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Implementation·22 min read·2026-02-14

The 47-Point Agent Readiness Audit: Your Complete ACO Checklist for E-Commerce

The definitive AI shopping agent readiness checklist covering entity verification, structured data, API performance, machine-readable policies, UCP readiness, and AI checkout optimization. Interactive progress tracking included.

MS
Michael Salamon

Founder, Agent Commerce Optimization

The 47-Point Agent Readiness Audit: Your Complete ACO Checklist for E-Commerce

Your Agent Readiness Score

Track your progress through this AI shopping agent readiness checklist. Your progress saves automatically in your browser.

0/47
Points Completed
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Agent Readiness Score
Entity Verification0/7
Structured Data0/9
Discovery Manifests0/7
API Architecture0/9
Policy Machine-Readability0/7
Agent Experience (AX)0/8

If You Only Do 10 Things (Start Here)

Overwhelmed? These 10 points give you 70% of the value with the least friction. Focus here first:

#1Verified Google Business Profile#2Merchant Center account health#8Comprehensive Product schema#10OfferShippingDetails implementation#11MerchantReturnPolicy schema#17UCP service manifest (/.well-known/ucp)#18llms.txt foundation file#24API latency optimization#33Structured shipping thresholds#39Final landed cost calculation

Fastest Wins (No Developer Required)

Platform owners can implement these through admin settings alone:

#1 GBP#2 Merchant Center#3 SSL#4 Brand verification#7 Capability sync#11 Return policy#14 GS1 identifiers#18 llms.txt#33 Shipping rules#34 Return windows

By 2030, analysts expect a meaningful share of global commerce will be orchestrated by autonomous AI agents. As AI shopping agents mediate discovery, comparison, and purchase, the merchants who provide the path of least friction will capture disproportionate market share. This agentic commerce readiness audit is your comprehensive UCP readiness checklist for transforming from a traditional e-commerce store to an AI checkout-ready merchant.

Understanding Agent Commerce Optimization (ACO)

Agent Commerce Optimization (ACO) is the systematic process of making your product data, business policies, and transactional capabilities legible, citable, and transactable for AI agents. Unlike traditional SEO, which focuses on human keywords and visual persuasion, ACO prioritizes "retrieval qualification" and the reduction of machine ambiguity.

The foundation of the agentic web rests upon standardized protocols: the Agentic Commerce Protocol (ACP) by OpenAI and Stripe for in-platform checkout, and Google's Universal Commerce Protocol (UCP) for multi-surface discovery and orchestration.

For Non-Technical Merchants: What This Means for You

If you're running a Shopify store or using another e-commerce platform, think of this audit as a health check for how well AI shopping assistants can find, understand, and buy from your store. When someone asks ChatGPT or Google's AI to "find waterproof hiking boots under $150," you want to be the merchant the AI recommends and can actually complete the purchase through.

The 47 points are organized into 6 pillars. Some you can handle yourself through your platform's settings, others may require help from a developer or an ACO specialist. We've marked each point as "Platform Settings," "Developer Required," or "Advanced" to help you understand what's in your control.

The 5-Level Maturity Model

The transition to full agent readiness typically follows a 90-day roadmap structured into five maturity levels. Understanding where you are helps you prioritize the most impactful improvements.

LevelFocusTimelineKey Actions
Level 1Entity VerificationWeeks 1-2GBP verification, Merchant Center setup, domain authentication
Level 2Data ModernizationWeeks 3-6Real-time data access, high-velocity inventory syncing
Level 3Discovery LayerWeeks 7-8llms.txt deployment, UCP manifest publication
Level 4Transactional EndpointsWeeks 9-12API endpoints for inventory, cart, checkout
Level 5Autonomous ScalingOngoingDynamic pricing, B2B negotiation, continuous optimization

The 47-Point Agent Readiness Audit

Pillar 1: Entity Verification and Institutional Trust (7 Points)

AI agents are fundamentally risk-averse. They won't recommend your store if there's any doubt about your legitimacy, pricing accuracy, or fulfillment reliability. This pillar establishes your "Data Moat" with verifiable business identity.

1

Verified Google Business Profile (GBP) Status

Platform Settings

Your GBP serves as the primary source of truth for business identity, providing agents with confirmed phone numbers, physical addresses, and operational hours.

Implementation:

Log into Google Business Profile, complete verification via postcard or phone, ensure all information matches your website exactly.

