Can an AI agent find you? 130 Romanian domains · 12 machine-verifiable signals · ADO Score 0–100 after the Exista.io framework · 0 Agent Cards · mean score 17 · 8 September 2026 Until now, this series has measured one thing: when a person asks ChatGPT or Gemini "where do I get a good laptop", which brands come back in the answer.
The person reads, the person chooses.
But a second kind of customer is arriving.
An AI agent receives an objective ("find a supplier of corporate jewelry for 200 gifts, under a given budget, with an invoice") and solves it on its own.
It does not search Google and does not read marketing copy.
It requests configuration files at standardised paths, checks structured data, cross-references sources and, if it cannot find what it needs in a format it can process, moves on to the next candidate.
The first customer is persuaded; the second is verified.
This is the fifth episode in the series measuring how Romanian markets appear in front of artificial intelligence.
The first three measured the human layer: jewelry, books, electronics.
The fourth measured a technical artefact, .
This one measures the next layer, the agents', using the framework Exista.io published in February 2026 under the name AI Visibility Stack: whether an autonomous agent can find, evaluate and select a Romanian company without a human stepping in.
The paper proposes a metric for this, the ADO Score, and makes a prediction: most companies will score near zero, not because they are weak, but because the necessary artefacts are not yet part of standard practice.
We tested the prediction on Romania.
Two changes of method from the previous episodes, stated up front.
We did not query models; we probed websites: every figure below comes from an HTTP request or a Wikidata query, reproducible with the published script.
And we did not build a new sample: we took exactly the brands the AI engines recommended to people in episodes 1–3, plus the marketing agencies from episode
4.
That is, the companies that have already won layer 1 and the companies that sell AI visibility.
If anyone is ready for agents, it should be them.
In short — what we found Zero Agent Cards.
None of the 126 domains with an HTTP response serves , the file the paper calls "the functional equivalent of a website for agents".
Nor on the legacy path, .
The dimension with the largest weight in the ADO Score, 30 points, is empty across the board.
The mean ADO Score is 17 out of 100; the maximum is
35.
Nobody leaves the paper's "minimal visibility" band (0–20) by more than a little: 55 domains sit between 21 and 40, 61 between 1 and 20, 9 at exactly zero.
Above 40 there is no one.
All the score comes from old SEO.
Trust signals (JSON-LD, Wikidata) and sitemap freshness bring, on average, 12 of the 17 points.
Interoperability brings 0.3 of
15.
Whoever does well does well for reasons unrelated to agents.
The interoperability that exists comes from the platform, not from strategy. 8 domains serve MCP authorisation discovery (): 3 are Shopify stores (the protocol ships with the platform), 4 run a WordPress plugin () and exactly one built its own server.
A single domain out of 130, snsys.ro, an IT firm, wrote a manifest for agents by hand.
The agencies selling AI visibility do slightly better than their clients (17.9 vs 15.9 points), exclusively through (66% vs 29%) and JSON-LD (80% vs 59%).
On Agent Cards, zero as well.
Layer 1 transfers weakly to layer
2.
Across the 41 brands, the Spearman correlation between visibility in AI answers (ep. 1–3) and the ADO Score is 0.31.
The top 10 brands by AI visibility have a mean ADO of 17.2; the rest, 15.4. eMAG, the most visible brand in the series (94% of answers), scores
22.
Wikidata is the signal that separates brands from agencies: 12 of 41 brands have an entity with an official website declared; 3 of 85 agencies.
But of the 4 domains that have both Wikidata and JSON-LD, only 3 carry the same name in both.
The closed door: 6 domains answer a JavaScript-free request with 403, 503, 406 or an anti-bot challenge, among them 4 of the 13 electronics retailers the engines recommend (Flanco, PC Garage, Vexio, Quickmobile) and Amazon.de, which additionally blocks all six answer crawlers.
An agent-crawler gets exactly what the probe got.
A real false positive: libris.ro answers 200 with a generic JSON ("Forbidden!") on any path.
A naive agent would read it as an Agent Card, and at once.
The mandatory-field filter removed it; the lesson stays.
Raw data, free.
The three CSV files — scores and every signal per domain, adoption rate per signal and group, statistics per dimension — plus the scoring specification and the probe script are published under CC BY 4.0.
Download the data 01 · How did we measure whether an AI agent can find a Romanian company?
Methodology Parameter Value Theoretical framework AI Visibility Stack / Agent Discovery Optimization — Marco, G. (2026), Beyond AEO: The AI Visibility Stack and the Era of Agent Discovery Optimization, Exista.io Working Paper, doi:10.5281/zenodo.18728629 Metric ADO Score 0–100, five dimensions with the paper's weights: Agent Card 30 · trust signals 25 · knowledge completeness 20 · interoperability 15 · freshness 10 Operationalisation Websem — one machine-verifiable criterion per point, published in Sampling frame 130 domains: 43 brands named by AI engines in episodes 1–3 (jewelry, books, electronics and IT) + 87 marketing agencies and sites cited by the engines in episode 4 Probing 8 September 2026, a single pass, from Romania, ~12 HTTP requests per domain, no JavaScript Signals checked and (A2A) · and (MCP) · · · and · JSON-LD on the homepage · visible text without JavaScript · () · Wikidata (P856 = official website) Instrument , standard Python 3, published with the data; declared User-Agent Domains with an HTTP response 126 of 130 (4 without: 2 timeouts, 1 DNS, 1 invalid certificate) Domains in the ranking 125 (without websem.ro, the author's domain — reported separately, section 07) Four design choices deserve an explanation up front, because they decide what the numbers mean.
We measure what an agent can verify, not what a person can read.
The probe does not execute JavaScript, does not interpret marketing copy and does not judge design.
It requests files at standardised paths, parses raw JSON and HTML, and queries a public knowledge graph.
This is exactly how the Exista.io paper describes a procurement agent in its evaluation phase: "persuasive prose, testimonials, and visual design elements are irrelevant to this process".
The weights are the paper's; the criteria are ours.
The paper defines the dimensions and weights of the ADO Score but does not publish scoring criteria.
We wrote them, transparently, one binary signal per point, and publish them with the data.
This is not the Exista.io instrument and claims no equivalence with it; it is a reproducible measurement of the same dimensions.
Anyone can run the probe and get the same figures, on the same day.
A 200 response does not mean "served".
Many servers answer 200 with an HTML page on any path, and some answer 200 with a generic JSON error.
A file counts only if it parses as JSON and contains the protocol's mandatory fields: for an Agent Card, and or ; for MCP discovery, or .
Without this filter, one domain in the study would have had an Agent Card, and simultaneously, all three being the same "Forbidden!" message.
The sampling frame is not the market.
The 43 brands are exactly the companies the AI engines recommended to people in previous episodes, the winners of layer 1; the 87 agencies are those the engines cite on marketing and AI topics, i.e. those who sell AI visibility.
It is not a representative sample of the Romanian economy.
It is, however, the most favourable possible sample for the paper's hypothesis that LLM visibility is inherited by agent discoverability: if it does not hold here, it is unlikely to hold elsewhere.
The main limitation, declared from the star