The agent assembled a reasonably complete overview of Aims Community College by combining direct site navigation with web search discovery and prior knowledge, but struggled to extract detailed pricing and affordability information due to JavaScript-rendered content that did not materialize in the fetches. The site's information architecture was navigable (URLs were guessable or discoverable via search), but the actual pricing and program-specific cost details were not fully accessible to the agent's fetch-and-parse workflow.

• ›Steps [1], [3], [4] returned HTML shells without rendered body content—the agent could not extract what was visually present on the pages, indicating heavy client-side rendering that broke direct fetching.
• ›Web search [9] was critical: it surfaced the /affordability and /tuition-and-fees URLs the agent could not discover organically, and search snippets likely contained pricing figures ($3,410 in-state, ~$11,786 out-of-state) that the agent cited in the final answer but did not retrieve from direct page fetches.
• ›Steps [10] and [11] (affordability and tuition-and-fees pages) also returned HTML shells, confirming the site publishes key pricing pages but not in a machine-readable form—the agent had to rely on search result snippets and prior knowledge to populate the pricing section.
• ›The agent successfully identified core positioning (broad, inclusive community college; Northern Colorado; 200+ programs; flexible formats), but explicitly flagged missing details: program-specific costs, detailed financial aid criteria, and employment outcomes—these gaps reflect both incomplete site content and inaccessible structure.
• ›Navigation structure is agent-friendly (guessable URLs like /about, /programs, /affordability work), but content delivery is not—the site requires JavaScript execution to render answers to the task questions.