Why it matters: A verified profile acts as a foundational trust signal in the agentic discovery layer. Without it, agents may flag your business as unverified.
2

Merchant Center Account Health and Standing

Platform Settings

Maintain an active Google Merchant Center account with zero policy violations.

Implementation:

Create or review your Merchant Center account at merchant.google.com, resolve any policy violations, ensure product feed has no disapprovals.

Why it matters: Agents use Merchant Center feeds to verify you're authorized to sell specific products, particularly in regulated industries.
3

Domain Ownership and SSL/TLS Integrity

Platform Settings

Valid SSL certificates and verified domain ownership are non-negotiable security requirements.

Implementation:

Ensure your domain has a valid SSL certificate (look for https:// and the padlock icon). Most platforms like Shopify handle this automatically.

Why it matters: Any certificate error or domain mismatch will cause an agent to terminate connection instantly to protect user security.
4

Brand Verification in Feed Metadata

Platform Settings

Brand identity must be explicitly confirmed within your Merchant Center feed.

Implementation:

In your product feed, ensure the 'brand' field is consistently populated with your official brand name for all products.

Why it matters: This ensures agents can differentiate between authorized retailers and grey-market aggregators.
5

Decentralized Identifier (DID) Integration

Advanced

DIDs facilitate account linking, verifiable credentials, and passkey-based authentication, allowing agents to provide user credentials without password creation.

Implementation:

Work with development team to implement DID support through standards like did:web or did:key. Focus on guest checkout optimization and cross-platform identity linking. Consider passkey integration for passwordless flows.

Why it matters: Significantly reduces friction in guest checkout process, improving agent conversion rates. Enables verifiable credential exchange for age verification and compliance.
6

Persistent Agentic Identity Management

Developer Required

Your database must handle persistent identities for AI agents, allowing continuous relationships.

Implementation:

Configure your platform to store agent IDs alongside customer records, enabling agents to query order status or initiate returns.

Why it matters: Enables agents to provide post-purchase services like order tracking and automated returns without human intervention.
7

Merchant Center 'Capability Command Center' Sync

Platform Settings

Synchronize operational capabilities like same-day delivery or specific return logistics with Merchant Center.

Implementation:

In Merchant Center, navigate to Shipping & Returns settings, configure all available delivery options and return policies with accurate timelines.

Why it matters: Allows agents to filter for merchants based on specific user-imposed delivery constraints like 'must arrive by Friday.'

Pillar 2: Semantic Depth and Advanced Structured Data (9 Points)

Schema.org markup is the primary language through which your products communicate with AI agents. Deep structured data is the prerequisite for "retrieval qualification."

8

Comprehensive Schema.org Product Audit

Developer Required

Beyond basic names and prices, verify the presence of GTIN, SKU, and brand attributes in JSON-LD markup.

Implementation:

Review your product page source code for JSON-LD schema. Ensure gtin, sku, mpn, and brand are populated for all products. Use Google's Rich Results Test to validate.

Why it matters: Without canonical identifiers, agents cannot accurately compare your offering against the broader market.
9

Real-Time Inventory Level Markup

Developer Required

Use the inventoryLevel node within JSON-LD to allow agents to prioritize merchants with guaranteed stock.

Implementation:

Add 'offers.inventoryLevel' property to product schema with actual quantity or availability status. Sync in real-time with your WMS.

Why it matters: Risk-averse agents prioritize merchants who can guarantee stock. High-velocity syncing ensures 100% accuracy.
10

Advanced OfferShippingDetails Implementation

Developer Required

Shipping schema must include complete matrix with shippingRate and deliveryTime for specific regions.

Implementation:

Implement OfferShippingDetails schema with shippingDestination, shippingRate, deliveryTime, and transitTimeValue for each region you serve.

Why it matters: Agents require this to calculate 'final landed cost' before committing to a transaction.
11

MerchantReturnPolicy Organization Schema

Platform Settings

Publish return policy schema at the organization level on your site and validate it in Search Console.

Implementation:

In your site's Organization schema (typically in header/footer), add MerchantReturnPolicy with returnPolicyCategory, returnMethod, returnFees, and merchantReturnDays. Use Google's Rich Results Test to validate.

Why it matters: Provides agents with machine-readable understanding of return windows, methods, and fees for risk evaluation.
12

Structured Variant Attribute Mapping

Developer Required

All product variants (size, color, material) must map to standardized fields.

