How an AI agent can turn a research question into a source-backed brief with findings, links, confidence, and next steps
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 Exa 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 turn a research question into a source-backed brief with findings, links, confidence, and next steps?
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.
What this agent helps you do
An Exa web research agent helps teams gather evidence quickly without losing source quality. It can search for relevant pages, extract context, and prepare a concise summary of what the sources show.
When to use this workflow
Use it for market research, technical discovery, content briefs, competitor scans, vendor research, or background material before a product decision.
How Exa gives the agent context
Connect the plugin and describe the question, source types, freshness requirements, and exclusions. Ask the agent to report links and explain why each source is relevant.
Example starter prompt
Research this topic using Exa. Find high-quality sources from the last year where possible, summarize the main findings, include links, and flag claims that need stronger verification.Suggested workflow steps
Define the question, run targeted searches, filter weak results, extract useful passages, and synthesize findings by theme. The agent should preserve source links for review.
Expected handoff
The output should include an executive summary, source table, key findings, open questions, and recommended next action. It can become a Notion doc, Google Drive brief, or task for another LatchLoop agent.
Questions this workflow answers
Is it possible to give an agent an unfamiliar business question and get back a reading list I can verify instead of an unsourced answer?
Yes. The prompt must define what counts as a useful source and what the research will inform. Exa can search the web around the question, but the agent should treat every result as a candidate until it checks the page itself. Search snippets, copied summaries, and high ranking do not prove that a page contains the evidence the reader needs.
Give the agent the subject, geography, date range, audience, and preferred source hierarchy. A technical question may prioritize official documentation and standards. A market question may need company filings, government data, direct product pages, and credible reporting. Tell it which domains or source types to exclude and what would make the research stale. The agent can split a broad request into smaller claims, search each one, and show where no source met the standard.
Every accepted source should carry a URL, title, publisher, publication or update date when available, retrieval date, and the passage that supports a finding. The answer should distinguish direct evidence, a reasonable interpretation, and a question that remains open. If two credible pages conflict, the conflict is part of the result. If several articles reproduce one announcement, the source table should reveal that shared origin.
The handoff can begin with a short answer for the decision-maker, followed by findings grouped around the original question and a source register for the reviewer. Ask the agent to state which conclusion would change if a weak source were removed. That makes the research easier to challenge and update. A person approves the source set and any decision based on it; the agent shortens discovery and synthesis without pretending the open web is clean, complete, or automatically trustworthy.
It should also record the final search date and the claims most sensitive to new information, giving the owner a practical refresh trigger.