fryga.io
success
Claude Code
intent
What does fryga.io do and who is it for? Explain it back to me.
6steps
40.0sduration
$0.2466cost
18,531tokens
8 steps2 reasoning steps
home
docs
docs
home
home
home
80%
answer from your site
67%
answer efficiency
100%
followed site links
answer sources
- from this site80%
- from agent knowledge20%
url discovery
- given in the task17%
- followed a link83%
insight
The agent successfully understood fryga.io's core offering, pricing, and differentiation by extracting information directly from the homepage (fetched multiple times). The site publishes its key facts clearly on a single page, but lacks subsidiary pages (pricing, about returned 404s), forcing the agent to rely on repeated homepage fetches and work around missing structured detail on outcomes, team composition, and alternative comparisons.
- ›Homepage is the only content-bearing resource: steps [1], [4], [5], [6] all fetched fryga.io/ and returned different sections of the same page. Attempts to navigate to /pricing and /about both returned 404s, indicating the site does not expose navigation to subpages—all information is consolidated on the homepage.
- ›Agent discovered all URLs by typing from memory, not surfacing: 83% of discovered URLs came from following links, but the agent's typed fetches of fryga.io/ (steps [1], [4], [5], [6]) were 'URL typed from memory (not surfaced by the site)', revealing the site has no explicit navigation structure or menu the agent could follow. The agent had to repeatedly re-fetch the same URL to extract different sections.
- ›Site content is comprehensive but unstructured: the homepage contains pricing, service descriptions, differentiation, and commitment terms, but appears to be a single long-form narrative rather than machine-readable structured data (no pricing table, no schema markup detected). The agent assembled the answer by parsing multiple full-page responses.
- ›Friction outcome 'succeeded_with_heavy_bridge' reflects the workaround: the agent had to guess or re-fetch the homepage multiple times to extract discrete sections, and could not navigate to dedicated pages, creating friction despite eventual success in answering the task.
- ›No data on outcomes or team details: the agent flagged legitimate gaps (no quantified metrics from case studies, no team bios, no wait-time clarity for Q3 2026 booking). These absences were not due to failed fetches but are genuine content gaps on the live site.
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