Why AI Agents Can't Buy From Your B2B Store (And What to Fix First)

AI purchasing agents are already shopping for your customers' competitors. If your B2B catalog isn't machine-readable, agents skip you. Here's what to fix.

February 18, 2026

An AI purchasing agent representing one of your biggest accounts wakes up at 2 AM, checks current pricing from three suppliers, verifies your inventory levels, and places a $40,000 order. Except it didn’t choose you. Your competitor’s catalog had machine-readable specs, real-time contract pricing via API, and a structured quote endpoint. Yours had a PDF and a phone number.

This is not a hypothetical future. BigCommerce launched their Agentic Commerce Suite in late 2024. SAP is embedding autonomous purchasing agents into their procurement stack. Visa and PayPal are rolling out agent-ready payment APIs. The Agentic Commerce Alliance (ACA) is building shared schemas and secure checkout protocols across the industry. By most analyst estimates, AI agents will be involved in a significant portion of B2B reorder transactions within 18 months.

The uncomfortable truth for most B2B suppliers: your catalog is not built for machines. It was built for humans — humans who can read marketing copy, call a sales rep to ask about volume pricing, and navigate a checkout flow designed around clicking buttons. AI agents don’t work that way. They query APIs, parse structured attributes, apply contract terms programmatically, and abandon any supplier that can’t respond to a machine-to-machine request within seconds.

Here are the four gaps most B2B operations need to close before AI agents start routing around them.

Gap 1: Product Data Written for Humans, Not Agents

Your product descriptions are probably excellent for converting a human buyer who found you through search. They’re useless to an AI agent.

Agents need structured attributes — dimensions in standard units, compatibility codes, certifications in machine-readable formats (ISO numbers, not marketing claims), and technical specs as discrete fields, not paragraphs. A product page that says “our industrial-grade fastener meets all relevant aerospace specifications” tells an AI agent nothing. A schema.org/Product record with certificationBody: "AS9100" and technicalAttribute: ["tensile strength: 150,000 psi", "thread standard: UNF-3A"] lets an agent compare your product against a spec sheet in milliseconds.

Research from Mirakl found that even detailed B2B product descriptions consistently fall short of what agents need — specifically because marketing copy doesn’t expose the technical attributes agents use for comparison and qualification. The data needs to exist as structured fields, not buried in prose.

What this means practically:

Every SKU needs a complete attribute schema, not just a title and description
Units must be explicit and standardized (metric vs. imperial, not just “large”)
Compatibility matrices need to be queryable, not just listed in a table image
Product identifiers (GTIN, MPN, manufacturer SKU) must be accurate and present

If you’re running Adobe Commerce or Magento, this means auditing your EAV attribute coverage. If you’re using a PIM — Akeneo, Salsify, or Pimcore — it means defining attribute templates that expose technical specs as discrete fields rather than free-text blobs. If you want more on structuring complex B2B catalogs for this kind of downstream use, our PIM & complex catalogs guide covers the foundation.

Gap 2: Contract Pricing Locked Behind Human Interfaces

B2B pricing is almost never list price. You have contract tiers, customer-specific discounts, volume breaks, and in some cases, negotiated pricing that lives in your ERP or sales team’s heads. AI agents need access to the actual price a customer qualifies for — instantly, programmatically, with no human in the loop.

Most B2B ecommerce platforms expose pricing through a checkout flow designed for browsers. You add something to a cart, you log in, the price resolves. AI agents can’t navigate that flow reliably. They need a price inquiry API: send a customer identifier, a SKU, and a quantity — get back the contract price, any applicable surcharges, and the conditions that apply.

This is one of the core arguments for API-first commerce architecture. Commercetools, BigCommerce B2B Edition, and Adobe Commerce with B2B modules all have mechanisms for programmatic price resolution — but they’re not all equal, and most implementations have gaps. Common problems:

Contract pricing lives in the ERP (NetSuite, SAP, Epicor) and isn’t synced to the commerce layer in real time
Guest or agent API requests can’t authenticate against a company account
Volume break pricing requires a cart-context that API calls don’t provide

Solving this means treating your pricing engine as a first-class API service — not a side effect of your checkout. For a technical deep-dive on where pricing sync breaks and how to fix it, see our article on pricing and inventory sync for B2B.

Gap 3: No Machine-Readable Quote Workflow

For high-value or configurable products, B2B purchasing still often involves a quote. An AI agent hitting your site in 2026 will look for a structured RFQ endpoint — a way to submit a request with quantities, delivery requirements, and account information, and receive a quote back in a structured format, ideally with a response SLA.

What most B2B sites offer instead: a contact form. Maybe a PDF quote request. In some cases, an email address.

AI agents will simply skip suppliers who don’t offer machine-accessible quoting. The Agentic Commerce Alliance’s emerging standards include structured quote schemas — essentially a standardized JSON payload for submitting and receiving quote requests. Platforms that support it will be discoverable to agents; platforms that don’t will be invisible.

