Zeodyn Score Explained: What It Means and How to Improve

AI agents are already shopping. Google's AI Mode, ChatGPT shopping, and Amazon's Buy for Me are live, and every major platform is building more. If you're new to agent commerce, the shift is simple: AI agents now discover, evaluate, and purchase products autonomously. The question is no longer whether they will interact with your online store — it is whether they can.

The Zeodyn Score™ is a composite metric from 1 to 100 that measures how ready your website is for AI agent commerce. It assesses whether AI agents can discover your products, understand your catalogue, evaluate your offers, transact programmatically, and trust your infrastructure. This guide explains exactly how the score works, what each band means, where most sites fall short, and what you can do to improve.

The five score bands

Every Zeodyn Score falls into one of five bands. Each band represents a meaningful threshold in how AI agents can interact with your commerce capabilities.

Agent-Ready (90–100)

Your site is fully accessible to AI agents across all dimensions. Agents can discover your products, read structured data, evaluate offers, initiate transactions through supported protocols, and verify your legitimacy — all programmatically. Sites in this band are positioned to capture the full value of agentic commerce as it scales.

Strong Foundation (70–89)

Your site supports AI agent commerce well, with most interactions likely to succeed. There are improvements possible — typically one or two dimensions holding the score back — but the fundamentals are solid. Sites here are ahead of the vast majority of e-commerce businesses.

Developing (50–69)

AI agents can discover your site but struggle with evaluation, comparison, or transactions. This is where most technically competent e-commerce sites land today. The data is partially there, but gaps in structured markup, protocol support, or commerce signals prevent agents from completing their tasks reliably.

Limited (25–49)

Significant gaps prevent meaningful agent interactions. AI agents might find your site but cannot extract the product data, pricing, or trust signals they need to recommend or transact. Sites in this band are effectively invisible to agent-driven commerce.

Not Ready (1–24)

AI agents cannot meaningfully interact with your commerce capabilities. This typically means the site actively blocks AI crawlers, lacks machine-readable data entirely, or has critical infrastructure failures like missing HTTPS. Immediate action is needed.

The six scoring dimensions

The Zeodyn Score is built on the Agent Commerce Stack™ framework, which evaluates your site across six dimensions. Each dimension answers a specific question about your AI commerce readiness.

1. Discovery & Access

Can AI agents find and access your commerce capabilities?

This dimension checks whether AI agents are permitted to crawl your site, whether your content is discoverable via sitemaps and well-known endpoints, and whether your robots.txt policy allows the major AI user agents (GPTBot, ClaudeBot, GoogleBot, AmazonBot, PerplexityBot, and others) to access your pages. A site that blocks all AI agents at the front door scores poorly here, regardless of how good its product data is.

Weight: High

2. Structured Data

Can AI agents understand your products in machine-readable form?

This is about JSON-LD schema markup — specifically, whether your pages contain Schema.org Product and Offer types with the fields AI agents need: name, description, price, availability, images, brand, SKU, and GTIN identifiers. Pages without structured data force agents to scrape and interpret raw HTML, which is unreliable and slow.

Weight: Very high

3. Commerce Data

Can AI agents trust your operational data for transactional decisions?

Beyond basic product information, agents need operational signals to make purchasing decisions: real-time pricing, stock availability, shipping information, return policies, and payment methods. This dimension assesses whether that transactional data is present and machine-readable.

Weight: High

4. Protocol Support

Can AI agents programmatically transact with your commerce infrastructure?

This dimension evaluates support for agent commerce protocols — UCP (Universal Commerce Protocol), ACP (Agentic Commerce Protocol), MCP (Model Context Protocol), and OpenAPI specifications — as well as platform-level integrations (Shopify, WooCommerce, BigCommerce). Sites with protocol support give agents a structured, reliable way to browse, negotiate, and purchase. Without protocols, agents must rely on fragile HTML scraping.

Weight: High

5. Security & Trust

Can AI agents verify your legitimacy and operate safely?

Agents need to confirm that a site is legitimate before recommending it or processing transactions. This dimension checks HTTPS enforcement, security headers (HSTS, CSP, X-Content-Type-Options), privacy policies, contact information, and business verification signals. A site without HTTPS is fundamentally untrusted.

Weight: Moderate

6. Technical Performance

Can AI agents parse your pages quickly and efficiently?

