Agentic Commerce FAQs

Agentic commerce is a retail environment where autonomous, AI-powered shopping agents act on behalf of your consumers—not just assisting them with search, but also actively researching, comparing, and completing purchases under defined parameters.

These agents don’t require manual input at each step; they observe, plan, act, learn, and collaborate with other systems.

For instance, a customer’s shopping agent might identify a product need, check availability across multiple vendors, apply discounts, select the optimal delivery node, and execute checkout—all with minimal human intervention.

A study shows that 38% of U.S. shoppers used generative AI tools for online shopping in 2025, with AI-driven traffic to U.S. retail sites up 805% YOY during Black Friday and Cyber Monday 2025.

As AI agents become the discovery/search layer, you need to ensure that your brand is discoverable by those systems; otherwise, your products risk being bypassed entirely.

The sooner you optimize your product data, improve supply chain transparency, and automate order fulfillment, the greater your chances of capturing market share before your competitors.

fabric empowers your brand through a modular, AI-first commerce architecture built to support the transition to agentic shopping:

• Our Product Agent tool takes the raw catalog data and enriches, benchmarks, and activates machine-readable content across channels, making your product agent visible.
• Our agentic commerce platform deploys agents to unify inventory, orders, fulfillment logic, and real-time routing so you can deliver on brand promise in the agentic commerce era.
• Our composable architecture lets you plug these capabilities into your tech stack—including storefronts like Shopify—without the need to replatform.

Shopping agents operate by parsing structured, highly detailed product information—not just what humans read. They rely on data fields such as:

• Product title, brand, category
• Clear description, including use-case or intent language
• Price, availability, and inventory count
• Images and variants (color, size, model)
• Delivery estimates, shipping options, and return policies
• Metadata like warranty, material composition, compatibility, SKU/GTIN/MPN

Ensuring your catalog includes these elements gives agents the material they need to compare, prioritize, and recommend your products.

Machines don’t just scan for keywords; they read structured markup. Some of the best practices include:

• Implementing schema.org markup on product detail pages using JSON-LD.
• Ensuring category pages and variant listings carry consistent attribute names and values.
• Publishing a clean feed (CSV, XML, or JSON) with core attributes such as ID, title, description, link, price, and availability.
• Regularly refreshing the feed to keep inventory and pricing up to date.

This machine-readable layer enhances your visibility to AI agents, enabling them to understand your offerings rather than skip over them.

Images aren’t just for human appeal; agents use and evaluate image clarity and metadata to enhance visibility of your products. Some best practices for image optimization are:

• Using high-resolution images (e.g., 1000 px+ on the longest side) with clean backgrounds.
• Add descriptive alt text (e.g., “sustainable steel adjustable desk—walnut finish”)
• Include structured metadata where supported (e.g., image_object tags).
• Provide multiple views and variants (front, side, in use) so the agent can evaluate use cases and features.

Optimized image data helps the agent see what you sell, improving relevance and reducing surface friction.

To stand out in agentic commerce, extra contextual signals can help. These include ​​Product FAQs highlighting use cases, context-specific details, and comparison information, as well as:

• Loyalty program data (e.g., points earned on purchase or VIP pricing).
• Detailed warranty information, return policy, and service availability.
• Use-case attributes (e.g., “ideal for small spaces”, pet-friendly fabric material”).
• User-generated content (reviews, Q&A) incorporated into feed or markup.

These enrichments allow your AI agents to turn raw data into differentiated value.

An AI-optimized product feed is a structured dataset that provides your full catalog in a format optimized for AI shopping agent ecosystems. For instance, feeds that align with protocols like Agentic Commerce Protocol (ACP) or the feed spec built for ChatGPT shopping.

Key benefits:

• Better machine visibility → higher chance of appearing in AI-agent recommendations.
• Faster feed updates → fewer issues with out-of-stock or price mismatches.
• Uniform distribution of your catalog across human-facing and agent-facing surfaces ensures consistency and responsiveness.

For AI shopping agents to recommend your products, they need access to more than just the SKU and price. Essential supply-chain signals include:

• Real-time inventory availability (available units, reserved stock)
• Fulfillment location (warehouse, store, dropship supplier)
• Estimated time of delivery or shipping
• Estimated shipping cost or fulfillment fee

With these data points, you can enable the agents to evaluate whether your product is relevant and deliverable, improving conversions and reducing cancellations.

Accurate, real-time inventory is now a concern for both customer experience and discoverability. When stock levels are inaccurate:

• You risk overselling or cancellations, which damages the brand image.
• AI search systems and shopping agents prioritize other brands that are available and reliably fulfillable.
• Fewer than 25% of U.S. retailers meet 80%+ shelf/inventory accuracy, and 50% report lost sales as a direct result of inventory accuracy.