Implementation:

Use PropertyValue schema for variants. Map colors to standardized names or HSL codes, sizes to industry standards (S/M/L or numeric), materials to fabric composition percentages.

Why it matters: Eliminates need for agents to guess variant compatibility, enabling accurate filtering and comparison.
13

PotentialAction Actionable URLs

Developer Required

Schema must include potentialAction properties with direct URLs for cart initialization or checkout.

Implementation:

Add potentialAction to Product schema with ViewAction (product page), BuyAction (add to cart URL), and OrderAction (direct checkout URL) with proper urlTemplate values.

Why it matters: Allows agents to bypass traditional UI discovery and move directly to execution.
14

GS1 Product Identifier Compliance

Platform Settings

Use industry-standard GS1 identifiers to ensure perfect legibility to cross-merchant comparison engines.

Implementation:

Obtain GTINs from GS1 for your products. Add to product data feeds and schema. Avoid making up or reusing GTINs from other products.

Why it matters: Inconsistent identifiers are treated as 'noise' and reduce ranking in the retrieval set.
15

Semantic HTML5 Page Hierarchy

Developer Required

Use proper HTML5 tags and logical heading hierarchy for headless browser parsing.

Implementation:

Ensure product pages use

,
, and
tags. Use H1 for product name, H2 for main sections (description, specs, reviews), H3 for subsections.

Why it matters: Makes it easier for agents to understand content context when APIs are unavailable.
16

ARIA Label Optimization for Interaction

Developer Required

Add ARIA labels to interactive elements to provide context about button/form field functions.

Implementation:

Add aria-label or aria-labelledby to buttons ('Add to Cart,' 'Proceed to Checkout'), form fields ('Shipping Address,' 'Card Number'), and interactive elements.

Why it matters: Critical for agents performing screen-scraping or browser-emulation tasks.

Pillar 3: Machine-Readable Discovery Manifests (7 Points)

The "AI Identity" of your brand is defined by the manifest files you host at your domain root. These files provide the "Rules of Engagement" for AI agents.

Note on Emerging Conventions

Some files below (ai.txt, brand.txt, developer-ai.txt, ai.json) are emerging conventions, not formal standards yet. Prioritize llms.txt + sitemap + schema + feeds as your core foundation. Use the additional files when helpful for your specific use case. The UCP manifest is an established open standard.

17

The /.well-known/ucp Service Manifest

Developer Required

This JSON file is the cornerstone of the Discovery Layer, declaring your capabilities, supported regions, and API endpoints.

Implementation:

Create a JSON file at yourdomain.com/.well-known/ucp with serviceType, capabilities (inventory, cart, checkout), supportedRegions, and authentication requirements. Reference UCP implementation guide.

Why it matters: Essential for UCP-compliant agent discovery. Without this, agents cannot find your transactional endpoints.
18

The llms.txt Foundation

Platform Settings

Curated content map highlighting important pages for AI systems in a quickly-parsable format.

Implementation:

Create llms.txt at domain root with: site description, key pages (products, policies, FAQs) as markdown list, attribution guidelines. See llmstxt.org for spec.

Why it matters: Often the first point of contact for an LLM seeking to understand your brand's core offerings.
19

The ai.txt Behavioral Guidelines (Optional)

Platform Settings

Emerging convention providing behavioral rules defining when AI should recommend your service and how to attribute your brand.

Implementation:

Create ai.txt file with sections for: Allowed Usage, Attribution Requirements, Prohibited Usage, and Contact Info for agent developers. This is experimental—prioritize llms.txt first.

Why it matters: Ensures when your brand is cited, it's described correctly and linked to authoritative sources.
20

The brand.txt Identity Reference (Optional)

Platform Settings

Emerging convention establishing concise brand identity including official name, legal entity, and name variations.

Implementation:

Create brand.txt with: Official Brand Name, Legal Entity, Acceptable Variations, Prohibited Variations, and Brand Guidelines URL. Consider if brand disambiguation is a priority for your business.

Why it matters: Collapses ambiguity in brand mentions across AI platforms.
21

The developer-ai.txt Technical Path (Optional)

Developer Required

Emerging convention providing API documentation endpoints, rate limiting info, and programmatic discovery paths for specialized agents.

Implementation:

Create developer-ai.txt with: API Documentation URL, Rate Limits, Authentication Methods, Sitemap URLs, and OpenAPI/Swagger spec links. Most useful for API-first businesses.

