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The Agentic Commerce Checklist

How to Prepare Your Business for AI-Driven Search & E-commerce

AI agents are already browsing, comparing, and buying on behalf of consumers. Use this checklist — synthesized from across the web and updated for Google's Universal Commerce Protocol (UCP) — to make sure your business is discoverable, trustworthy, and AI transact-ready.

10 sections · 65 checks

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1. MAKE SURE AI AGENTS CAN FIND YOU

  • Audit your robots.txt file to allow AI crawlers (GPTBot, ClaudeBot, Google-Extended) without blocking them.
  • Use server-side rendering — not client-side JavaScript — so agents can read your product pages and prices.
  • Create a /llms.txt file at your root domain: a plain-text, Markdown index of your products, policies, and docs.
  • Publish a sitemap.xml and reference it alongside /llms.txt so agents can enumerate every page you want indexed.
  • Set up a real-time product feed so agents get current pricing and availability without waiting on crawl cycles.
  • Set up Google Merchant Center as your product-data hub — it remains the entry point for Google's Shopping and agentic (UCP) surfaces.
  • Ensure site speed and mobile performance are strong — agents prioritize fast, accessible content.
  • Keep your content current; outdated pages give agents incorrect data to act on.

2. HELP AGENTS UNDERSTAND YOUR BUSINESS

  • Add a well-known/ai-plugin.json file that explains who you are and what your API does — specific, actionable language, not generic marketing copy.
  • Publish a /.well-known/ucp profile declaring your version, services, capabilities (e.g. catalog_search), and payment_handlers — the first document UCP-aware agents fetch when deciding whether they can transact with you.
  • Create or update a manifest.json with your brand name, logo, and visual identity so agents render your brand correctly.
  • Write a detailed openapi.yaml that tells agents how to search products, check inventory, and place orders.
  • Build a product data model with a clear taxonomy, attributes, variants, pricing, and compatibility info — structured for machine comparison.
  • Build an action model that tells agents what they can do on your site (quote requests, availability checks, orders) with defined inputs, outputs, and fallback paths.
  • Create an agent directory combining your product data model and action model into one machine-readable entry point.
  • Provide a machine-readable agent usage policy — permitted actions, rate limits, purchase caps, and data-use terms an agent can read before it acts.

3. OPTIMIZE YOUR PRODUCT DATA

  • Add GTINs and MPNs to every product — these are the anchors agents use to compare products across sellers.
  • Replace vague descriptions ('premium quality') with specific, structured specs: materials, dimensions, weight, certifications, temperature ranges.
  • Implement comprehensive Schema.org markup in JSON-LD: Product, Offer, Review, AggregateRating, MerchantReturnPolicy, ShippingDeliveryTime.
  • Encode prices as ISO-4217 minor units (e.g. 4900 = $49.00 USD), not display strings — the price format UCP and ACP expect.
  • Write product titles that work in conversational queries, not just keyword searches.
  • Write descriptions answering: Who is this for? When would you use it? What problem does it solve? What makes it different?
  • Standardize formatting across your catalog — units, pricing, attribute styles, material descriptions, and title casing.
  • Keep inventory, pricing, and product data synchronized in real-time across all channels.
  • Make your return policy machine-readable with explicit MerchantReturnPolicy schema: return window, method, fees, and conditions.
  • Do not hide critical product details inside PDFs, images, or JavaScript-rendered content AI crawlers can't access.
  • Optimize image alt text — include material, color, form factor, and use case, not just generic labels.

4. BUILD EXTERNAL TRUST AND CONSISTENCY

  • Actively collect customer reviews and ratings — AI agents weight third-party validation heavily when making recommendations.
  • Implement AggregateRating schema markup to make review data machine-readable.
  • Seek editorial coverage, creator mentions, and forum presence — agents cross-check your claims against external sources.
  • Maintain a single source of truth for pricing, stock, policies, and descriptions — inconsistencies cause agents to hesitate or skip your brand.
  • Ensure pricing, descriptions, and availability are consistent across all marketplaces and channels.

