by an agent independent of your website’s UX.
- Merchant systems need to be agent-ready. APIs, product feeds, and checkout/transaction endpoints need to be structured, reliable, and accessible to automated systems — not just human-friendly.
- Website UX still matters, but for a shrinking share of journeys. It remains important for brand-building, direct visits, and edge cases the agent can’t resolve — but it’s no longer the only path to a sale.
The takeaway: design your commerce infrastructure so a transaction can complete correctly even if no human ever sees your website.
3. Brand Differentiation Still Matters But It Has to Be Made Explicit
It’s tempting to assume agentic commerce flattens everything into a commodity market, where the cheapest or fastest-shipping option always wins. That’s unlikely to be universally true.
When an agent is choosing between products with similar specifications, it will still weigh:
- Brand reputation — is this a name associated with reliability?
- Warranty and guarantees — does it reduce the customer’s risk?
- Customer experience signals — support quality, return ease, delivery reliability
- Unique features — genuine differentiators, not marketing language
- Trusted reviews — third-party validation the agent can point to
However, the issue is that bots cannot understand differentiators through visually beautiful pages, images, or voice tones as humans do. The fact is that if you cannot provide all these differentiators in a structured format, then basically, it does not exist for the bot.
Summary: Do not depend solely on visual storytelling. Translate your brand’s advantages into explicit, machine-readable claims and evidence — because that’s the only language agents can evaluate a decision in.
What Does This Mean in Practice?
Priority Action: Why is it ranked here?
- Audit and enrich product data – Nothing else works without this foundation
- Make backend systems (feeds, APIs, checkout) agent-accessible – Determines whether a sale can even happen without a site visit
- Convert brand differentiators into explicit, structured claims – Determines whether you win or lose the agent’s comparison
Agentic commerce doesn’t eliminate the need to build a great product or a trustworthy brand — it changes who you’re explaining it to first. The winners will be the merchants who treat AI agents as a new, literal-minded audience that needs to be given the evidence, not just the impression, of quality.
How Agentic Commerce Changes SEO and Product Discovery?
Summary: Agentic commerce does not end SEO; it places another constraint on it. It is no longer sufficient for someone to just find me. To do business, I must now be comprehensible to the point where a machine can recommend and conduct business with me.
SEO poses one question. Agency commerce poses a second, more difficult one:
SEO Question: Can a search engine find and rank my page?
Agency Commerce Question: Is an AI system capable of understanding my offer well enough to make recommendations or conduct transactions with it?
The following are the three most important shifts that SEO practices will undergo due to agency commerce, arranged from most to least fundamental change.
1. From Keyword Optimization to Entity/Product Understanding
It is the most significant mind shift of all because it actually shifts the object being optimized. Traditional SEO optimization occurs at the level of pages. Agentic commerce optimizes a product to be understood as a specific, comparable entity.
A traditional product page might be built around ranking for a phrase like:
“best office chair”
An agent doesn’t just need a page that ranks for that phrase — it needs enough structured information to reason about whether this chair is actually right for a specific person’s needs. That means answering questions the keyword never addressed:
- What type of chair is it (task, ergonomic, executive)?
- Who is it designed for?
- What height range does it support?
- What weight capacity does it have?
- Does it include lumbar support and is it adjustable?
- What is the warranty and return policy?
- Is assembly required?
- What is the time frame for delivery?
- How is it different from other similar chairs?
This is semantic commerce, and the goal is not just to use more keywords but to build a machine-readable, valid representation of the product that the AI can think about. You might be ranked #1 on Google for “best office chair”, but that doesn’t mean it meets your agent’s criteria because it can’t verify claims regarding maximum weight or lumbar support.
In summary, the message is clear: Do not try to figure out which keyword the page should rank for. Ask yourself instead, what information does artificial intelligence need on that particular product?
2. Structured Data becomes Infrastructure, Not an Add-On
This is ranked second because it’s the mechanism that makes shift #1 actually usable by machines without it, even excellent product information stays locked in unstructured text an agent has to guess at.
