Answer · Test

How do I test whether AI agents can use my website?

Four ways to test whether AI agents can use your website. Paste your top product page into ChatGPT, Claude, Gemini, or Copilot. Ask "is this credible?", "what are the risks?", "would you shortlist them?". If the answer is vague or skeptical, you have an Agent Surface problem: what an AI sees on your site doesn't survive the filter [S1]. The free Hyperize Snapshot tests one real task on your website and shows where agents get stuck.

Paste your best page into an AI. If it doesn't survive that filter, your buyers won't either.

Takeaway

Most brands learn whether their website survives the AI filter only when a sales conversation fails for reasons they can't explain. You can run the test yourself in three minutes: paste your strongest page into ChatGPT or Claude and ask buyer-style questions. The Hyperize Snapshot goes further: agents attempt a real task on your website, with the result recorded in one report.

Comparison

Four methods. One main weakness each.

Honest comparison — including what each method does not do.

Method
Cost
Setup
What it returns
Main weakness

Manual AI paste-test

Free
3 min
Subjective AI judgment from 1 model
n=1 model, no scoring rubric, no peer comparison

Internal AI shortlisting audit

Team time (~2h)
Half-day
Buyer-style prompts across 4 AI models
No competitive comparison; no scoring framework; outcome depends on prompt skill

Hyperize Snapshot

Recommended starting point

Free
48 h
One real task, Agent Success Score, priority fix
Diagnosis only. Implementation is agreed separately.

Hyperize Agent Success Audit

€1,900
7–14 days
Critical tasks tested by agent class, failure points and prioritized fix list
One category per engagement; takes 7–14 days

How we tested

The five-agent fleet.

Our testing distinguishes five agent classes: HTTP (raw GET requests), LLM (conversational with web search), Code Agent (programmatic access), Browser Standard (DOM automation), and Browser+ as the upper-bound capability test. The Snapshot focuses on one real task, with the outcome and failure point recorded [S2].

Visibility to one agent is not visibility to all. A brand indexed by ChatGPT can be invisible to a code agent that cannot parse JavaScript carousels. We distinguish whether agents find your offer from whether they can complete the task. Results depend on the task and agent used. The methodology is documented at Context Window Optimization and the parent Agent Surface concept.

Why this matters

Most brands fail silently.

When a buyer's AI dismisses your website, you don't get a notification. The sales call never happens. The shortlist gets built without you. The "we have a credibility problem" conversation arrives months later, framed as a sales-team issue or a marketing-content issue — when the actual problem is that the AI gate closed before any human ever saw your brand.

The test exists. The Snapshot makes it cheap to run. The only question is whether you'd rather find out now or after three quarters of unexplained pipeline weakness.

Sources

Evidence and provenance.

S1

internal

We built a button. An AI closed the deal. — original observation of the AI filter

Hyperize Insights · March 2026

https://www.hyperize.ai/en/insights/articles/we-built-a-button-ai-closed-the-deal

Supports: Demonstration that AI assistants apply a measurable credibility filter to website content — substantive content passes, marketing copy is dismissed.

S2

internal

Hyperize Snapshot — 5-agent fleet methodology

Hyperize Internal — Product · Q1 2026

fleet/snapshot/methodology-v1.md

Supports: Agent classes, task-level outcomes and the distinction between finding a business and completing a task.

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