How we measure Agent Citability

As AI systems — large language models, autonomous agents, shopping assistants, and search overviews — become primary intermediaries between consumers and businesses, traditional SEO metrics no longer capture the full picture of digital visibility.

A site can rank well in Google search results and still be effectively invisible to AI-driven discovery. The reasons are structural: AI systems evaluate content differently from keyword-matching algorithms. They assess whether content is machine-readable, structurally coherent, semantically clear, demonstrably authoritative, and fresh enough to be reliable.

The Agent Citability Index was developed to make these signals measurable. A high ACI score indicates that a site is well-positioned to appear in AI-generated responses, be cited in LLM outputs, and transact with AI-driven commerce agents. A low score reveals specific, fixable gaps in how the site presents itself to non-human systems.

Each page or site is evaluated across six independent dimensions. Scores for each dimension range from 0 to 100. The composite ACI score is a weighted combination of all six, calibrated against observed citation behaviour across major AI systems.

The composite ACI score runs from 0 to 100. Scores are normalised against observed performance across a continuously updated reference set. The bands below reflect meaningful thresholds in real-world citability outcomes.

In April 2026, COVEN AI conducted an ACI analysis of 500 Shopify-powered e-commerce sites spanning multiple industries and regions. The dataset represents a cross-section of active online retailers and provides the most comprehensive public benchmark of agent citability for e-commerce currently available.

The results reveal that the majority of online businesses are significantly underprepared for AI-driven discovery — a gap that will widen as agentic commerce adoption accelerates.

The mean score of 29.4 places the average Shopify store in the Critical band. The primary drivers of low scores are weak Schema markup, absent freshness signals, and minimal E-E-A-T infrastructure — all of which are addressable with targeted effort.

ACI scores are derived from live page analysis conducted by COVEN AI's scanning infrastructure at the time of assessment. Scores reflect the state of a site at scan time and will change as the site evolves. Scores are not cached indefinitely — monitoring subscribers receive updated scores on a regular cadence.

The analysis draws on signals from the publicly accessible version of each page as seen by a standard web client, structured data validators, crawl behaviour logs (for Agent Correlation), and heuristic evaluation of content quality signals. No proprietary or authenticated data is used; ACI scores reflect only what AI systems themselves can observe.

Industry benchmark data is aggregated and anonymised. Individual site scores are not disclosed in public reports.

Because ACI scores are decomposed into six independent dimensions, improvement is systematic rather than speculative. The highest-impact actions are almost always in the lowest-scoring dimensions — and the dimensions with the most room to improve tend to be Schema, Freshness, and E-E-A-T, as the Shopify benchmark data confirms.

Structure is consistently the most surprising high-impact area. Pages with genuinely good content often score poorly on structure because of invisible markup issues: FAQ sections built with <details> accordions register zero headings to an AI agent; section titles styled as <p> tags are indistinguishable from body copy; comma-separated item lists score nothing where a <ul> would score full points. These are low-effort fixes with outsized score impact. Read the full breakdown →

Run a free scan to see your site's current scores across all six dimensions, or commission a full Agent Readiness Audit for a prioritised action plan with copy-paste implementation guidance.