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Agentic Commerce in 2026: How AI Agents Are Changing Online Shopping
What if AI could do more than recommend products and actually help you complete a purchase? That's the idea behind agentic commerce, where AI agents can search for products, compare options, evaluate prices, and carry out shopping tasks on behalf of customers, with their permission.
As AI becomes more involved in online shopping, businesses are facing a new question: how do you sell to customers when an AI agent may be part of the buying journey? This guide explores how agentic commerce works, the different types of AI agents, real-world examples in 2026, and what ecommerce businesses can do to prepare for this shift.
What Is Agentic Commerce?
An AI shopping agent helps customers compare products, check prices and delivery options, and prepare purchases for approval.
Agentic commerce is a form of ecommerce where AI agents can take action on behalf of shoppers, from discovering and comparing products to managing purchases and certain post-purchase tasks. Unlike traditional shopping assistants that primarily answer questions or suggest products, agentic AI can carry out multiple steps toward a shopping goal, within the permissions and capabilities available to it.
Agentic commerce changes who performs those tasks. A shopper could ask an AI assistant to find a lightweight laptop under $1,000, compare suitable models, check delivery options, and prepare a purchase for approval. Rather than navigating every product page manually, the shopper can delegate parts of the journey to an AI agent.
The shift toward AI-assisted shopping is already visible in consumer behavior. Recent research offers a few useful signals:
- 693.4% growth in AI-driven retail traffic: According to Adobe Analytics, traffic from generative AI tools to U.S. retail websites increased 693.4% year over year during the 2025 holiday season. Adobe notes that the growth rate looks dramatic partly because the starting base is still small: the equivalent figure a year earlier was roughly 1,300%.
- 47.6% use AI to compare products: In Bloomreach's 2026 consumer research, 47.6% of surveyed consumers reported using AI to compare product features or prices, up from 35% in 2025.
- 41.4% would prefer shopping through AI: The same Bloomreach research found that 41.4% of respondents would choose an AI shopping tool over a brand's website if they had to choose between the two.
The two Bloomreach figures come from research commissioned by a commerce software vendor, so treat them as a directional signal rather than an independent measurement. More broadly, these numbers show growing interest in AI-assisted product discovery, although they do not mean that the same proportion of purchases are already completed autonomously by AI agents.
To understand what changes for shoppers, consider the differences between traditional ecommerce and agentic commerce:
| Comparison | Traditional Ecommerce | Agentic Commerce |
|---|---|---|
| Product discovery | Shoppers browse websites, search results, and categories. | AI agents search supported catalogs and sources based on shopper requests. |
| Product comparison | Shoppers manually compare prices, specifications, and reviews. | Agents evaluate options against preferences and purchase requirements. |
| Decision-making | Shoppers research and select products themselves. | Agents can recommend or select options within defined instructions. |
| Checkout | Shoppers enter details and complete payment. | Agents may prepare or complete authorized transactions where supported. |
| Post-purchase | Shoppers manage orders and support requests. | Agents may assist with tracking, reordering, or service tasks. |
The main difference is how much of the shopping process the customer can delegate. Traditional ecommerce provides the tools for shoppers to make decisions themselves. Agentic commerce allows AI to perform some of that work, while the customer retains control over permissions and important purchase decisions.
How Does Agentic Commerce Work?
Agentic commerce begins with a shopping goal rather than a series of searches and clicks. Instead of browsing multiple stores, shoppers can describe what they need and allow an AI agent to handle parts of the process, from researching products to preparing a purchase.
The process generally follows five steps, although the actions an agent can complete depend on its capabilities, connected systems, and the permissions granted by the shopper.
Step 1. Understand the Shopper's Request
The process starts when a shopper gives an AI agent a goal, often in natural language. For example: "Find me a waterproof backpack under $150 that fits a 16-inch laptop and can arrive before next Friday."
The agent breaks this request into specific requirements: product type, weight, price, laptop compatibility, and delivery deadline. If important information is missing, it may ask follow-up questions before continuing.
Unlike a basic keyword search, the agent can use the shopper's preferences and constraints to guide the entire shopping task, rather than treating each search as a separate interaction.
Step 2. Find and Compare Relevant Products
Once the requirements are clear, the agent searches available product catalogs, merchant websites, feeds, or connected shopping services.
