Over the past weeks I ran an independent agent-readiness scanner over sixteen Finnish company websites, mostly industrial and B2B, a few in healthcare. The scanner was isitagentready.com, which grades on a Level 0 to 5 scale. This is a small, non-random sample. The sites came from my own prospecting, not a statistical draw, so read it as a snapshot, not a census. The pattern was consistent enough to be worth writing down.
Key figures
- Sixteen Finnish B2B sites scanned with an independent scanner, isitagentready.com.
- Almost all sites landed at isitagentready Level 1 of 5, a couple at Level 0, one at Level 2, none higher.
- The three most common gaps: HTML-only pages with heavy token overhead, missing structured data, and no action or capability layer.
- Worst measured token overhead: about 16500 tokens of HTML where 1400 tokens of markdown carry the same content, a 91 percent overhead.
- The two sites that published a real llms.txt sat at the top of the range.
The numbers
On the isitagentready Level scale almost all of the sixteen landed at Level 1 of 5, the floor an ordinary CMS site reaches, a couple sat at Level 0, and only one reached Level 2. None reached Level 3 or above.
To be clear about what that means, these are not broken websites. They load, they rank, a person can use them without trouble. The scanner measures something else, whether an AI agent can read the site and act on it.
The three gaps that showed up almost everywhere
Discoverability was usually fine, legibility was not. Most sites had robots.txt, a sitemap, sometimes explicit AI-bot rules, so an agent can find them. But the same sites served HTML only, often with heavy token overhead. One consumer-facing corporate site returned about 16500 tokens of HTML where 1400 tokens of markdown would carry the same content, a 91 percent overhead. An agent can fetch the page, but reading it is slow and lossy.
The second gap was structured data, or the lack of it. Missing JSON-LD and product data was common, so an agent reaches the site, sees a wall of markup, and cannot answer a plain question like what this company makes or sells.
The third and most consistent gap was the action and capability layer. No markdown negotiation, no MCP server, no API discovery, no agent-auth metadata. One site that belongs to an AI company itself passed zero of eight checks in that discovery group. This is the layer that lets an agent move from finding a site to operating it, and it was absent almost everywhere.
Why this matters now
AI agents are becoming a discovery and transaction channel. When an agent reads a site and cannot parse or act on it, the business does not rank lower, it becomes invisible inside the answer. The sites in this sample are not behind on SEO, most rank fine. They are behind on the next thing, being legible and actionable to the agents that increasingly read on a person's behalf.
The encouraging part is that the fixes are mostly known and mechanical. Serve markdown alongside HTML, add structured data, publish an llms.txt, expose the discovery manifests. Two of the sixteen had already started, they published a real llms.txt, and that is exactly why they sat at the top of the range.
Note added July 17: one reading of the llms.txt point is circular, since
the scanner scores llms.txt directly, so publishing one raises the score
by construction. The observation stands as a description of the measured
range, not as a causal claim about readiness.
To check where a site stands, the free llms.txt validator is at turva.dev/llms-txt-validator, and the agent-readiness audit and advisory work is at turva.dev.
Top comments (2)
Thank you for sharing such an excellent post. I really enjoyed reading it.
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