Agentic Eyes
Agentic Eyes is a simulated customer with a browser. Pick a persona, point them at your site, and they walk it like a first-time visitor: skim pages, click what looks promising, get confused where a real person would, and hand you a plain-language report of the journey — what they tried, where they stalled, what questions they left with, and whether they’d buy.
It is QA from the outside in. Instead of checking your implementation against specs, Agentic Eyes checks your product against a person.
How a review runs
Section titled “How a review runs”- Choose a persona — Sally, the Dreaming Planner, the Worst Customer Ever, or one of your own saved personas.
- Set the URL and optionally a concrete goal (“book a lagoon tour”, “find the enterprise pricing”).
- In landing-page mode the persona studies the page and reports first impressions and friction. With drive mode they actually click through: multi-step journeys, typed input, real navigation — and the report describes the path they took.
- You get a report written as a message from the user: what worked, what confused them, the fixes they’d suggest — ranked.
What makes it different
Section titled “What makes it different”- A different buyer’s report. Agentic Eyes output is written for product and growth people, not QA engineers. “I couldn’t find the price and assumed it was expensive” is a finding anyone can act on.
- Real browser, real journey. Findings come from what actually happened during navigation, not from static page inspection.
- Privacy-safe by construction. Credentials and identity fields are blocked — the persona browses like a cautious stranger, never fills your login forms with invented data. External actions (payments, signups) stay off unless you explicitly allow them.
- Privacy-safe in storage. Only origin and path are retained — never query strings or fragments — and everything persisted passes recursive redaction.
Evidence and reports
Section titled “Evidence and reports”Every run is backed by session telemetry: route sequences (including loops and revisits), interaction and failure counts from the actual action trace, and bounded console-failure counts — message content is never retained. Reports export to standalone HTML or PDF, and can be shared with a link — useful for handing a conversion review to a founder or agency unchanged.
Going further
Section titled “Going further”- Persona catalogue — built-ins and building your own.
- Consensus panels — the same review across several AI models; agreement is the signal.
- Evaluation modes — conversion vs research framing, and why passing one explicitly matters.