It looks for products that meet the shopper's criteria and gathers information needed for comparison, including:
- Product specifications and variants
- Current prices and availability
- Shipping costs and delivery estimates
- Customer reviews and ratings
- Return policies and seller information
For the backpack example, the agent might identify several models within the budget, then exclude those that cannot accommodate a 16-inch laptop or meet the requested delivery date.
This stage depends heavily on the quality and accessibility of merchant data. If product specifications are incomplete or availability is outdated, the agent may struggle to identify suitable options.
Step 3. Evaluate Options and Make Recommendations
Finding matching products is only part of the job. The agent also needs to assess which options best satisfy the shopper's priorities.
For instance, one backpack may be lighter but offer less storage, while another has better laptop protection but costs more. The agent can compare these trade-offs and present a shortlist with explanations.
It may also ask the shopper to clarify their preferences, such as whether weight matters more than capacity.
The goal is to help shoppers make a decision based on their requirements, rather than simply displaying a list of products matching a keyword.
However, recommendations are not guaranteed to be complete or accurate. Shoppers may still need to verify important details before purchasing.
Step 4. Prepare and Complete Authorized Purchases
After the shopper selects a product, the agent may help move the purchase forward.
Depending on the platform and supported integrations, it can:
- Confirm product variants and quantities.
- Check current price and availability.
- Prepare a shopping cart.
- Retrieve available shipping options.
- Direct the shopper to checkout or initiate supported payment steps.
In more advanced agentic commerce systems, an AI agent may complete certain transaction steps on the customer's behalf after receiving appropriate authorization.
Not every AI shopping assistant can complete checkout. Some only recommend products and redirect customers to the merchant's website. Transaction capabilities depend on the commerce platform, payment integrations, and available permissions.
Step 5. Support Post-Purchase Activities
Agentic commerce can also extend beyond the initial transaction.
Where supported, an AI agent may help customers check order status, find delivery information, request assistance, or prepare a repeat purchase.
For example, after buying the backpack, the shopper might ask: "Has my order shipped, and when should it arrive?"
If the agent has authorized access to the order information, it can retrieve the relevant details without requiring the customer to navigate the merchant's support pages.
More advanced workflows may support returns, exchanges, or reordering, but these actions require appropriate merchant integrations and customer authorization.
This is where agentic commerce begins to connect shopping with customer service. The same AI experience that helps someone choose a product may also assist with certain tasks after the purchase.
The Protocol Layer: UCP and ACP
The shopping process may look simple from the customer's perspective, but completing a purchase through an AI agent requires several systems to communicate. The agent needs accurate product information, the merchant needs to confirm prices and inventory, and the payment provider must process the transaction securely.
That's where agentic commerce protocols come in. They establish shared rules for how AI agents, ecommerce platforms, merchants, and payment providers exchange information and carry out transactions.
UCP and ACP connect AI shopping agents with product catalogs, checkout, payments, and order management systems.
Two important developments in 2026 are the Universal Commerce Protocol (UCP) and the Agentic Commerce Protocol (ACP).
| Protocol | Developed By | Primary Purpose |
|---|---|---|
| Universal Commerce Protocol (UCP) | Google and Shopify, with industry partners | Standardize commerce interactions between AI agents, merchants, and payment providers, including checkout and order-related operations |
| Agentic Commerce Protocol (ACP) | OpenAI and Stripe | Connect merchants with AI shopping experiences through structured product information and supported checkout workflows |
Universal Commerce Protocol (UCP)
Announced on January 11, 2026 at the NRF Big Show, UCP provides a common framework for AI agents to interact with commerce systems. It supports capabilities such as checkout, order management, and post-purchase operations, while allowing businesses to retain their existing commerce infrastructure.
Google and Shopify developed the protocol alongside retailers including Etsy, Wayfair, Target, and Walmart, and it has been endorsed by more than 20 organizations, among them Visa, Mastercard, and Stripe. One detail matters for merchants in particular: under UCP, the merchant remains the merchant of record on every transaction and keeps ownership of the customer relationship.
UCP also supports integration through existing technologies, including APIs, Agent2Agent (A2A), and Model Context Protocol (MCP).
Agentic Commerce Protocol (ACP)
ACP focuses on connecting merchants with AI-powered shopping experiences such as ChatGPT. It provides specifications for sharing product information and supporting checkout interactions while keeping core commerce operations with the merchant.
Under its checkout specification, an AI shopping experience can create and update a checkout session, receive current prices and shipping options, and submit an authorized purchase. The merchant remains responsible for validating the order, processing payment through its existing payment provider, and managing fulfillment.