Why it matters: Critical for developers of specialized shopping agents seeking to build deep integrations.
22

The ai.json Structured Data Layer (Optional)

Developer Required

Emerging convention providing programmatic data layer for agents that prioritize JSON-LD or XML formats over markdown.

Implementation:

Create ai.json with same information as llms.txt but in JSON format with: siteInfo, capabilities, products array, policies object. Use if you have JSON-first processing needs.

Why it matters: Provides alternative ingestion path for agents with JSON-first processing pipelines.
23

Agent Allow List and Robots.txt Optimization

Platform Settings

Ensure robots.txt explicitly allows legitimate agents while blocking non-compliant scrapers.

Implementation:

Edit robots.txt to add: User-agent: GPTBot / Allow: /, User-agent: Google-Extended / Allow: /, User-agent: ChatGPT-User / Allow: /, User-agent: Claude-Web / Allow: /.

Why it matters: Prioritizing AI web crawlers ensures your content is available for agent training and real-time retrieval.

Pillar 4: API Architecture and Performance (9 Points)

Agentic commerce moves at the speed of computation. If your API is slow or inconsistent, agents will move to more performant competitors.

24

API Response Latency (<200ms Target)

Developer Required

For agent-mediated flows, sub-second latency becomes table stakes. <200ms is an aggressive target for critical endpoints.

Implementation:

Use monitoring tools (New Relic, Datadog) to track API endpoint performance. Optimize database queries, implement caching, use CDN for static assets. Focus on inventory and pricing endpoints first.

Why it matters: Agents prioritize fastest merchants to ensure seamless user experience. While <200ms is ambitious, aim for consistent sub-500ms across all transactional endpoints.
25

Real-Time Stock Check (GET /inventory/{sku})

Developer Required

Dedicated endpoint for real-time inventory and pricing without page simulation.

Implementation:

Create GET endpoint at /api/inventory/{sku} returning: { sku, inStock (boolean), quantity, price, currency, lastUpdated (timestamp) }.

Why it matters: Provides accurate stock levels and current pricing, essential for agent decision-making.
26

Cart Validation (POST /cart/validate)

Developer Required

Endpoint allowing agents to test cart validity before payment commitment.

Implementation:

Create POST endpoint accepting: items array, shippingAddress, returning: isValid (boolean), totalPrice, tax, shipping, errors array, estimatedDelivery.

Why it matters: Includes address validation, tax calculation, and stock confirmation to prevent checkout failures.
27

Checkout Session Creation (POST /checkout/create)

Developer Required

API support for initializing checkout session with temporary inventory allocation.

Implementation:

Create POST endpoint that: creates session ID, holds inventory for 30 minutes, calculates final pricing, returns sessionToken and expiresAt timestamp.

Why it matters: Prevents overselling during agent checkout process.
28

Checkout Completion (POST /checkout/complete)

Developer Required

Endpoint finalizing payment via provider like Stripe and generating order.

Implementation:

Create POST endpoint accepting: sessionToken, paymentToken (from Stripe/PayPal), returning: orderId, orderStatus, confirmationEmail, trackingInfo (if available).

Why it matters: Final transactional handshake in the agentic commerce funnel.
29

HMAC-SHA256 Signature Verification

Advanced

Cryptographically sound authentication for agent-to-merchant traffic.

Implementation:

Implement HMAC signature verification on API endpoints. Share secret keys with verified agent platforms. Validate signature on each request.

Why it matters: Faster and more secure than stateful OAuth2 for programmatic interactions.
30

Headless and Composable IT Architecture

Advanced

Shift toward headless models where primary interface is API, not visual storefront.

Implementation:

Consider platforms like Shopify Hydrogen, BigCommerce Catalyst, or custom headless solutions with separate frontend and backend.

Why it matters: Allows for 100% real-time accuracy and event-driven logistics.
31

Inventory Syncing Velocity

Developer Required

Real-time connection between e-commerce platform and Warehouse Management System.

Implementation:

Implement webhook-based inventory updates. When WMS updates quantity, trigger immediate update to product database and Merchant Center feed.

Why it matters: Agents are intolerant of 'out of stock' errors post-checkout. Real-time sync is essential.
32

API Key Management and Whitelisting

Developer Required

Robust framework for managing API keys and whitelisting trusted agents.

Implementation:

Implement API key generation system. Maintain whitelist of verified agent platforms (verified through registries like Web Bot Auth). Rate limit by key.

