Agentic Commerce in 2026: Definition, UCP, and Concrete Implications for Merchants

Agentic Commerce: A Major Shift in How Purchases Are Initiated

Agentic commerce refers to a purchasing model where artificial intelligence no longer merely assists with search or provides recommendations. Instead, it helps execute an intent within a defined framework. Concretely, an agent can help clarify a need, select an option, apply constraints such as budget, brand preference, or delivery timeframe, then initiate a transaction and orchestrate the steps that follow. This is not just an experience enhancement, it represents a shift in the distribution model, as the human interface is no longer necessarily the central point of the journey.

For eCommerce merchants, the value is twofold. On one hand, agentic commerce can reduce friction, particularly for complex carts, large catalogs, or scenarios where comparison is time-consuming. On the other, it introduces an additional channel where competition plays out differently, not only on the attractiveness of the offer, but also on the ability to make that offer understandable, interpretable, and actionable by automated systems.

Why UCP Is Central to the Conversation

The Universal Commerce Protocol (UCP) is rooted in a standardization approach. For agentic commerce to move from isolated demonstrations to broad adoption, the ecosystem needs a shared language between agents, platforms, and merchant systems. UCP is designed to make these interactions more universal, enabling an agent to discover what a merchant can support, query critical information, and then execute transactional steps in a more robust and reliable way.

In other words, UCP is not just a technology announcement, it’s a signal that leading players are working to industrialize agentic commerce by removing integration friction. For organizations, this marks a level of maturity. When a protocol emerges, it quickly shapes how platforms, partners, and purchasing surfaces prioritize their roadmaps and structure their capabilities.

What This Means in Practice: The Transaction Becomes a Compatible Process

In a more agentic commerce model, several elements become non-negotiable. An agent needs reliable, structured, and up-to-date information in order to act. Availability, pricing, variants, shipping and return policies, compliance requirements, and even certain service rules must be expressed in a consistent way. Commerce is no longer just about convincing a visitor, it is also about making the offer executable in an environment where decision-making and action can be partially automated.

This compatibility directly impacts architecture. The more a commerce stack relies on systems that can exchange information cleanly, the better equipped it becomes to support agentic interactions. Conversely, an environment where data is fragmented, rules are implicit, or inventory and pricing do not reflect operational reality becomes increasingly incompatible with agent-initiated transactional journeys.

Why Shopify Merchants Are Well Positioned in This Context

The competitive advantage does not lie solely in being on a popular platform. It comes from the alignment between the platform, emerging standards, and the strength of underlying data foundations. Shopify’s co-development of UCP with Google is a clear directional signal. It shows Shopify’s intent to ensure its ecosystem is ready to support increasingly agentic commerce experiences. Because discoverability and AI comprehension rely heavily on data structure and consistency, platforms that accelerate and standardize these foundations provide a built-in advantage to their merchants. In this context, Shopify merchants benefit mechanically from an ecosystem designed to make offers more readable, interpretable, and actionable by AI-driven agents.

This does not mean everything is automated, nor that preparation is already complete. Rather, it means the foundation is more favorable: a unified architecture, more advanced data structuring practices, and product roadmaps aligned with emerging standards.

From Conversation to Execution: A Logical Continuation of Previous Announcements

Over the past few years, many announcements have focused on conversational AI in commerce, particularly around assisted search, customer service, and content generation. Agentic AI fits into this progression but represents a decisive step forward. It connects understanding to action. Where conversational AI improved the information experience, agentic AI targets the transactional and orchestration experience. For merchants, this translates into a shift in expectations: it’s no longer enough to be persuasive, you must also be executable.

What’s the Difference Between the Shopify x ChatGPT (2025) Announcement and the Google UCP (2026) Announcement?

The 2025 ChatGPT announcement primarily fit within a conversational AI framework. Its goal was to enhance discovery, assistance, and information experiences by making interactions more natural and more efficient for customers. In other words, AI was mainly improving how users find, understand, and choose. By contrast, the 2026 announcement around the UCP protocol represents a more structurally significant step for commerce. It’s no longer just about a conversational interface, but about a compatibility layer designed for agents capable of triggering and orchestrating transactional actions. The shift is meaningful: from dialogue that guides, to a framework that makes offers not only discoverable, but truly executable by agents, in a standardized and secure way. For context, see our analysis of the 2025 announcement.