Real-time and accurate inventory supports visibility to agents and builds trust with your customers.

You must prioritize fulfillment logic by:

1. Identifying high-value SKUs or fast-moving items that merit preferential sourcing.
2. Defining top fulfillment nodes or regions (store, DC, dropship) based on cost, speed, and margin.
3. Setting delivery SLAs (e.g., 1-2 days, same-day, or international) and routing rules accordingly.

By clarifying these scenarios, you ensure your supply chain data aligns with what shopping agents evaluate and what your customers expect—faster, more reliable fulfillment.

Agentic commerce is rapidly evolving, and there are some emerging protocols you’ll want to support:

• MCP (Model Context Protocol): A standard framework for AI agents to query structured context from commerce platforms.
• OpenAI ACP (Agentic Commerce Protocol): A commerce-specific protocol enabling AI-agent-driven transactions.
• Google AP2 (Agent Payments Protocol): Emerging for payment processing in agent-mediated commerce flows.

Your APIs must be fast, accessible, and designed for seamless integration with agent workflows. A modern, API-first system like fabric can help you become future-ready by enabling you to plug in these protocols.

Agentic commerce depends on the speed, accuracy, and reliability of your backend systems. To support an AI-evaluated fulfillment, you need:

• A modern OMS to unify inventory, orders, routing logic, and fulfillment across channels.
• A Warehouse Management System for real-time warehouse operations.
• An ERP-adjacent financial layer, used only for accounting, and not as the operational engine.
• Third-party logistics (3PL systems that can expose real-time fulfillment updates.)

fabric’s advanced Order Management serves as the orchestration hub across these systems, delivering the real-time accuracy AI shopping agents require.

AI search tools and shopping agents score retailers based on fulfillment performance. Late deliveries, inaccurate stock levels, split orders, and missed delivery promises reduce ranking potential.

Here are the key areas you should optimize:

• Inventory accuracy: AI agents rely on real-time inventory and will deprioritize items that appear unreliable or frequently out of stock.
• Estimated delivery dates: EDD accuracy impacts customer trust. In the U.S., late deliveries are a leading driver of customer dissatisfaction—65% of shoppers report abandoning buying from a retailer after repeated delays.
• Fulfillment logic: Ensure your OMS can dynamically route orders based on cost, speed, distance, weather, or stock.
• Fallback scenarios: Build contingency rules that reroute orders automatically when a node becomes unavailable.
• Split-shipment reduction: Reducing split shipments improves margins and elevates your reliability score with shopping agents.

To meet agent-driven expectations, you should focus on:

• Speed: Real-time visibility across all inventory locations (DCs, stores, warehouses, suppliers).
• Flexibility: Ability to launch or adjust services like BOPIS or Ship-from-Store quickly.
• Consistency: Fulfillment performance should improve over time rather than fluctuate. Agents prefer brands that deliver predictable outcomes.
• Automation: Use AI-assisted routing and automated exception handling to rescue manual workload and errors.

fabric’s advanced order fulfillment platform is designed to support your fulfillment needs with a composable, API-first architecture.

In the new era of an agent-driven world, routing logic must adapt dynamically. Agents evaluate the entire fulfillment path and not just the product listing, so routing rules should account for:

• Inventory availability in real time
• Node proximity to the customer
• Cost to fulfill, including shipping fees or margin impact
• Service-level agreements (SLAs) and promised delivery windows
• Regional delivery constraints

You can assess your readiness by checking whether key operational signals align with AI-driven discovery and fulfillment. You’re likely agent-ready if you:

• Maintain accurate, real-time inventory across stores, warehouses, and suppliers.
• Use structured product data, including attributes, schema markup, and consistent categorization.
• Have clear fulfillment rules and delivery SLAs that support reliable EDDs.
• Can access supply-chain, pricing, and product updates through accessible APIs.
• Monitor fulfillment performance and understand how it impacts customer trust.

If any of these elements are missing, you can still prepare, but readiness begins with complete transparency and visibility into your current position.

The best starting point is diagnosing your product data foundation. fabric’s AI Search assessment provides a simple path to evaluate the visibility of your products to the AI shopping agents. Get your assessment today and pave your path to readiness.

Yes. At fabric, we offer onboarding, technical integration support, and phased deployment to help you adapt to agentic commerce.

Whether you’re enriching product data, unifying inventory, launching BOPIS/Ship-from-Store, or connecting APIs for future agentic protocols, our team of experts offers strategic and technical support to help you scale confidently.