Agentic AI Commerce on Adobe Commerce: What Merchants Should Do Right Now

A practical guide to preparing your Adobe Commerce catalog and storefront for agentic AI commerce — the next generation of AI-powered product discovery and automated buying.

In February 2026, Adobe made a clear public commitment: Adobe Commerce will support open agentic commerce standards, making merchant catalogs, pricing, and inventory machine-readable for AI agents across platforms like ChatGPT, Gemini, and others.

That announcement was not a product release. It was a signal about where commerce is heading — and a window of time that will close faster than most merchants expect.

If you run Adobe Commerce today, you have an advantage. The platform already ships with AI-powered tools that most teams have only partially activated. The gap between “we installed Product Recommendations” and “our catalog is ready for an AI agent to shop on behalf of a buyer” is significant, but it is bridgeable.

This is a practical guide to what you can do right now.

What agentic AI commerce actually means

Agentic commerce is the shift from human-clicks-through-menus to AI-navigates-the-catalog. Instead of a buyer browsing your site, an AI agent — embedded in a chatbot, a voice assistant, or a procurement workflow — queries your catalog, compares options, applies business rules, and either recommends or completes a purchase.

The buyer might never visit your storefront. The agent becomes the customer.

This is not hypothetical. Adobe’s own 2026 Digital Trends report found that nearly half of consumers would use AI for personalized product recommendations, and 44% would rely on it for instant customer service. On the B2B side, procurement teams are already piloting AI-assisted purchasing to reduce cycle times and enforce compliance with negotiated contracts.

The merchants who will win this transition are the ones whose catalogs speak the language these agents understand — structured data, clear attributes, reliable pricing signals, and consistent inventory visibility.

The three layers of agentic readiness

Preparing for agentic commerce is not a single project. It is a stack of improvements that compound.

Layer 1: Structured, enriched catalog data

AI agents do not browse like humans. They parse structured data — schema.org markup, product attributes, category hierarchies, and specifications. If your catalog has incomplete attributes, inconsistent naming conventions, or relies on images to convey specifications that should be in fields, agents will struggle to surface your products.

Adobe Commerce’s SaaS Catalog Service is the foundation here. It indexes your catalog data and makes it accessible through Adobe’s cloud infrastructure — the same infrastructure that powers Product Recommendations and Live Search. When Adobe rolls out agentic access standards, this indexed catalog is the data surface that agents will query.

What to do now:

• Audit your product attribute completeness. Focus on the attributes that drive comparison decisions: dimensions, materials, compatibility, certifications, lead times.
• Standardize attribute values. If one product says “stainless steel” and another says “SS304,” an agent may not connect them.
• Ensure your schema.org markup is present and accurate on every product detail page. This is the web’s shared vocabulary for product data, and it is what crawlers and agents rely on.
• Activate Adobe Commerce’s Product Recommendations service if you have not already. The behavioral data it collects — what gets clicked, what gets added to cart, what converts — feeds the same intelligence layer that agentic systems will draw on.

Layer 2: AI-powered search and discovery

Adobe Commerce’s Live Search, powered by Adobe Sensei, already uses machine learning to rank search results based on shopper behavior. Most merchants treat it as a search box upgrade. It is more than that — it is a proof-of-concept for how your catalog responds to intelligent queries.

When an AI agent queries your catalog, it will behave like a very sophisticated search. It will ask contextual questions: “Show me hydraulic fittings rated for 3000 PSI that are compatible with Parker’s 43 series.” If your search relevance model cannot handle that today, your agentic readiness has a gap.

What to do now:

• Test your Live Search with long-tail, specification-heavy queries that mirror how an AI agent or a knowledgeable buyer would ask.
• Use Adobe Commerce’s search merchandising rules to boost products that are most competitive and most complete in their attribute data.
• Review search analytics for zero-result queries. Every zero-result query is a catalog gap that will also trip up an agent.
• If you are on Magento Open Source without Live Search, consider that the shift toward agentic commerce makes cloud-native search services a near-term investment, not a nice-to-have.

Layer 3: Business logic exposure

This is the layer most merchants have not started on, and it is where the real differentiation will happen.

An AI agent does not just need your catalog. It needs your business logic: volume pricing tiers, customer-specific negotiated rates, shipping rules, approval workflows, and account-level permissions. In Adobe Commerce’s B2B feature set, this logic already exists — company accounts, shared catalogs, requisition lists, quote workflows.

The question is whether that logic is accessible to an external agent in a structured way, or whether it only works through the storefront UI.

What to do now:

• Map which of your B2B rules are enforced through the UI only versus through API-accessible logic. Shared catalog pricing, for example, is API-accessible. Custom approval workflows may require API extensions.
• If you use Adobe Commerce’s company account structure, document the permission model. An agent acting on behalf of a buyer needs to know what that buyer is authorized to purchase and at what price.
• Evaluate your GraphQL API coverage. Adobe Commerce’s GraphQL API is the natural interface for agentic integrations. If your custom pricing rules, shipping calculations, or inventory checks are not exposed through GraphQL, plan the work to extend the schema.

The Hyvä connection

If you are running Hyvä on Adobe Commerce, you have a structural advantage for agentic readiness. Hyvä’s lean frontend architecture means faster page loads, cleaner HTML, and more consistent structured data output — all of which make your storefront easier for crawlers and agents to parse.

But Hyvä is also a philosophy: strip away complexity, keep what matters, and make performance a first-class concern. That philosophy applies to agentic preparation. The merchants who will be easiest for AI agents to work with are the ones with clean, fast, well-structured catalogs — not the ones with the most elaborate frontend animations.

We have seen teams spend months on personalization overlays while their product pages lack basic specification tables in machine-readable format. Agentic readiness starts with the fundamentals.

What this means for your roadmap

If you are planning a 2026 Adobe Commerce roadmap, agentic commerce preparation should be a thread running through multiple workstreams — not a separate project.

Q2 2026 priorities:

• Complete catalog attribute audit and enrichment sprint
• Validate SaaS Catalog Service indexing and data quality
• Extend GraphQL API coverage for B2B business logic
• Test storefront with automated crawlers to verify structured data quality

Q3–Q4 2026 priorities:

• Pilot agentic integrations with at least one AI platform (ChatGPT plugin, Gemini action, or custom agent framework)
• Implement real-time inventory and pricing API endpoints optimized for agent queries
• Build monitoring for agent-originated traffic and conversions

The merchants who start now will not just be ready when agentic commerce goes mainstream — they will be the ones shaping how it works.

The Creatuity approach

We work with Adobe Commerce merchants every day, and the pattern is consistent: the teams that treat AI as a feature to install are behind, while the teams that treat it as a capability to build for are ahead.

Our AI-accelerated delivery model means we can run catalog audits, attribute enrichment, and API extension work in parallel — compressing months of preparation into focused sprints. If your catalog is not ready for the agentic shift, the best time to start was last quarter. The second best time is this one.

For more on how we approach AI-powered commerce on Adobe Commerce, see our Adobe Commerce services.