Why it matters: Prevents abuse while ensuring legitimate agents have reliable access.

Pillar 5: Policy Machine-Readability and Constraints (7 Points)

AI agents resolve "state trees" to find the path of least friction. If business logic is buried in PDF policies, agents will fail the transaction.

33

Structured Shipping Thresholds

Platform Settings

Convert prose-based shipping rules into data-driven constraints.

Implementation:

In your platform settings, define clear shipping tiers: under $50 = $5.99, $50-99 = $3.99, $100+ = Free. Add to schema as OfferShippingDetails with eligibleTransactionVolume.

Why it matters: Agents need to know exactly when 'Free Shipping' triggers to weigh trade-offs against other merchants.
34

Machine-Readable Return Windows

Platform Settings

Define return windows as programmatic parameters.

Implementation:

In MerchantReturnPolicy schema, set merchantReturnDays: 30, returnMethod: 'ByMail', returnFees: 'FreeReturn'. Make this data available via API.

Why it matters: Allows agents to verify compliance with user's risk profile before purchase.
35

Warranty and Guarantee Parameters

Platform Settings

Deconstruct warranty conditions into structured data.

Implementation:

Use WarrantyPromise schema with: durationOfWarranty (P1Y for 1 year), warrantyScope (description), and warrantyCoverage (product coverage details).

Why it matters: Defines specific steps and durations for coverage in machine-readable format.
36

Geographical Order Disqualifiers

Platform Settings

Clearly code order disqualifiers like PO Box exclusions or international shipping limits.

Implementation:

In shipping settings, explicitly list: excluded regions, PO Box policy, APO/FPO support. Add to OfferShippingDetails with eligibleRegion and ineligibleRegion.

Why it matters: Prevents agents from reaching 'dead ends' late in the checkout journey.
37

Age-Gated and Compliance Metadata

Developer Required

Machine-readable tags for products with legal restrictions.

Implementation:

Add 'ageRequirement' property to Product schema (e.g., 'P21Y' for 21 years). Include compliance flags for hazmat, alcohol, tobacco.

Why it matters: Ensures agents follow necessary verification protocols for restricted products.
38

Deterministic Variant Selection Logic

Developer Required

Variant options must be deterministic, not ambiguous.

Implementation:

Structure variants as: Required selections (size, color) with clear options; Optional add-ons clearly marked; Incompatible combinations explicitly defined in schema.

Why it matters: Ambiguity in variant selection is a leading cause of agent transaction abandonment.
39

Final Landed Cost Calculation

Developer Required

Provide final total including taxes and shipping through API before checkout.

Implementation:

Create endpoint that accepts: items, shipping address, returns: subtotal, shipping, tax, total. Must not require UI interaction to calculate.

Why it matters: Deterministic pricing independent of UI triggers is essential for agent confidence.

Pillar 6: Agent Experience (AX) and Success Metrics (8 Points)

Measuring agentic commerce effectiveness requires departure from traditional KPIs like CTR or page views.

40

Agentic Trust Score (ATS) Optimization

Advanced

The most critical metric in 2026, measuring reliability of pricing, fulfillment data, and reputation as perceived by AI agents.

Implementation:

Monitor: pricing consistency across channels, fulfillment promise accuracy, return rate, product data quality score. Work with ACO partners to track your ATS.

Why it matters: ATS determines whether agents confidently recommend your store or skip it for more reliable merchants.
41

Prompt-Space Occupancy Score (PSOS)

Advanced

Measures share of voice your brand occupies in LLM responses across platforms.

Implementation:

Use tools to track brand mentions across ChatGPT, Gemini, Claude, Perplexity for key product queries. Calculate percentage of relevant queries where you're mentioned.

Why it matters: High PSOS indicates you're a primary 'retrieval candidate' when agents research your product category.
42

Agent Hit Rate and Traffic Analysis

Platform Settings

Track percentage of total traffic originating from verified AI agents.

Implementation:

In Google Analytics 4, create segment for user agents: ChatGPT-User, GPTBot, Claude-Web, Google-Extended. Monitor traffic trends and conversion rates.

Why it matters: Growing hit rate indicates successful ACO and protocol compliance.
43

Agent Checkout Completion vs. Abort Rates

Developer Required

Instrument tracking of where agents drop out of the funnel.

