How to Do Agentic Commerce: A Step-by-Step Workflow
To do agentic commerce well, you build a machine-readable product foundation first, then verify that AI shopping agents can actually read, trust, and transact against it. That means structured product data, agent-legible policies, checkout compatibility, and a testing loop where you repeatedly ask agents to shop your category and fix whatever they get wrong.
- Start With a Baseline Audit
- Build a Machine-Readable Product Feed
- Add Structured Data Agents Actually Use
- Write Agent-Readable Policies
- Prepare for Agentic Checkout
- Test How Agents Describe and Recommend You
- Measure Agent-Driven Traffic and Conversions
- Common Pitfalls That Get Brands Excluded
- Where to Start This Week
Below is the sequence we walk clients through at Salterra Digital Services when a product catalog needs to become “agent-ready.” It’s ordered the way we actually run it: audit first, fix the data layer, harden policies, confirm checkout paths, then test and measure on a recurring cadence, because agent behavior shifts as the platforms update.
Start With a Baseline Audit
Before touching a single feed or schema tag, find out what agents currently know about you. This is the step most teams skip, and it’s the one that saves the most rework later, because you’ll often discover the problem isn’t your product data at all — it’s that an agent can’t reach your site, or is reading a stale cached version of it.
Step 1: Check crawler access
Pull your robots.txt and confirm you aren’t blocking the user agents that power shopping assistants (OAI-SearchBot, GPTBot, PerplexityBot, ClaudeBot, and similar). Sites block these by accident through a default security plugin or CDN bot-management rule, which silently removes them from agent consideration before any optimization work even matters.
Step 2: Ask the agents directly
Open ChatGPT, Perplexity, and Gemini and run the buying queries a real customer would use — “best [product type] under $X,” “where should I buy [specific product],” “compare [your product] to [competitor].” Record what each agent says: is your brand named, is the price right, is availability current, and does the recommendation sound confident or lukewarm. This baseline is what you’ll compare against later to prove the workflow moved the needle.
Build a Machine-Readable Product Feed
Agents don’t browse your site the way a human does — most rely on structured feeds and APIs to pull authoritative price, availability, and variant data rather than scraping rendered pages. A thin, stale, or incomplete feed means the agent skips you or hallucinates details from an old crawl, and either way you lose the sale.
Step 3: Get every SKU into a complete, current feed
Whether you’re feeding Google Merchant Center, a marketplace connector, or a direct feed for ChatGPT shopping surfaces, every SKU needs a full attribute set: title, brand, GTIN/MPN, price, sale price, availability, condition, size/color/variant, shipping weight, and a canonical product URL. Gaps in required fields are the single most common reason a product gets excluded from agent-surfaced results.
Step 4: Automate price and inventory sync
Agentic commerce assumes near-real-time accuracy. If your feed updates once a day but stock sells out by noon, an agent will confidently recommend an out-of-stock item, and the failed checkout damages trust in your brand at the platform level, not just with that one shopper. Push updates on change, not on a batch schedule, wherever your platform supports it.
Add Structured Data Agents Actually Use
Structured data is how you make claims verifiable rather than just readable. An agent choosing between two similar products will lean on whichever has clean, unambiguous markup, because it reduces the agent’s own risk of recommending something wrong.
Step 5: Mark up Product, Offer, and AggregateRating
Implement schema.org Product markup with nested Offer (price, priceCurrency, availability, url) and, where honest, AggregateRating and Review. Keep every value in the markup identical to what’s rendered on the page and in your feed — mismatches between structured data and visible content are exactly the kind of inconsistency that gets a product deprioritized or flagged.
Step 6: Mark up return and shipping policy at the offer level
Schema.org supports MerchantReturnPolicy and OfferShippingDetails nested right inside your Offer markup. This is what lets an agent answer “can I return this if it doesn’t fit” without leaving the product page or guessing. Most sites bury this in a separate policy page an agent may never associate with the specific SKU it’s evaluating — nest it at the offer level so it travels with the product.
Write Agent-Readable Policies
Agents increasingly answer policy questions on the spot to close a sale — shipping cost, return window, warranty terms — and if your policy page is a wall of legal prose, the agent either misreads it or refuses to state anything definitive, which reads to the shopper as uncertainty about your brand.
Step 7: Rewrite policies as plain, extractable statements
State the concrete facts up front in short sentences: “Returns accepted within 30 days, unworn, with tags.” “Free shipping on orders over $50, arrives in 3–5 business days.” Put the qualifiers and exceptions after the core statement, not before it. Agents (and impatient humans) extract the first clear sentence; bury it under caveats and it gets dropped or mangled in a summary.
Step 8: Keep one canonical policy source
If your shipping terms live in three slightly different versions across your FAQ, your footer, and your checkout page, an agent may quote any of them — including the outdated one. Consolidate to a single canonical policy page, link every other mention back to it, and update in one place only.