AI agents are not patient browsers. If your pages take seconds to load, rely entirely on client-side JavaScript rendering, or return excessively large payloads, agents will time out or move on. This dimension measures response times, server-side rendering, page weight, and content efficiency.

Weight: Moderate

How the score is calculated

Geometric aggregation — not a simple average

The Zeodyn Score uses a weighted geometric mean to combine the six dimension scores into a single composite. This is the same mathematical approach used by the UN Human Development Index.

Why does this matter? With a simple arithmetic average, a site could score 100 in five dimensions and 10 in one, and still end up with a respectable average of 85. But in reality, that single weak dimension would completely block agent commerce. An agent that cannot discover your site (dimension 1) will never reach your beautifully structured product data (dimension 2).

Geometric aggregation prevents this compensatory effect. Weakness in any single dimension pulls the entire score down disproportionately. A chain is only as strong as its weakest link, and the same is true for AI agent commerce readiness.

The dimensions carry different qualitative weights — Structured Data carries Very high importance, Discovery & Access, Commerce Data, and Protocol Support carry High importance, while Security & Trust and Technical Performance carry Moderate importance. The exact numeric weights are proprietary, but the qualitative labels are published with every scan result so you know where to focus.

Fail gates — hard floors on critical requirements

Some requirements are so fundamental that failing them caps your dimension score regardless of how well you perform on other sub-checks. These are called fail gates.

Current fail gates include:

• No HTTPS — caps the Security & Trust dimension. Without encrypted connections, no agent can safely transact.
• Blocking all AI agents via robots.txt — caps the Discovery & Access dimension. If agents cannot crawl your site, nothing else matters.
• Active AI blocking with no programmatic alternative — caps Protocol Support. If you block agents and offer no API or protocol endpoint, agents have no way to interact.
• No detectable pricing — caps Commerce Data. Without price information, agents cannot make transactional decisions.
• No Product or Offer schema — caps Structured Data. Without any schema markup, agents cannot parse your catalogue.
• Pure client-side SPA with no server-rendered content — caps Technical Performance. If the initial HTML response contains no meaningful content, most agent crawlers will see an empty page.

When a fail gate is triggered, it is flagged in your scan results alongside the reason. This makes it immediately clear which critical issues need addressing first.

Real-world examples

To illustrate how the scoring works in practice, here are Zeodyn Scores™ from scans of major e-commerce sites (scores as of February 2026; results may change as sites evolve). These scores reflect what the scanner observes at the website layer.

amazon.com — 6 (Not Ready)

Amazon has possibly the world's largest product catalogue, with rich operational data on pricing, availability, and shipping. Yet it scores just 6. Amazon's robots.txt blocks key AI agents — GPTBot, ClaudeBot, and PerplexityBot are all disallowed — and the homepage contains zero JSON-LD structured data. No Product schema. No Offer schema. No price detectable in markup. The page is server-rendered (Technical Performance scores 71), but without machine-readable product data, agents have nothing to work with. The geometric aggregation does what it is designed to do: Amazon's commerce capabilities cannot compensate for the fact that agents can neither access nor parse them.

etsy.com — 9 (Not Ready)

Despite being an early ACP adopter, Etsy's website scores just 9 on a standard crawl. Here's the twist: Etsy's robots.txt actually allows all six AI agents. But the server returns HTTP 403 responses, blocking access at the infrastructure level. On top of that, the page is a client-side SPA with almost no server-rendered content (Technical Performance: 30). Structured Data scores just 11 — no JSON-LD at all, zero schema markup. Three fail gates fire simultaneously: Structured Data (no Product or Offer schema), Commerce Data (no detectable pricing), and Technical Performance (pure SPA) — the only benchmark site to trigger all three. The ACP integration works at the API layer (accessed via ChatGPT), not at the website layer. That gap between API capability and website readiness is exactly what the Zeodyn Score™ measures.

walmart.com — 31 (Limited)

Walmart scores 31 — the second-highest in this group, but still Limited. The site has Organization and WebSite JSON-LD, good Open Graph tags, and strong technical performance (94). Walmart also publishes llms.txt and llms-full.txt discovery files. But there is no Product schema on the pages scanned, and protocol support is nearly absent (11) — no UCP, no ACP, no MCP. Walmart has built a fast, accessible site that agents can find. They just cannot transact with it yet.