This isn’t about replacing human relationship selling for your most strategic accounts. It’s about the routine, repeatable reorders and standard-catalog quotes that don’t need a sales rep’s judgment — which, for most B2B distributors, is 60-70% of transaction volume. Automating those transactions through a machine-readable quote flow frees your sales team to focus on accounts that actually benefit from human attention.

The short-term practical step: build a quote API endpoint even if it’s backed by a human reviewing and responding on a 2-4 hour SLA. Agents can work with response delays if the interface is machine-readable. The long-term step: automate the resolution for standard catalog items using your ERP pricing rules, routing only exceptions to humans.

Gap 4: ERP Data Not Exposed in Real Time

AI agents make purchasing decisions on real-time data. If your inventory, pricing, and availability data is stale by 4 hours — or 4 minutes — agents will route to suppliers who can guarantee accuracy.

This is the ERP integration problem that has always existed in B2B ecommerce, but agentic purchasing makes it existential rather than just annoying. The standard integration pattern — nightly batch sync from ERP to ecommerce — is fine when human buyers browse catalogs during business hours. It’s completely insufficient when an AI agent queries your inventory at 2 AM before placing a just-in-time order.

What’s required:

Real-time or near-real-time inventory: webhook-based updates or ERP APIs that return current warehouse quantities on demand
Order status feeds: agents will check order status programmatically, not through a “track my order” UI
Availability windows: not just in-stock/out-of-stock, but expected ship dates with warehouse-level precision

The good news: this is solvable with existing middleware tools. Celigo, Boomi, MuleSoft, and Tray.io all have B2B ERP connectors with event-based update capabilities. The choice of which to use depends on your ERP and transaction volume. Our ERP integration roadmap walks through the migration from batch-based to event-driven patterns.

The bad news: most ERP implementations weren’t built to expose real-time APIs to external systems. NetSuite and SAP both have REST APIs, but actually surfacing inventory availability in real time requires thoughtful rate limiting, caching strategy, and sometimes custom endpoints that your ERP implementation vendor never built because no one asked for them until now.

What to Prioritize First

If you’re reading this and your current catalog is mostly PDFs, phone-based quoting, and batch ERP sync — don’t panic. None of this needs to happen simultaneously.

The sequence that makes sense:

1. Start with structured product data. This is the foundation everything else depends on. Define your attribute schema, audit coverage across your SKU catalog, and fill the gaps. A PIM implementation can accelerate this, but even a spreadsheet-driven attribute cleanup project delivers value before any API work starts.

2. Expose a pricing inquiry API. Even if it just wraps your existing customer-tier pricing logic, having a machine-addressable price endpoint lets agents evaluate you as a supplier. This is typically a 3-6 week development project on most B2B ecommerce platforms.

3. Build or buy a quote endpoint. Start with human-reviewed quotes returned in a structured format. Set a response SLA you can actually meet. Add automation over time as volume justifies it.

4. Move ERP sync to event-driven. This is the highest-effort item but has returns well beyond agentic commerce — it also fixes the real-time pricing accuracy problems your human buyers complain about today.

The companies pulling ahead in B2B commerce right now aren’t waiting for AI agents to arrive. They’re making infrastructure decisions today that determine whether they’re buyers’ first call — or the supplier that gets bypassed in an automated workflow they never saw coming.

If you want an honest assessment of where your current stack stands against these requirements, we do exactly that and have been doing it for manufacturers and distributors for over a decade.

Frequently Asked Questions

What is an AI purchasing agent in B2B ecommerce?

An AI purchasing agent is an autonomous software system that acts on behalf of a buyer to research products, compare pricing, check inventory, and place orders — without requiring a human to manually navigate supplier websites or portals. These agents use APIs and structured data to make purchasing decisions based on pre-set parameters and budget rules.

Why can’t AI agents use my existing B2B website?

Most B2B websites were designed for human navigation — they rely on browser-rendered interfaces, manual login flows, and product information in prose rather than structured fields. AI agents need machine-readable APIs for pricing, inventory, and quoting, plus structured product attributes they can parse programmatically. Without these, agents can’t reliably retrieve the data they need to qualify you as a supplier.

What is machine-readable product data for B2B?

Machine-readable product data means product attributes are stored and exposed as discrete structured fields — dimensions, certifications, compatibility codes, technical specs — rather than embedded in marketing descriptions. It typically uses schema.org markup, structured JSON feeds, or PIM-exported attribute sets that AI systems can parse and compare without natural language processing.

How soon do B2B companies need to prepare for agentic commerce?

Now. BigCommerce launched their Agentic Commerce Suite in 2024, SAP is building autonomous purchasing into their stack, and the Agentic Commerce Alliance is defining shared schemas that will become de facto standards. Companies making platform and catalog decisions in 2026 will either build agentic-ready infrastructure or spend 2027-2028 retrofitting systems that should have been designed correctly from the start.

Do I need to fully rebuild my B2B ecommerce platform to support AI agents?

No. Most agentic commerce readiness improvements are additive — you’re adding API endpoints, improving product data completeness, and moving ERP sync to event-driven patterns. These changes layer on top of existing platforms. The most important first step is structured product data, which delivers value across SEO, buyer experience, and agentic readiness simultaneously.