AI agent readiness
Can AI agents operate your application?
More and more people let AI assistants research, compare and send requests for them. In the AI agent readiness audit I check whether these agents can find, understand and operate your application and what is still missing.
What the AI agent readiness audit examines
Discoverability
I check what language models and their crawlers get to see of your application: content without JavaScript, structured data, robots.txt and llms.txt.
Operability
I let common AI agents run your most important flows and record where they fail: at forms, logins, confirmations or protection mechanisms.
Interfaces
I check whether your flows can be offered as tools, for example through an MCP server or WebMCP, and how they need to be protected against abuse.
What you have in hand after the audit
Test log
What each agent managed to do in your application and where it gave up, flow by flow.
Prioritized findings
Every obstacle with its cause, impact and estimated effort.
Action plan
From quick fixes to markup and content to a dedicated interface for agents, in a sensible order.
Closing call
We go through the results together and answer your team's questions.
Example finding from an AI agent readiness audit
This is how every finding in the report is structured. The example is made up and shows how specific the results are.
Finding
The quote form only loads its fields via JavaScript and labels them with placeholders only.
Impact
In the test none of the three agents could send a request: two did not find the fields, one filled them in incorrectly.
Recommendation
Deliver the fields directly in the HTML, mark them up with labels and make sure the HTML is valid, effort about two hours. As a second step, offer the same request as a tool via WebMCP.
How the audit works
Request the audit
You pick your audit and briefly describe your application.
Fixed price and analysis
You get the fixed price for your application, then I review it.
Findings and action plan
You receive prioritized findings and a plan for the next steps.
Why does a human check this and not an agent?
Agents run the tests, that is the whole point, and AI helps me evaluate their logs. But no agent reliably draws the conclusions itself: which flows should agents be allowed to run in your application at all, where does it take confirmation by a human and how do you protect yourself against abuse? I answer these questions from experience with my own interfaces for agents, including the ones on this website.
Frequently asked questions about AI agent readiness
Which applications is the audit worthwhile for?
For all those where customers search, compare, book or send requests: shops, portals, booking systems and websites with an inquiry form. The more of your customers use AI assistants, the sooner it pays off.
Do we need an MCP server for this?
Not necessarily. Clean HTML, labeled forms and structured data often get you most of the way. Whether a dedicated interface is worthwhile is a recommendation in the action plan.
What does the AI agent readiness audit cost?
The audit comes at a fixed price from €1,500. I tell you the exact price before I start, it depends on the size of your application. If a follow-up project comes out of it, the audit price is credited towards it.
Which agents do you test with?
With the widely used AI assistants and their browser agents at the time of the audit. The test log names them, because this field changes quickly.
Request the audit or talk first
Request your audit directly and you get the fixed price and time frame, with no obligation. Not sure which audit fits? Then we sort that out in a free initial call.
Problem
Your software doesn't talk to AI agents yet
Agents struggle through your interface or fail at it. With MCP and WebMCP your product gets an interface that agents understand.
Case study
A website AI assistants can operate
This website accepts inquiries not only through a form but also directly from AI assistants. This is how the MCP server and WebMCP are built for it.