As these protocols develop, they can make it easier for AI agents to work across different commerce platforms without requiring every merchant to build an entirely new shopping infrastructure.
Learn more: How To Integrate AI into a Website To Boost Performance and Sales
Types of Agentic Commerce
Agentic commerce goes beyond AI assistants helping people shop. AI agents can also work behind the scenes, supporting ecommerce operations, business purchasing, and ongoing customer relationships.
The main distinction is who the AI agent represents and what responsibilities it can handle. Based on these roles, agentic commerce can be divided into four broad types.
1. Consumer-Facing Shopping Agents
Consumer-facing shopping agents act on behalf of individual shoppers. They interpret customer requests, research available products, compare options, and help move purchases forward.
Their capabilities may include product discovery, price comparisons, availability checks, and checkout assistance. Some agents only provide recommendations, while others can initiate transactions through supported integrations.
The level of independence depends on the platform, the shopper's instructions, and the permissions granted to the agent.
2. Merchant-Side AI Agents
Merchant-side agents operate within a retailer's business systems rather than acting directly on behalf of shoppers.
They can support activities such as:
- Product merchandising and catalog management
- Customer segmentation and personalization
- Marketing content and campaign production
- Inventory monitoring and operational decisions
- Customer service and post-purchase workflows
Unlike fixed-rule automation, agentic systems can interpret information, plan multiple steps, and use connected tools to complete supported tasks.
However, the level of autonomy varies. Some agents execute approved workflows, while others require human review before making customer-facing changes.
3. B2B Procurement Agents
B2B procurement agents help businesses manage purchasing activities involving suppliers, contracts, and larger orders.
They can assist with supplier discovery, quotation comparisons, purchase order preparation, inventory requirements, and delivery coordination.
These agents must often account for more complex purchasing criteria than consumer shopping assistants, including supplier compliance, negotiated pricing, contractual terms, and internal approval procedures.
For high-value or regulated purchases, human authorization remains an important part of the process.
4. Subscription and Service Management Agents
Subscription and service management agents focus on ongoing customer relationships rather than individual purchases.
Depending on their integrations, they may monitor renewals, compare service plans, recommend changes, support cancellations, or initiate repeat orders.
This type of agentic commerce is particularly relevant to subscription businesses and services where customers regularly reassess their needs, spending, and available options.
Together, these categories show how agentic commerce can operate on both sides of a transaction: helping customers make purchases while supporting the businesses responsible for selling, delivering, and maintaining those products or services.
Examples of Agentic Commerce in 2026
Agentic commerce is already taking shape across AI shopping platforms, marketplaces, and merchant operations. However, the examples differ in how much work AI can perform independently. Some systems focus on product discovery, others connect AI to checkout, and some help businesses carry out internal ecommerce tasks.
The following examples show how these capabilities are being applied in 2026.
1. ChatGPT Shopping: AI-Assisted Product Discovery and Checkout
ChatGPT helps shoppers discover products, compare options, and complete purchases through supported AI shopping integrations. Source: OpenAI.
ChatGPT allows shoppers to describe what they need, compare products, and refine their choices through natural-language conversations.
Instead of searching multiple websites individually, shoppers can provide requirements such as budget, product features, and intended use. ChatGPT can help research suitable options and explain their differences.
OpenAI has also developed the Agentic Commerce Protocol (ACP) with Stripe to support commerce integrations, including checkout workflows for participating merchants.
This brings product discovery and purchasing closer together, although direct checkout availability depends on supported merchants and integrations.
What makes it agentic: AI can carry out multiple product research and comparison steps, with supported integrations extending the experience toward authorized transactions.
Learn more: How to Use ChatGPT for Ecommerce: Transform Your Shopify Store with AI
2. Google AI Mode and Gemini: Connecting AI Shopping With Commerce Systems
Google AI Mode and Gemini support conversational shopping, connecting product discovery with commerce systems through UCP. Source: Google.
Google is developing shopping experiences where users can describe their needs, compare products, and continue refining their choices through AI conversations.
An important part of this development is the Universal Commerce Protocol (UCP), introduced by Google and Shopify with industry partners in 2026.
UCP provides a common framework for connecting AI agents with merchant systems, allowing supported shopping experiences to communicate with product catalogs, checkout services, and order-management infrastructure.
What makes it agentic: AI shopping experiences can move beyond product recommendations and interact with commerce systems to support transaction-related actions.