What This Changes for Consumers and Merchants: A Concrete Example

Let’s take a home improvement scenario. Someone wants to repaint their deck and asks Gemini for an exterior paint suitable for treated wood, UV-resistant, compatible with a humid climate, in a specific color, with the quantity estimated based on surface area, and available for in-store pickup on Saturday morning. Today, this type of purchase often requires multiple searches, technical comparisons, rough quantity estimates, and manual checks of inventory and required accessories. In an agentic commerce flow, the agent can translate intent into a complete and coherent cart. It selects a paint that meets the constraints, calculates the required quantity based on surface area, adds the compatible primer and tools, checks local inventory, then proposes a pickup or delivery option before finalizing the purchase. For this journey to be possible and advantageous for merchants (for example, on Shopify), the eCommerce strategy must be agent-ready: structured and exhaustive product data (substrates, use cases, compatibilities, coverage, climate constraints), reliable pricing and availability signals, up-to-date logistics information, and explicit relationships between complementary products so the agent can assemble an accurate cart. In this context, discoverability is no longer driven solely by the interface, but by the catalog’s ability to be correctly understood and executed by agents.

How to Prepare Now: An Operations- and Foundations-Led Approach

Preparing for agentic commerce in 2026 is not about adding an agent to your website. Real readiness comes from building foundations that make the offer robust, readable, and reliable. In an agentic world, an inventory mismatch, an ambiguous policy, a poorly structured catalog, or misunderstood data results in a failed transaction or a loss of trust. Winning organizations will therefore approach this as a commercial and operational maturity initiative, not a surface-level AI feature.

The first priority is to strengthen data quality and structure. The catalog must clearly express what the product is, what it includes, who it’s designed for, and under what conditions it can be delivered, returned, or supported. Agentic commerce places high value on precise attributes, well-defined variants, and consistent identifiers. The second priority is to make dynamic signals reliable, especially inventory, pricing, and lead times. An agent does not interact with a marketing promise; it interacts with operational reality. The third priority is to improve system compatibility, as agentic commerce depends on the ability to chain transactional steps with minimal friction.

Finally, the topic must be addressed through the lens of governance. Who validates what an agent is allowed to do? Which actions require confirmation? How are exceptions, substitutions, cancellations, and returns handled? These decisions, just as much as the technology itself, will determine an organization’s ability to extract real value from agentic commerce.

How Agentic Commerce Changes Competition

L’agentic commerce introduit une compétition plus structurelle. Les marchands et marchandes ne seront pas seulement comparés sur la valeur perçue, mais aussi sur la capacité à être compris(es) et actionné(e)s efficacement. Les organisations qui investissent tôt dans la structuration des données, dans la fiabilité opérationnelle et dans l’architecture unifiée pourront capter plus rapidement les bénéfices de ce nouveau canal. En effet, 51 % des dirigeants et dirigeantes retail, et CPG dans des organisations qui utilisent la GenAI indiquent avoir des agents d’IA en production et 37 % des dirigeants et dirigeantes retail, et CPG déclarent que leur organisation a déjà lancé plus de 10 agents d’IA (1). À l’inverse, celles qui abordent l’agentic AI comme une simple couche d’interface risquent d’être limitées par des fondations insuffisantes.

How to Adapt an SEO and GEO Strategy to an Agentic Commerce Context

In an agentic commerce environment, SEO remains essential for capturing human demand—but it is no longer sufficient on its own. As AI agents increasingly act as purchasing intermediaries capable of comparing, recommending, and even executing transactions, businesses must also optimize for discoverability and comprehension by language models. This is precisely the objective of GEO (Generative Engine Optimization) or GSO (Generative Search Optimization): structuring information, product data, and credibility signals so they can be correctly interpreted, cited, and prioritized by AI systems. In other words, the competition is no longer just about ranking on a search results page, it’s about being selected as a reliable, actionable answer in AI-driven journeys where decisions are made through agents. From this perspective, GEO becomes a direct competitive advantage. It strengthens machine readability, signal consistency (products, pricing, availability, policies), and the ability to surface within a new intent-driven acquisition and conversion channel. To go deeper, see our article, SEO vs. GSO: What Are the Winning Strategies to Boost Your eCommerce with AI?

Potential Impacts for B2B Organizations

In B2B, agentic commerce could accelerate a transformation that is already underway: the automation of recurring purchases, the streamlining of complex buying journeys, and the orchestration of advanced business rules. While B2C primarily benefits from increased fluidity, B2B can unlock measurable productivity gains, especially for large catalogs, product configurations, negotiated pricing tiers, compliance constraints, specific delivery conditions, or internal approval policies. An agent can, for example, support a buyer by translating intent into a cart that complies with contractual terms (pricing, availability, allowed substitutions, lead times, MOQs), then initiating the transaction in accordance with the organization’s authorization rules. For B2B organizations, the challenge becomes strategic: structuring data, clarifying rules, and ensuring the reliability of operational signals in order to be ready to work with agents, not only to sell more effectively, but also to reduce friction on the buying side and strengthen service quality for key accounts.

Would you like to discuss this with a Novatize specialist?

Pierre-Olivier Brassard

Vice President - Products and Technology, Partner

contact@novatize.com+1 844 932-6682

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