Retailers no longer ask whether AI should handle commerce tasks. They ask which tasks to hand over first. The shift is visible in deployments already live at scale: AI agents that browse on behalf of shoppers, negotiate returns without a human queue, reorder grocery staples before a customer thinks to ask, and surface products from a conversational prompt rather than a keyword search box. These are not prototypes. They are agentic commerce examples running today inside some of the world's largest commerce platforms.
This article maps real deployments across retail, fintech, grocery, and search. Each example shows what the agent does, how it acts autonomously on a user's intent, and what outcome it drives for merchants and buyers alike. If you want to understand the category itself before diving in, start with What Is Agentic Commerce?
What Makes an Example Agentic Commerce?
Not every AI feature qualifies. A chatbot that answers FAQ questions is helpful but not agentic. An agentic system takes goal-directed action across multiple steps, uses tools (search, cart APIs, payment rails), and completes commerce tasks with minimal interruption.
Three signals separate agentic commerce from conventional AI features:
- Multi-step execution — the agent moves through more than one action (search, filter, add to cart, confirm) without human direction at each step.
- Real-world tool use — the agent calls live systems: inventory databases, payment gateways, shipping APIs.
- Goal completion, not just response generation — the measure of success is a transaction or a fulfilled intent, not a helpful answer.
With that bar set, here are the deployments that clear it.
Retail and E-Commerce Agent Deployments
Amazon Rufus: The Shopping Copilot
Amazon launched Rufus in February 2024, embedding a conversational AI agent directly in its mobile app and website. Rufus answers complex shopping questions, compares products across the catalog, reads customer reviews to surface pros and cons, and places recommended items in a user's cart — all within a single conversation thread.
What makes Rufus an agentic commerce example rather than a search upgrade is its ability to chain intent signals. A user asking about camping gear does not get a keyword-matched list. Rufus infers budget, skill level, and season from follow-up questions, then filters the Amazon catalog accordingly. Amazon reported that Rufus handled hundreds of millions of queries in its first year, with the company continuing to expand its capabilities into fashion, electronics, and consumables.
Shopify Sidekick: Merchants Meet AI Agents
While Rufus faces consumers, Shopify's Sidekick targets merchants. Launched in 2024, Sidekick is an AI agent that runs inside the Shopify admin panel. It can generate discount campaigns, analyze sales reports, write product descriptions, and trigger inventory workflows based on natural language instructions.
A merchant can tell Sidekick: Create a 20 percent off discount for all sneakers, valid this weekend only, and exclude items already on sale. Sidekick interprets the instruction, accesses the catalog, cross-references existing promotions, and executes the campaign — all without the merchant navigating a single settings menu.
Shopify also introduced buyer-facing agents through its AI shopping assistant integrations, allowing customers on Shopify-powered stores to query products and complete purchases conversationally. By mid-2026, Shopify's agentic infrastructure had become one of the most widely adopted deployment patterns for mid-market e-commerce operators.
Walmart Replenishment and Personalization Agents
Walmart has deployed agentic AI at two layers. On the backend, inventory replenishment agents monitor stock levels across thousands of store SKUs and automatically trigger purchase orders when thresholds are crossed — without buyer approval for routine items. On the consumer side, Walmart's app uses AI agents to build personalized shopping lists based on past purchases, weekly specials, and dietary preferences stated in-app.
The inventory agent is a strong agentic commerce example because it closes the loop between demand signals and supply action. No human reviews each micro-order. The agent handles it within policy guardrails set by the merchant ops team.
Fintech: Klarna's AI-Powered Commerce Agent
Klarna is one of the clearest agentic commerce examples in fintech. The Swedish buy-now-pay-later company replaced most of its customer service function with an AI agent in 2024. The agent handles refunds, dispute resolution, payment plan adjustments, and order tracking — tasks that previously required human agents in queues.
Klarna reported in early 2024 that its AI agent handled the equivalent of 700 full-time human agents' workload in its first month, processing two-thirds of all customer service chats and resolving most inquiries in under two minutes. Customer satisfaction scores remained on par with human agents.
Beyond service, Klarna's agent recommends products, surfaces relevant merchant offers, and guides users to checkout from within the Klarna app. A user browsing for a laptop can ask Klarna's agent to find the best option within a stated budget, filter by the merchants Klarna partners with, and then split the payment at checkout — all in one agent-led flow.