Businesses should maintain accurate, current structured data wherever relevant, including:
- Schema type – What it communicates to an agent
- Product – Core identity, specs, category
- Offer – Price, currency, conditions of sale
- Availability – In-stock status, fulfillment options
- Pricing Current and comparative pricing signals
- Review – Trust and satisfaction signals
- Organization – Brand legitimacy and identity
A few important caveats worth being explicit about:
- Structured data doesn’t guarantee selection. It’s not a ranking hack or a guaranteed recommendation slot — an agent can still choose a competitor with better specs, price, or trust signals.
- It does make information explicit and verifiable. This eliminates any ambiguity, and thus decreases the possibility that the agent would ignore your product simply because of its inability to verify one important point. Incomplete or stale structured data can be worse than none — it can actively mislead an agent’s evaluation.
The takeaway: treat structured data as core commerce infrastructure — as important as your checkout flow — not as a technical SEO checkbox.
3. Generative Engine Optimization (GEO) Now Has to Extend to “Action Eligibility”
This is ranked third because it’s the newest and least settled discipline — but it’s where the other two shifts converge into a single practical goal.
Generative Engine Optimization (GEO) is the emerging practice of improving how brands and information are represented within generative AI systems — essentially, SEO’s answer to AI-generated answers rather than ranked links.
Agentic commerce adds a layer on top of GEO. It’s no longer enough for a brand to be mentioned accurately in an AI-generated answer. The brand also needs to be eligible for action — meaning an agent could plausibly complete a transaction on the brand’s behalf, not just cite it.
That requires several things working together simultaneously, not in isolation:
- Discoverability — the agent can find the product/offer at all
- Factual accuracy — what’s found matches reality
- Product data depth — enough detail to support a real decision
- Transaction readiness — backend systems can actually complete a purchase
- Trust signals — reviews, warranty, return policy reduce perceived risk
If any one of these is missing, a brand can be “visible” in an AI system without ever being actionable — mentioned, but never chosen.
The takeaway: GEO for agentic commerce isn’t just about earning a mention — it’s about earning the right to be transacted with.
Summary: What to Prioritize?
Priority Shift Core question to ask
- Entity & product understanding – “Would an AI understand this product well enough to recommend it?”
- Structured data as infrastructure – “Is our product information explicit, current, and machine-readable?”
- GEO + action eligibility – “Could an agent not just mention us, but actually transact with us?”
SEO isn’t disappearing, it’s gaining a second layer of scrutiny. Ranking well still matters, but in agentic commerce, ranking is now just step one. The real competition happens after that, in whether an AI system trusts your product enough to act on it.
What Can Be Done by Businesses to Facilitate Agentic Commerce?
Companies do not have to reconstruct their complete e-commerce framework immediately.
A better way to go is to build your infrastructure first and try out the agentic commerce experience where it makes business sense.
Step 1: Check Out Your Product Data
Begin with the data that the AI will require to make its decision confidently.
Check:
- Product titles
- Descriptions
- Attributes
- Variants
- Pricing
- Inventory
- Shipping
- Returns
- Warranty
- Compatibility
- Images
- Reviews
- Merchant information
Look for contradictions across your website, feeds, marketplaces, and other channels.
Step 2: Identify High-Intent Shopping Scenarios
Do not start by asking:
How do we implement agentic commerce?
Start with:
Where would an agent genuinely make shopping easier for our customers?
Good candidates include products that are:
- Frequently compared
- Technically complex
- Reordered regularly
- Highly personalized
- Time-sensitive
- Available across multiple sellers
- Difficult to select without expert guidance
Step 3: Define Your Agentic Commerce Use Cases
Create a simple matrix:
| Use Case | Customer Value | Business Value | Complexity |
|---|---|---|---|
| Product recommendations | High | High | Low |
| Product comparison | High | High | Low |
| Automated reordering | Very high | High | Medium |
| Personalized bundles | High | High | Medium |
| Autonomous purchasing | Very high | Very high | High |
| Agent-to-agent negotiation | Emerging | Potentially high | Very high |
Step 4: Make Your Commerce Infrastructure Agent-Ready
Your backend should be able to answer basic questions reliably.
For example:
- Is this product available?
- What does it cost right now?
- When can it ship?
- Which variant is available?
- Can it be returned?
- What if the customer decides to cancel?
If your systems can’t provide answers to these questions consistently, the introduction of an AI layer won’t help you solve your problems.
On the contrary, it may even obscure them further.