These capabilities remain dependent on platform availability, merchant participation, and supported integrations.
3. Amazon Rufus: AI Shopping Assistance Within a Marketplace
Amazon Rufus helps shoppers discover products, compare options, and receive personalized recommendations within Amazon's marketplace. Source: Amazon.
Amazon's Rufus brings conversational product discovery into its retail marketplace.
Shoppers can ask product questions, compare available options, and explore recommendations without relying entirely on traditional search queries and filters.
Because Rufus operates within Amazon's shopping environment, it can draw on relevant product listings and shopping context to help customers evaluate their options.
Amazon has also been expanding its agentic shopping capabilities, although individual features vary in availability and the actions they support.
What makes it agentic: Rufus demonstrates how AI can support multi-step product research and shopping decisions within an existing retail ecosystem.
Conversational recommendations should not be confused with fully autonomous checkout, which requires additional transaction capabilities.
4. Bloomreach: Conversational Product Discovery for Ecommerce Brands
Bloomreach demonstrates how merchant-side AI can improve product discovery and support buying decisions directly within an ecommerce store.
Through its Loomi AI ecosystem, Bloomreach provides capabilities for conversational shopping, AI-powered search, merchandising, and personalization.
Rather than requiring shoppers to translate their needs into exact keywords and filters, conversational shopping systems can interpret natural-language requests and guide customers toward relevant products.
Bloomreach reports that when TFG's Bash ecommerce platform tested its conversational shopping agent during Black Friday, customers interacting with the agent showed:
- 35.2% higher conversion rate
- 39.8% higher revenue per visit
- 28.1% lower exit rate
These are vendor-reported results from a specific implementation, not general performance benchmarks for agentic commerce.
What makes it agentic: The system supports contextual product discovery and guided shopping using connected commerce information, although the reported results do not establish autonomous purchasing.
5. GemPages MCP: AI Agents Supporting Ecommerce Content and Page Creation
Agentic commerce also involves the work merchants do before shoppers arrive. Product research, campaign content, and landing page production often require teams to move information between multiple tools before anything is ready to publish.
For Shopify merchants, GemPages offers an example of how connected AI agents can support this production process through GemPages MCP.
GemPages MCP connects AI agents with Shopify and GemPages to support product research, content creation, and editable landing page generation.
GemPages MCP connects compatible AI environments with Shopify and GemPages, allowing AI agents to access relevant product context and carry out specialized research and page-building workflows.
Its three core capabilities include:
- Content Research: Analyze product information, competitor positioning, customer reviews, pain points, objections, and buying motivations to develop content directions.
- Content to HTML: Turn finished advertorials, listicles, or comparison articles into structured HTML landing pages using suitable templates.
- AI Page Generation: Create editable GemPages pages from HTML, image, or URL references, allowing merchants to review and customize the result before publishing.
These capabilities can work together or independently, depending on the team's starting point.
For instance, a merchant preparing a new product campaign can use an AI agent to research the product and its target customers, develop suitable content, generate an HTML landing page, and bring it into GemPages for further editing. Teams that prefer to start from a proven layout can also begin with a design from the GemPages template library and let the agent fill in the campaign content.
What makes it agentic: The AI agent can use connected tools to perform multiple research and page-production tasks rather than stopping at a written recommendation or draft.
This is an example of merchant-side agentic workflows, not autonomous consumer purchasing. GemPages MCP does not itself provide agentic checkout or guarantee that products will appear in external AI shopping recommendations.
6. Fin for Ecommerce: AI Assistance Across Shopping and Customer Support
Fin for Ecommerce illustrates how AI agents can support customers before and after a purchase.
Its customer-facing capabilities include helping shoppers discover products, answer purchasing questions, compare options, and handle supported post-purchase requests.
The important difference from a basic FAQ chatbot is the ability to use connected store information and tools to carry out supported actions, rather than simply returning answers from help articles.
This can help businesses connect product guidance with customer service activities such as order inquiries and other supported workflows.
What makes it agentic: Fin can use connected commerce and support systems to assist with shopping decisions and perform authorized customer-service tasks.
These examples illustrate several levels of agentic commerce, from customer-facing product discovery to merchant-side operations and connected customer service.
How to Prepare Your Store for Agentic Commerce
As AI agents become more involved in shopping, ecommerce businesses need to think beyond how their websites look to human visitors. An AI agent may need to find a product, understand its specifications, compare prices, check availability, or retrieve order information before it can help a customer.
