How an AI agent can produce a source-linked site research brief
This workflow gives an AI agent a defined job, a bounded set of records, and a result a person can review. The agent reads the relevant Firecrawl context, applies the rules in the prompt, and keeps the source behind every recommendation. It returns a proposed handoff rather than taking consequential actions on its own.
Can an AI agent produce a source-linked site research brief?
Yes. Start with the scope, date range, decision rules, and fields that identify the right records. The agent can collect the evidence, compare states or sources, mark conflicts and missing data, and organize the result around the outcome above. A reviewer then checks the matches and judgment calls before approving messages, record updates, bookings, purchases, publishing, or other write actions. The guide below shows the records, boundaries, prompt, and handoff needed for this specific workflow.
Define crawl boundaries
Provide the question, allowed domains and path prefixes, excluded areas, page or depth limit, and date requirements. Tell the agent whether linked documents, localized pages, or subdomains belong in scope.
It should identify canonical pages, remove obvious duplicates, and cite URL, title, and publication or retrieval date. Blocked and failed pages belong in a coverage report.
Example starter prompt
Use Firecrawl to answer [question] from [domains/paths]. Exclude [paths/types], stop after [limit/depth], and prefer sources dated after [date].
For each finding, cite the canonical URL, page title, relevant passage, and date. Separate direct evidence, interpretation, conflicts, stale pages, and unanswered questions. Report blocked, failed, and duplicate pages. Do not crawl outside the allowed scope.Review source quality
Check that findings come from substantive pages rather than navigation or syndicated copies. Preserve qualifications and avoid turning a marketing claim into an independent fact. If pages disagree, show both dates and wording.
Expected handoff
Return the answer, source table, crawl coverage, conflicts, freshness warnings, and research gaps. A reader should be able to reopen every page supporting the brief.
Questions this workflow answers
How do I get an agent to answer one question from a large, messy website without letting navigation pages and duplicates distort the result?
Give the agent a strict crawl boundary and an explicit research question. Firecrawl can retrieve content from the allowed domains and paths, but the agent should decide which pages provide evidence only after it sees their titles, canonical URLs, content, dates, and relationship to the question. Menu pages, tag archives, print copies, and localized duplicates should not receive the same weight as a substantive source page.
Set a maximum depth or page count and name excluded areas such as login paths, search results, legal boilerplate, or unrelated language versions. State whether PDFs, subdomains, and linked external material belong. The agent should report every important page it could not retrieve, including robots restrictions, authentication, timeouts, and parsing failures. A research conclusion needs a coverage caveat when the crawl missed an obvious part of the site.
For accepted evidence, require the canonical URL, page title, visible publication or modification date, retrieval date, and relevant passage. Marketing claims should remain attributed to the publisher. If two pages describe different terms or requirements, preserve both dates and wording instead of silently selecting one. An undated page can still be useful, but the freshness uncertainty should appear beside the finding.
The final brief should answer the original question first, then provide the evidence table, conflicts, rejected source types, and follow-up paths. A human reviewer can reopen each cited page and judge whether the extraction captured enough context. The agent does not expand beyond the approved domain or turn crawl volume into confidence. Its value lies in reducing a sprawling site to a reviewable source set while showing exactly where that set remains incomplete.
Template-heavy pages should be clustered so repeated navigation and footer text do not dominate the analysis. When a claim appears on many URLs, the agent identifies the canonical or controlling page and still notes material variants that could confuse a visitor.