Implementation:

Add logging to checkout API endpoints. Track: sessions started, cart validations, checkouts initiated, checkouts completed, abort reasons.

Why it matters: High abort rates at payment handshake indicate protocol misalignment.
44

Semantic Form Field Standardization

Developer Required

Form fields must use standard semantic names for headless browsing agents.

Implementation:

Use autocomplete attributes: autocomplete='name', autocomplete='email', autocomplete='address-line1', autocomplete='cc-number', autocomplete='cc-exp'.

Why it matters: Obscure custom IDs make it difficult for agents to fill forms correctly.
45

Implementation of Web Bot Auth

Advanced

Distinguish legitimate agents from malicious bots, allowing removal of aggressive CAPTCHAs.

Implementation:

Integrate with Web Bot Auth service. Verify agent signatures against registry. Remove CAPTCHAs for verified agents.

Why it matters: Confirms agent follows network rules and improves Agent Experience by reducing friction.
46

Agent Payments Protocol (AP2) Readiness

Advanced

Support secure, cryptographic payment handshakes without exposing card numbers.

Implementation:

Integrate with Stripe or payment provider supporting AP2. Accept cryptographically signed payment mandates. Never request raw card details from agents.

Why it matters: Allows agents to initiate purchases while maintaining payment security.
47

Post-Purchase API Accessibility

Developer Required

Order management system accessible via API for post-purchase agent operations.

Implementation:

Create endpoints for: GET /orders/{id} (order status), GET /orders/{id}/tracking (shipment tracking), POST /orders/{id}/return (initiate return), GET /orders/{id}/invoice.

Why it matters: Enables agents to perform 'post-purchase journeys' like tracking shipments or initiating automated returns.

Canonical Sources: Entity Graph for Agent Trust

AI agents don't just read your website—they cross-reference your business across multiple authoritative sources to verify legitimacy and build trust. This "entity graph" of canonical sources is critical for retrieval qualification.

Canonical Sources Agents Trust

Business Identity
  • Google Business Profile (verified)
  • Google Merchant Center account
  • Wikidata entry (if applicable)
  • LinkedIn company page
  • Crunchbase profile (if applicable)
Transactional Authority
  • Major marketplace presence (Amazon, eBay if applicable)
  • Returns policy page (yoursite.com/returns)
  • Shipping information page
  • Support/contact page with verified phone/email
  • Privacy policy with clear data handling

Implementation: Use Schema.org sameAs property in your Organization schema to link all these canonical sources. Agents cross-reference these to verify your business legitimacy and build a trust score.

Priority Framework for Non-Technical Merchants

Feeling overwhelmed? Here's how to prioritize if you're a Shopify or similar platform user:

Start Here (You Can Do This):

  • Points 1-4, 7, 11: Verify Google Business Profile, set up Merchant Center, ensure SSL is active, configure shipping & return policies in your platform
  • Points 18-20, 23: Create simple llms.txt and ai.txt files (basic text files following the guides)
  • Points 33-36: Structure your shipping rules clearly, define return windows, add warranty info

Get Developer Help For:

  • Points 8-16: Schema.org implementation, product markup, structured data
  • Points 17, 21-22: UCP manifest, developer-ai.txt, ai.json files
  • Points 24-32: API development for inventory, cart, checkout

Consider ACO Specialist For:

  • Points 5-6, 29-30: Advanced identity management, headless architecture
  • Points 40-47: Agentic Trust Score optimization, AP2 integration, comprehensive agent analytics

Need help implementing this audit? Our Agent Readiness Audit service provides a prioritized roadmap with difficulty scoring and ROI projections for your specific situation.

The Business Imperative of Agent Readiness

By the end of 2026, the digital storefront will no longer be a destination—it will be a data source. The shift from Human-to-Browser to Agent-to-Agent commerce represents a fundamental reset in e-commerce power dynamics.

Implementing this 47-point audit is not merely a technical exercise; it's a strategic defense against "Agent Invisibility." Merchants who provide the path of least friction for AI agents will capture the projected 25% of global commerce expected to be driven by autonomous systems by 2030.

The Agentic Trust Score will become the most valuable asset on your balance sheet—representing the quantifiable reliability of your digital infrastructure in a world where software does the shopping.

Ready to Make Your Store Agent-Ready?

Whether you're a Shopify merchant looking to start with the basics or an enterprise needing full protocol implementation, we can help you navigate this transition.

Request Agent Readiness Audit →Discuss Your Situation