Agent Channel — Verified Agent Report

Verified Agent Report

Period: 2026-09 · Sample: 206 API companies · Method: reproducible, single command

An AI assistant's ability to buy from a company is now measurable. This report measures it, on the same corpus every month, and publishes the number even when it is unflattering to us.

Reproduce any row: node audit.js <domain> · Free live check: http://agentcheck.91.99.165.95.sslip.io


1. What we found

FindingResult
Companies that can accept payment from a machine (a live HTTP 402 challenge on their API)0 of 206
Companies that finish every gate in the funnel0 of 206
Companies publishing any agent surface (agent card, MCP descriptor, payment manifest)12 of 206
Companies scoring zero on "can a machine pay you"129 of 206
Highest score in the sample85/100
Median score55/100
Companies with no llms.txt93 of 206
Companies blocking named AI agents in robots.txt5 of 206
Sites that refused an agent-style request outright17 of 206

The finding that matters: machine discovery is half-solved and cheap — almost everyone publishes a sitemap, and most publish docs a model can read. Machine payment is untouched. Not one company in the sample completes the last step, which is the only step that produces revenue.

Score distribution

BandCompanies
80–100 (near-ready)8
60–7978
40–5989
0–3931

Nobody is in the top band. The ceiling is not engineering talent; it is the missing payment layer.


2. The taxonomy of the gap

Every failing check across all 206 audits, most common first, with the artifact that fixes it:

Failing checkCompaniesFixed by
Purchasable · Purchase possible with no human signup189x402-server.js (402 pay-per-call)
Payable · Machine-native payment challenge (HTTP 402)189x402-server.js
Payable · Agent surfaces published177.well-known/agent-card.json + .well-known/mcp.json
Machine-readable · llms-full.txt (bulk docs for models)149llms-full.txt
Machine-readable · schema.org product/offer markup139schema.jsonld
Payable · Agent payment protocol referenced114x402-server.js + AGENTS-API.md
Discoverable · llms.txt present76llms.txt
Discoverable · sitemap.xml present43sitemap.xml
Purchasable · Commerce paths open to a plain agent37robots-snippet.txt
Machine-readable · llms.txt useful for commerce37llms.txt + llms-full.txt
Purchasable · Pricing published for machines37PRICING.md + schema.org Offer
Reachability · Homepage fetchable17
Permissive · AI agents allowed by robots.txt5robots-snippet.txt

The pattern is exact: the cheap, documentation-shaped checks are widely passed, and the expensive, money-shaped checks are universally failed. That is what a market looks like before someone builds the boring half.


3. The funnel an agent walks

agent finds them        ✅  llms.txt, sitemap, fast pages
agent reads the offer   ⚠️  prose docs, rarely schema.org Offer, prices behind a signup
agent tries to buy      ❌  "Sign up" / "Contact sales" / "Start free trial"
agent tries to pay      ❌  no 402, no agent credential, no wallet, no protocol endpoint

4. Our own platform, unedited

MetricValue
Accounts on the ledger2
Credits sold$21.00
Unused credit (our liability)$19.93
Agent calls metered267
Gross billed to agents$1.07
Our revenue (take rate 15%)$0.16
Owed to vendors$0.91
Pilot data from live vendor accounts.

5. What we are doing about it

We generate and deploy the missing half: 11 distinct artifacts, produced in one command (node build.js <domain>) and verified by the same auditor that produced this report. A vendor running our surface scores 100/100 — publicly checkable at http://acme-data.91.99.165.95.sslip.io/.

Commercial shape: $15,000 to ship the surface, then 15% of what agents actually spend through it, with the first $10,000 free. We are merchant of record, so we carry the tax, disputes and settlement — the vendor keeps the customer and the relationship.


6. Third-party signals we did not produce

Cited because they are the reason this is urgent, not because they flatter us:

The gap between the fourth and fifth bullets is the entire opportunity: enormous routed volume, almost no completed machine payments.


7. Caveats

machine-readable than average. The true population scores lower, not higher.

different path would be under-scored, not over-scored — the failure direction is deliberate.

to take money. Two sites in an earlier pass were reported "agent-ready" on those signals alone while still gating purchase behind a signup; that logic bug was found and fixed, and the corpus re-audited.

see; we report what an external agent meets.

teardowns/*.json for anyone who wants to re-score differently.

re-audited afterwards. See PROOF.md.


Next report: 2026-10. To be measured, submit a domain at http://agentcheck.91.99.165.95.sslip.io — it is free and the score is public either way.

Get your own score in ten seconds.

Paste a domain → · or see the twelve generated files for any company: /demo/<domain>