The agent could not access most of Horizon Store's actual content and had to assemble its understanding primarily from the Zzenz platform's ecosystem rather than the target site itself. It succeeded in retrieving one product via an unauthenticated API endpoint and fetched a comparative blog post about Algerian e-commerce platforms, but the storefront itself is JavaScript-rendered and largely opaque to crawling. The final answer blends educated inference (the agent understood Horizon Store is built on Zzenz) with acknowledged gaps (pricing, full product catalog, differentiators) that required web search and external sources to fill.

• ›Step [25] and [29] retrieved product data via api-horizon-store.zzenz.com/products (single item: 'Lampe Torche' with price 3,900 and form config). This was the only content-bearing API call that succeeded; most other API endpoints returned 401 errors. The agent used this one item as evidence of what the store sells but acknowledged it could represent the full catalog or just a fragment.
• ›The homepage and all attempted direct navigation routes (/pricing, /about, /shop) returned 404 errors or blank HTML shells. The storefront is built with a JavaScript framework (Astro) that renders on the client side, making static page fetching useless for understanding store content, pricing, or positioning.
• ›Step [42] fetched an external comparative blog (dzbuild.com) that discussed e-commerce platforms in Algeria and positioned Zzenz as a local alternative to Shopify and WooCommerce. This external source became the primary evidence for describing Zzenz's differentiation, not Horizon Store's own documentation. The agent had to infer that Horizon Store is simply an instance of Zzenz rather than finding this explicitly stated on the domain.
• ›The agent discovered product data only by guessing at the API endpoint structure (api-horizon-store.zzenz.com); this was not discoverable from the homepage or any navigation. Pricing for the Zzenz platform itself was never publicly surfaced and remains unknown in the final answer.
• ›The site required heavy bridging: 24% prior knowledge (domain guessing), 65% previous artifacts (likely cached API responses), and only 6% web search. The agent had to compensate for inaccessible content with external research and platform-level inference.