Prepare for Agentic Checkout
This is the newest and fastest-moving layer, and it’s where “agent-readable” turns into “agent-transactable.” The platforms are converging on agentic checkout protocols that let a shopping assistant complete a purchase on a user’s behalf without bouncing them through a full manual checkout flow.
Step 9: Evaluate your platform's agentic checkout support
If you’re on Shopify, WooCommerce, or a major platform, check whether it already supports an agentic/instant checkout integration or an emerging protocol built for agent-initiated purchases. For custom carts, this usually means exposing a structured checkout API rather than requiring a rendered HTML form the agent has to fill out blind.
Step 10: Confirm identity, tax, and fraud logic work headless
Agentic checkout strips away the visual cues a fraud system normally relies on — mouse movement, session behavior, dwell time. Confirm your payment processor and fraud rules have an agent-aware path, and test a handful of live small-dollar transactions end to end rather than assuming the integration “just works” because the vendor says it does.
Test How Agents Describe and Recommend You
Optimization without testing is a guess. Build a short, repeatable script of prompts and run it on a schedule, the same way you’d track keyword rankings.
Step 11: Run a standing prompt set monthly
Reuse the queries from your baseline audit plus a few competitor-comparison prompts, and log the answers verbatim. Watch for three things specifically: is your brand named at all, is the factual detail (price, stock, policy) correct, and does the framing sound like a confident recommendation or a hedge.
Step 12: Fix what the agent gets wrong at the source
If an agent states an incorrect price, don’t argue with the chatbot — trace it back to whichever feed, schema block, or cached page is actually feeding that number and fix it there. Agent answers are downstream of your data; treat every wrong answer as a data bug report, not a content problem to argue around.
Measure Agent-Driven Traffic and Conversions
You can’t improve what you can’t see, and agent referrals often hide inside “direct” traffic because agents don’t always pass a clean referrer.
Segment your analytics for known agent referrers (chatgpt.com, perplexity.ai, and similar) and tag trackable outbound links from AI platforms with UTM parameters where the platform allows it. Cross-reference against server logs for agent user-agent strings, since some agent-driven visits arrive without a referrer at all. Even an imperfect view of this segment, tracked consistently, tells you whether the earlier steps are translating into real sessions and sales — the trend line matters more than any single month’s number.
Common Pitfalls That Get Brands Excluded
A few mistakes show up repeatedly in client audits, worth checking proactively rather than waiting to trip over them.
- Blocking agent crawlers by accident through a WAF or bot-management default that predates agentic commerce.
- Feed and page mismatches — price or stock in your feed disagrees with what’s rendered on the product page.
- Policy details that only exist in an image or PDF, which most agents can’t reliably extract.
- Thin or missing reviews, leaving an agent no basis to vouch for the product over a competitor’s.
- No monitoring — treating this as a one-time setup instead of an ongoing loop, while the platforms keep changing how they parse and rank offers.
Where to Start This Week
If you only have a few hours, do the audit (Steps 1–2) and the feed/schema pass (Steps 3–6) first — everything else depends on agents being able to read accurate data about you. Checkout readiness and measurement matter, but they’re wasted effort if the underlying product data is still wrong or unreachable.
We run this sequence on real client catalogs at Salterra, and the pattern holds across niches: the sites that show up favorably in agent answers are rarely the ones with the flashiest marketing — they’re the ones whose product data an agent can trust without guessing.
Frequently Asked Questions
Do I need a separate feed for AI shopping agents, or can I reuse my Google Merchant feed?
Your existing Merchant Center feed is a strong starting point since it already enforces the attribute discipline agents need, but confirm each channel's specific requirements — some agentic commerce integrations expect additional fields, like return policy or shipping detail objects, that a standard feed may omit.
How long does it take to see results after optimizing for agentic commerce?
Expect weeks rather than days, since agents rely on caches and periodic recrawls, and your own monthly prompt-testing cadence is what will actually surface the change.
Is agentic checkout only relevant for large ecommerce brands?
No — small and mid-size stores on mainstream platforms are increasingly getting agentic checkout support built in, so the more relevant question is whether it's enabled and correctly configured, not whether you're large enough to qualify.
What's the single biggest reason an agent skips a product it should recommend?
Incomplete or inconsistent structured data is the most common cause — missing required feed attributes or a mismatch between schema markup and the visible page is enough for an agent to exclude a product rather than risk recommending something inaccurate.
Should I write content specifically "for AI agents" separately from my normal product copy?
No — write one clear, accurate version of your product and policy information and make it structured and accessible; agents and human shoppers both respond better to plain, front-loaded facts than to content artificially split into a human version and a machine version.
How do I know if an agent is already sending me traffic?
Check your analytics for known AI-platform referrers and review server logs for agent user-agent strings, since some agent-driven sessions arrive without a referrer and would otherwise be misclassified as direct traffic.
Terry has 30+ years in software and SEO. He’s the founder of Salterra Digital Services and SEO Spring Training, host of the Roundtable SEO Mastermind, and lead instructor at SEO University — teaching the exact tactics his team uses on client work.
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