Preparing for agentic commerce doesn't necessarily mean rebuilding your store or adopting every new AI tool. A better starting point is making sure your product information, shopping systems, and customer experience can support these interactions.
1. Keep Your Product Data Accurate and Complete
AI agents depend on product information to determine whether an item matches a shopper's request. Missing attributes, outdated prices, or inconsistent descriptions can make that evaluation difficult.
Start by reviewing:
- Product details: Clear names, descriptions, categories, specifications, and variants.
- Pricing and availability: Current prices, currencies, discounts, and stock status.
- Product identifiers: Brand, model, GTIN, or MPN where applicable.
- Purchase information: Shipping options, delivery estimates, returns, and warranties.
For example, if a shopper asks an agent to find a waterproof backpack under $150 that fits a 16-inch laptop, the agent needs those specific attributes to assess whether your product qualifies.
Keep information consistent across your website, product feeds, marketplaces, and other sales channels. Better product data supports both AI-assisted discovery and the shopping experience customers receive afterward.
2. Make Your Products Accessible to AI Shopping Systems
Having complete product information is useful only if the relevant AI systems can access it.
Depending on your ecommerce platform and target channels, review:
- Product feeds and catalog integrations.
- Crawlability and indexing settings.
- Product structured data, including relevant Product and Offer properties.
- Merchant eligibility for supported AI shopping channels.
- Consistency between product pages and connected commerce systems.
AI shopping platforms may use different discovery methods, including merchant feeds, search indexes, and direct integrations. There is no single setting that guarantees visibility across all of them.
For businesses exploring agentic checkout, it is also worth reviewing emerging standards such as UCP and ACP to understand which platforms and transaction capabilities are currently supported.
3. Improve Product Content and Customer Trust
Accurate specifications help an agent identify a product, but shoppers often need more information before making a decision.
Product pages should answer common buying questions, including:
- Who is this product suitable for?
- How does it compare with similar options?
- What are its limitations?
- Which size or variant should the customer choose?
- What do verified customers say about it?
- What happens if the customer needs a return or exchange?
Clear product descriptions, comparison tables, FAQs, authentic reviews, and useful images can help customers evaluate a product after discovering it through AI.
For merchants, this also means reviewing content regularly. Outdated claims, inconsistent product details, and unsupported testimonials can undermine trust regardless of how the customer discovered the store.
4. Review Your Checkout and Customer Service Integrations
Product discovery is only one part of agentic commerce. Agents that assist with purchases or post-purchase requests also need access to reliable commerce systems.
Review whether your current infrastructure can support:
- Checkout: Accurate cart totals, taxes, shipping charges, and available payment methods.
- Authorization: Clear customer approval before purchases or account changes.
- Order management: Reliable order status, fulfillment, and tracking information.
- Customer support: Access to relevant policies and permitted order-related actions.
- Security: Appropriate authentication, data permissions, and protection of customer information.
Not every business needs autonomous checkout immediately. For many merchants, improving order information and customer service integrations may be a more practical first step.
AI agents should only perform actions that their connected systems and customer permissions allow.
5. Test AI Shopping Experiences and Measure Results
Once the foundations are in place, test how AI shopping systems interpret and present your products.
Start with realistic customer questions rather than searching only for your brand name. Check whether AI systems identify the right products, describe their features accurately, and provide useful purchase information.
Then review the results across three areas:
- Visibility: Are your products appearing for relevant shopping requests?
- Accuracy: Are product details, pricing, and availability represented correctly?
- Business performance: Are AI-originated visitors or supported transactions contributing to purchases and revenue?
Compare results over time and investigate recurring issues. If AI systems repeatedly overlook an important product attribute, for example, check whether that information is missing from your catalog, feed, or product page.
The goal is to make your store easier for AI systems to understand and interact with while maintaining a reliable buying experience for customers.
Conclusion
Agentic commerce is changing how online shopping works, giving AI agents a more active role in product discovery, purchasing, and customer support. While fully autonomous shopping is not yet available everywhere, businesses are already finding ways to use AI agents across the commerce journey.
For ecommerce businesses, the priority is to build a reliable foundation: accurate product data, accessible information, secure transactions, and connected customer service systems. These improvements are valuable today and can help businesses adapt as agentic commerce continues to develop. For more guides on preparing your store for AI-driven shopping, browse the GemPages blog.
FAQs about Agentic Commerce
