How an AI agent can return a claim-by-claim verification report
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 Tavily 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 return a claim-by-claim verification report?
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.
Verify one claim at a time
A Tavily verification agent works from a claim list, not a broad topic. Give it the exact wording, intended meaning, required freshness, allowed or preferred source types, and the audience for the result. It should search each claim independently and record sources that support, qualify, or contradict it.
The agent should inspect source content rather than rely on snippets. Publication date, author or organization, directness, and scope determine whether a source applies. Two sites repeating the same unsourced statement do not create independent confirmation.
Example starter prompt
Use Tavily to verify the claims in [draft or claim list] as of [date].
For each claim, find direct supporting and conflicting sources, record URL and publication date, quote the relevant passage accurately, and explain whether the source covers the exact scope. Mark the claim supported, qualified, contradicted, or unresolved.
Do not rewrite unsupported claims into facts. Return a claim-by-claim verification table and source register.Review the strongest and weakest claims
Open the cited pages and check quotations, dates, and context. The final handoff should make weak support visible and suggest what evidence would resolve it.
Questions this workflow answers
Which claims in this draft are supported by current sources, and which need to be qualified or removed?
The agent turns the draft into discrete, testable claims and records the intended scope of each one. Dates, geography, product version, population, and words such as “all,” “first,” or “largest” can change the evidence required. It searches claims independently so a strong source for one paragraph does not lend false authority to nearby statements.
Sources are evaluated for directness, authority, date, and applicability. A primary document that states the fact directly carries different weight from a blog that repeats an unattributed number. Two pages copying the same release do not create independent corroboration. The agent opens the source, captures an accurate passage, and records where context narrows or contradicts the draft.
Each claim receives a status: supported, qualified, contradicted, or unresolved. The explanation names exactly what the evidence covers. A qualified claim may be correct for one year or region but too broad as written. The agent can suggest the information needed to resolve it, but it does not quietly rewrite weak evidence into a stronger fact.
The verification table includes claim text, source links and dates, relevant passages, status, scope notes, and conflicting evidence. A writer reviews the cited pages and chooses whether to revise, caveat, remove, or retain an unresolved statement. This keeps editorial judgment with the author while making unsupported certainty easy to find.
A writer decides whether to revise, remove, or retain an unresolved claim with an explicit caveat.
Claims should be split at the point where one source may support only part of a sentence. “The policy begins in July and applies to every contractor” contains a date claim and a scope claim that may require different passages. The agent can classify direct support, partial support, outdated support, contradiction, and no reliable source, preserving the exact wording and relevant quote. It should prefer primary material and state when a page describes a proposal rather than a final rule. The author then sees whether to narrow the sentence, add a date, attribute uncertainty, or remove it.