Grocery and On-Demand Commerce Examples
Instacart Caper Cart: The Physical Store Agent
Most agentic commerce examples live in digital interfaces. Instacart's Caper Cart brings the category into physical retail. The Caper Cart is a smart grocery cart equipped with AI, computer vision, and a touchscreen. It identifies items placed in the cart, tracks the running total, suggests complementary products, and lets shoppers check out without a cashier.
The agent layer emerges in the suggestion engine. Caper tracks what a shopper has already selected, cross-references a store's current promotions, and proactively surfaces relevant additions. A shopper who drops pasta, sauce, and ground beef into the cart may see a prompt for parmesan and garlic bread — items the agent inferred belong with that meal.
Kroger and Schnucks have deployed Caper Carts in U.S. stores. The deployment represents an important class of agentic commerce example: physical commerce augmented by an agent that operates in real time without requiring the shopper to open an app.
Search-to-Buy: AI Assistants as Storefronts
Perplexity Buy Mode: Intent Meets Purchase
Perplexity launched a Buy feature in 2025 that lets users complete product purchases directly from search results. When a user asks for the best noise-canceling headphones under $250, Perplexity's agent retrieves live product data, compares options across retailer partners, and presents a purchase interface in the same thread. Shoppers can buy without leaving Perplexity.
This compresses the traditional funnel: awareness, consideration, and purchase collapse into a single conversational exchange. Perplexity earns revenue through merchant partnerships, making it an agentic commerce layer sitting directly between intent and transaction.
OpenAI Shopping in ChatGPT
In early 2025, OpenAI added shopping capabilities to ChatGPT, allowing users to ask product questions and receive curated recommendations with pricing, review summaries, and direct buy links — without paid placement influencing the results. By mid-2025, ChatGPT's shopping feature pulled from a broad product index and, for users with memory enabled, began incorporating stated preferences and past purchase context into recommendations.
This integration matters because ChatGPT has hundreds of millions of active users. When those users ask "what's the best coffee grinder under $100," the agent evaluates options, reads review data, and surfaces a recommendation with a direct path to purchase. The merchant is invisible in this flow until the agent selects them. That reality makes optimizing for AI-driven discovery as important as optimizing for traditional search.
Google AI Overviews and Shopping
Google's AI Overviews deepened its Shopping Graph integration in 2025 and 2026 so that AI-generated summaries for product queries include real-time pricing, stock status, and direct checkout links from verified merchants. The agent behavior is more constrained than Perplexity's, but the intent interpretation and autonomous product matching qualify as early-stage agentic commerce. As Google expands Gemini's tool-use capabilities, the line between search result and commerce agent will continue to blur.
Common Threads Across All Examples
Looking across these deployments, four patterns appear consistently:
Agents work within defined policy guardrails. Walmart's replenishment agent does not place unlimited orders. Klarna's agent escalates disputes above a certain dollar value. Guardrails allow scale without catastrophic errors.
The best examples reduce user effort to near zero. Rufus finds the camping gear. The Caper Cart suggests the parmesan. The measure of success is that the user barely had to think about the purchase.
Tool access is the critical enabler. Every example above integrates live data sources — catalog APIs, inventory databases, payment rails. Without tool access, these would be chatbots. With it, they are agents completing real transactions.
Merchant economics shift toward the platform layer. Platforms that control the agent layer also control where the transaction ends up. Merchants who do not optimize for agentic interfaces risk losing visibility in the channels that increasingly drive purchase decisions.
What These Examples Mean for Merchants
The agentic commerce examples listed here are not isolated experiments. They represent a structural shift in how products are discovered, evaluated, and purchased. Merchants who understand this shift early can take concrete steps:
- Structure product data for agent consumption — clean titles, detailed attributes, and machine-readable pricing matter more than ever when an agent, not a human, is reading your catalog.
- Optimize for intent, not just keywords — agents interpret goals. Content and catalog copy should map to shopper intent rather than narrow keyword matches.
- Build or partner with agentic platforms — selling through Shopify with Sidekick enabled, optimizing for Perplexity's Shopping Graph, or integrating with Klarna's merchant network places products in front of agents when they act on user goals.
Agentic commerce is not a future category. These examples show it is already the present for companies operating at scale. The question for most merchants is not whether to engage — it is how fast to move.