5. CONFIGURE YOUR SITE FOR NON-HUMAN TRAFFIC

  • Use a CDN with edge computing to detect AI crawlers and serve lightweight Markdown or JSON — can cut token cost by ~90%.
  • Configure your Web Application Firewall to rate-limit (not block) known AI agents with a 429 response and Retry-After header.
  • Aggressively cache read-heavy endpoints like price and inventory at the CDN edge to prevent agent timeouts.
  • Prepare for Model Context Protocol (MCP) — design for API-first data access so agents can query live catalog and pricing directly.
  • Enable API-first checkout so agents can programmatically create carts, validate addresses, and authorize payments.

6. WRITE CONTENT THE WAY CUSTOMERS ASK QUESTIONS

  • Shift from keyword-focused product copy to conversational, intent-based language ('Who is this for? What problem does it solve?').
  • Make sure category pages explain tradeoffs, not just list SKUs.
  • Update FAQs to mirror how customers actually ask questions, not how internal teams label things.
  • Monitor your AI visibility monthly — query ChatGPT and Perplexity the way a customer would and track whether your brand appears.

7. PREPARE YOUR INTERNAL SYSTEMS AND DATA

  • Audit and clean your core business data — deduplicate customer records, standardize labeling, and archive outdated data.
  • Ensure all major systems have accessible, documented APIs that agents can interact with.
  • Connect your website to business data systems (CRM, inventory, bookings) through native integrations.
  • Map your repeatable internal workflows with enough specificity that an AI agent could execute them — define the trigger, inputs, steps, decision points, and expected output.
  • Prioritize workflows for automation by frequency, time cost, and error rate.

8. ALIGN YOUR TEAM AND LEADERSHIP

  • Shift marketing copy from emotional storytelling to structured, precise product data.
  • Train IT and security teams to distinguish legitimate AI agents from malicious scrapers — and to verify agent identity and authorization (signed requests, allow-listed agents), not just rate-limit traffic.
  • Align your CIO, CTO, and CDO around treating product data as transactional infrastructure, not just analytics.
  • Build AI literacy across your team — they need to know what agents can and can't do, when to intervene, and how to escalate.
  • Consider creating an 'Agentic AI Product Manager' role to own and optimize the machine-facing experience.

9. ESTABLISH GOVERNANCE AND FRAUD CONTROLS

  • Set clear escalation thresholds — define at what point a human reviews before an agent acts.
  • Implement audit logging so you can see what an agent did and why.
  • Create override protocols so your team can take back control when needed.
  • Implement fraud detection tuned for AI traffic patterns, not just human behavior.
  • Declare your accepted payment_handlers in your discovery profile so agents know how they can pay before reaching checkout.
  • Use Shared Payment Tokens (SPTs) so agents can initiate purchases without exposing customer credentials.
  • Adopt AP2 (Agent Payments Protocol) mandates — cryptographically signed customer authorizations that cap what an agent may spend on their behalf.
  • Build controls to revoke or restrict specific agent tokens without disrupting human traffic.
  • Set predefined financial thresholds for edge-case transactions.
  • Define system prompts and content filters for any first-party AI agents to keep responses on-brand.

10. FUTURE-PROOF YOUR COMMERCE INFRASTRUCTURE

  • Prepare for Google's Universal Commerce Protocol (UCP), OpenAI's Agentic Commerce Protocol (ACP), and Google's AP2 payments layer — all require clean structured product data and overlap significantly.
  • Apply to Google's UCP-powered checkout (via Google Merchant Center + the UCP integration interest form — currently early access) and register with emerging agent directories. Publishing /.well-known/ucp makes you protocol-discoverable, but Google's shopping surfaces require a separate application.
  • Prepare for agent-to-agent (A2A) commerce, where one AI negotiates or transacts with another on a customer's behalf.
  • Choose platforms natively built for the agentic era (MCP support, active AI partnerships, clear agentic roadmaps) rather than those adding AI as an afterthought.

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