Data & Integration
The term “agent-ready” is moving through B2B commerce conversations faster than the definition is keeping up. Platform vendors use it. Analyst reports use it. Conference panels use it. Most B2B operators we advise nod along, then quietly admit they're not sure what it requires of their data.
That gap matters, because Signal 1 of the Operability Shift is already real for most B2B businesses. Discovery is increasingly happening on surfaces operators don't own. AI agents, LLM-driven search, syndicated marketplaces, AI assistants embedded in productivity software. Buyers arrive at storefronts pre-qualified by systems that have already evaluated dozens of options.
Whether your catalog appeared in those evaluations, and whether your business took part in the buying journey at all, depends on how agent-ready your product data is.
Before the characteristics, it helps to understand what an AI agent is actually doing.
A buyer asks a conversational agent for a recommendation. The agent isn't browsing your storefront. It's reading structured data from many sources, synthesizing across them, applying the buyer's stated and inferred intent, and producing a shortlist in a few seconds.
The data it reads is mostly not the marketing copy on your product detail page. It's the structured product feed flowing to AI surfaces through systems like Google's Universal Commerce Protocol, PayPal Store Sync, retail media platforms and marketplace integrations. The agent reads attributes, taxonomy, identifiers, relationships and availability signals. If those signals are missing, incomplete or ambiguous, your product doesn't surface, and in most cases you never find out.
Your catalog used to be optimized to convert humans who arrived on your storefront. It now has to be readable by machines that decide whether humans arrive at all.
Structured. The data has to be machine-readable beyond marketing copy. Specifications, dimensions, technical attributes, certifications, materials, capacities and tolerances need to live in structured fields rather than PDF datasheets or product image overlays.
In B2B catalogs this is where most operators have the biggest gap. Decades of engineering and product data live in PDFs, spec sheets and embedded images. Buyers and salespeople learned to navigate those formats. AI agents have not. A product whose specifications are only available in a downloadable PDF is invisible to agent-mediated discovery.
The first investment is structured extraction. Pulling attributes out of PDFs and into the product information management layer where they become queryable.
Cross-referenced. Your products have to relate to other things buyers ask about. Compatible parts, alternative SKUs, complementary products, common applications, industry use cases, superseded products. All modeled as explicit relationships.
This matters more in B2B than B2C. A consumer agent recommending a product mostly needs to surface the product. A B2B agent often needs to answer “what works with this,” “what's the equivalent if this is out of stock,” “what's the upgrade path,” or “what application is this used for.” Those answers require relational data most B2B catalogs don't maintain.
Customer part numbers become strategic here. Most B2B buyers think in their own internal part numbers, not the supplier's. When an agent processes a query that references a customer part number, the catalog has to cross-reference it to the actual SKU, or the product is functionally invisible.
Identified. Rich identifier systems let AI surfaces disambiguate products. Manufacturer part numbers, GTINs, UPCs, model numbers, SKU codes, and where applicable customer part numbers. All of them consistent, all of them cross-linked.
This sounds basic, and in well-run consumer catalogs it usually is. In B2B, identifier inconsistency is one of the most common data quality issues we see. The same product might be referenced by three different identifiers across the catalog, the ERP and the customer-facing portal. AI agents can't reconcile that. They treat each identifier as a different product, fragmenting the catalog's authority signals and reducing visibility.
Identifier hygiene is unglamorous work. It also produces one of the highest-ROI investments available for agent-readiness.
Live. Real-time signals on availability and price, plus lead time wherever it applies. Agents synthesizing recommendations factor in whether a product is in stock, when it ships and what it costs. Products with stale or missing availability data get deprioritized or omitted.
For B2B, pricing is the nuanced piece. Contract pricing, tier-based pricing and customer-specific catalogs make price visibility a different problem than it is for consumer products. Agent-ready B2B catalogs need to communicate pricing intent even when actual prices are gated.
“Price varies by account” is an acceptable agent signal. Silence is not.
Lead time and availability need to be authoritative and current. A buyer asking an agent for components that have to ship by next Friday is filtering on lead time. A catalog that doesn't expose lead time data fails that filter silently.
Having agent-ready data isn't enough on its own. The data has to flow to the surfaces where buyers are interacting with agents.
This is where the distribution layer becomes structurally important. Feedonomics and the expanded Feedonomics Surface, Agentic Catalog Exports, PayPal Store Sync, and the endorsement of Google's Universal Commerce Protocol are all parts of the path that gets agent-ready catalog data from the platform to the surfaces buyers are using.
Without that layer, agent-ready data sits inside the platform and doesn't show up where it matters. With it, the platform becomes operating infrastructure for agent-mediated discovery rather than just the place where transactions complete.
We work with B2B operators evaluating most of the major commerce platforms. Rather than rank them, here's what each has assembled so far and what remains open.
BigCommerce has put the most named components in place. Feedonomics for distribution, ACE for agent-specific catalog formatting, MCP for AI tool integration, PayPal Store Sync for agent commerce surfaces, and the UCP endorsement. The open question is execution. The assembly is further along than the track record.
Shopify's agentic investment has concentrated on the consumer side, where its APIs and checkout surface are strongest. B2B Edition has been moving quickly. How far the agentic distribution layer extends to B2B catalogs specifically is the open question for B2B operators.
Salesforce Commerce Cloud has mature catalog management, and the B2B and B2C clouds operate separately. For merchants serving both segments, that separation is the thing to plan around when thinking about a unified distribution story.
commercetools can support all of this through composable services, with the distribution layer built and maintained by the merchant. Whether that's the right distribution of effort depends entirely on the engineering capacity of the team.
Adobe Commerce, Oracle Commerce and SAP Commerce all carry established catalog and feed capabilities. Their public agent-readiness posture is less visible than the others, which may reflect roadmap sequencing rather than absence.
The useful read here isn't a league table. It's that the distribution layer is becoming a real evaluation dimension, and it barely existed as one two years ago.
Three priorities define the work.
Audit catalog completeness against the four characteristics. Most B2B catalogs we've seen have gaps in at least two of the four. Is the data structured, or locked in PDFs? Are relationships modeled, or implicit? Are identifiers consistent across the catalog, the ERP and the portal, or fragmented across all three?
Treat product data as infrastructure investment rather than a marketing project. Agent-readiness is a data engineering and PIM problem first. The teams running it need data management and platform engineering, with ERP and PIM ownership at the table. Run it as a marketing initiative and you get incomplete results.
Plan for non-storefront discovery as a permanent channel mix. The storefront is one channel of many, and the share of buying journeys it owns directly is shrinking. Operating models, marketing budgets and analytics frameworks all have to reflect that.
Signal 1 of the Operability Shift says discovery has moved off the storefront. This is what that requires of B2B catalogs specifically.
The pattern across the framework holds. The constraint is no longer architecture or platform choice. It's the operating work, in this case the catalog data work, that determines whether the platform investment produces results.
The infrastructure is becoming tractable. Preparing catalogs to use it is operator work, and operators who start now will be operating at a different pace than those who wait for the discovery problem to surface as a revenue gap.
The Operability Diagnostic includes a Discovery Readiness component that scores your catalog against the four characteristics above, with output on prioritization and effort.