shopify.com — 39 (Limited)

Shopify's own marketing site scores 39. This might seem surprising for the company co-developing UCP, but it makes sense: shopify.com is a marketing site, not a storefront. It has Corporation schema, strong OG tags, and Twitter Cards, but no Product or Offer schema — triggering a Structured Data fail gate that caps the dimension at 20 despite those other signals. Technical Performance is solid (87) and all AI crawlers are allowed. Protocol Support (32) drags the score further — no UCP manifest on its own domain. Shopify merchants will benefit from UCP when it ships. shopify.com itself is not a commerce destination for AI agents. (For a full breakdown of Shopify's strengths and gaps, see our Shopify AI agent readiness guide.)

Every site in this list has strong commerce operations. None scores above 39. Having great products is not the same as being AI-agent ready. The Zeodyn Score™ measures whether agents can access your commerce capabilities — not whether those capabilities exist.

Common failure points

Across the sites we have scanned, clear patterns emerge. These are the most frequent reasons sites score poorly:

1. Missing JSON-LD structured data — The single most common issue. Without Schema.org Product and Offer markup, AI agents cannot reliably extract product information. Many sites rely on microdata or have no structured data at all.

2. Blocking AI bots in robots.txt — Many sites added blanket blocks for GPTBot, ClaudeBot, and other AI user agents during the 2023–2024 AI scraping concerns. These blocks now prevent legitimate agent commerce interactions.

3. No agent commerce protocols — UCP, ACP, and MCP are new, and adoption is still early. Most sites have no /.well-known/ucp endpoint, no ACP integration, and no OpenAPI specification. This dimension drags down many otherwise strong sites.

4. Missing HTTPS or poor security headers — Less common on established sites, but still appears on smaller merchants and older platforms. Missing HTTPS triggers a fail gate.

5. Slow response times and client-side rendering — Single-page applications that render entirely in JavaScript are invisible to most agent crawlers. Similarly, pages that take more than a few seconds to respond will be skipped.

Five quick wins to improve your score

If you have scanned your site and want to improve, these are the highest-impact actions you can take today, roughly ordered by effort and return.

1. Add JSON-LD Product schema to your product pages

This is the single most impactful change for most sites. Add a <script type="application/ld+json"> block to each product page with Schema.org Product markup including name, description, price, currency, availability, image, and brand. If you are on Shopify, WooCommerce, or BigCommerce, your platform likely has plugins or built-in support for this.

2. Review your robots.txt AI agent policy

Check whether your robots.txt blocks GPTBot, ClaudeBot, GoogleBot, or other AI user agents. If you added blanket blocks, consider selectively allowing access — particularly if you want AI agents to discover and recommend your products. You can block training-specific crawlers while still permitting commerce-oriented agents.

3. Publish a UCP discovery endpoint

Create a /.well-known/ucp file on your domain that describes your commerce capabilities. Even a minimal endpoint signals to AI agents that you are open for agentic commerce. Google's UCP documentation provides the schema.

4. Ensure HTTPS is enforced everywhere

If any part of your site serves content over HTTP, fix it. HTTPS is a fail gate — without it, your Security & Trust dimension is capped and the geometric aggregation drags your entire score down. Ensure your server redirects all HTTP requests to HTTPS and sends an HSTS header.

5. Add operational commerce data to your markup

Beyond basic product information, include availability status, shipping estimates, return policy links, and accepted payment methods in your structured data. This strengthens your Commerce Data dimension and gives agents the transactional confidence they need to recommend your site.

Dive deeper

The Zeodyn Score is built on the Agent Commerce Stack™ v1.0 framework, which runs 54 individual sub-checks across the six dimensions. For the full technical methodology — including how sub-checks are scored, how scoring curves are applied, and how the framework was developed — see the methodology page.

Every scan produces a detailed breakdown showing your score in each dimension, which sub-checks passed or failed, which fail gates were triggered, and prioritised recommendations for improvement. You can scan any publicly accessible URL for free.

Get your Zeodyn Score

AI agent commerce is not a future trend — it is happening now. Google, OpenAI, Amazon, and every major platform are building agent shopping experiences. The businesses that prepare their infrastructure will capture this new channel. Those that do not will be invisible.

Find out where you stand. Scan your site and get your Zeodyn Score.