Step 5: Review Your Payment Infrastructure
Businesses should monitor emerging support for agentic transactions from payment providers and commerce platforms.
PayPal’s Agent Ready offering, for example, supports both ACP for ChatGPT and UCP for Google AI surfaces through Braintree, illustrating how payment infrastructure is adapting to multiple agentic commerce standards.
The right implementation would vary depending on the merchant’s platform, geographic location, payment stack, type of goods being sold, and risk management criteria.
Step 6: Set Permission and Risk Criteria
Set boundaries for what an AI agent is allowed to do.
For instance:
Low-risk actions
- Search
- Compare
- Recommend
- Add to cart
- Check availability
Medium-risk actions
- Apply discounts
- Create orders
- Reorder products
- Modify subscriptions
High-risk actions
- Complete expensive purchases
- Buy regulated products
- Make irreversible decisions
- Change payment information
This creates a practical autonomy ladder.
Wrapping It Up With A Practical Agent Readiness Framework
A useful way to evaluate a business is through five layers:
Layer 1: Discoverability
Can AI systems find your products and understand what you sell?
Layer 2: Interpretability
Can an AI accurately determine who your product is for and when it should be recommended?
Layer 3: Comparability
Can your product be compared fairly against alternatives because pricing, specifications, availability, policies, and differentiators are clear?
Layer 4: Transactability
Can an authorized agent actually initiate or complete the purchase using supported commerce and payment infrastructure?
Layer 5: Trustability
Can the customer and agent trust the merchant to fulfill the transaction correctly?
A business that scores poorly at Layer 1 should not spend heavily on Layer 4.
This is one of the most practical lessons for companies entering agentic commerce:
Frequently Asked Questions
What is agentic commerce?
Agentic commerce represents a type of commerce wherein AI agents could act on behalf of the customer or company to conduct market research on products, comparison shopping, decision-making within certain parameters, and ultimately complete a transaction. In contrast with traditional chatbots, agents can do complex tasks, not just answer queries.
How is agentic commerce different from ecommerce?
Traditional ecommerce generally requires the customer to search, compare, select, and purchase products manually. Agentic commerce transfers some or all of those activities to an AI agent acting according to the customer’s instructions and permissions.
What is an AI shopping agent?
An AI shopping agent is defined as a technology which enables shopping either on its own or helps a consumer buy a product by analyzing demands, locating products, making comparisons, verifying conditions, and even purchasing.
Can AI shopping agents purchase products without human approval?
They can be designed to perform transactions with varying levels of autonomy, but whether a purchase happens automatically depends on the permissions, platform, merchant, payment system, product category, and transaction rules involved.
What is the Agentic Commerce Protocol?
The Agentic Commerce Protocol (ACP) is an open protocol invented by OpenAI and Stripe that enables commerce interactions among buyers, agents, and businesses. At present, it is used in the underlying architecture for experiences like Instant Checkout in ChatGPT.
What is the Universal Commerce Protocol?
Universal Commerce Protocol, or UCP, is an open commerce protocol introduced by Google and partners to support agentic commerce interactions between AI systems and businesses. This move is part of Google’s larger strategy of linking AI-based shopping experiences to merchant infrastructure.
Does agentic commerce replace SEO?
No. SEO still plays an important role in terms of discovery, authority, and visibility. However, with agentic commerce, the scope of the optimization challenge gets even wider, since businesses will require structured information about their products that is understandable for AI algorithms used to help customers in decision-making.
Will AI agents always select the cheapest product?
Certainly not. A well-functioning shopping agent must compare various products in accordance with the client’s needs and requirements, which may consist of such factors as quality, delivery time, warranty, compatibility, reviews, price, and other aspects.
How should ecommerce businesses prepare for agentic commerce?
Start with product data quality, inventory accuracy, structured information, clear policies, reliable APIs and backend systems, payment readiness, and realistic testing across AI shopping experiences. Businesses should identify valuable use cases before attempting full autonomous purchasing.
Is agentic commerce safe?
This process can be made more secure by using authentication, permissions, transaction limits, fraud prevention measures, merchant policies, audit logs, and human intervention for high-risk transactions. The technology involved is still new and changing, so careful consideration should be taken in relation to security and regulations.
