How an AI agent can turn competitor research questions into a curated source list with links, evidence, and next analysis 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 competitor research questions into a curated source list with links, evidence, and next analysis 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 competitor source discovery agent finds the raw web sources a team should inspect before drawing conclusions. It can locate product pages, docs, pricing examples, case studies, changelogs, or public positioning around a competitor set.
When to use this workflow
Use it before a positioning project, launch brief, feature comparison, market landscape memo, or product strategy discussion.
How Exa gives the agent context
Connect Exa and describe the competitors, source types, freshness needs, and exclusions. Ask the agent to preserve links and explain why each source is relevant.
Example starter prompt
Use Exa to find high-quality competitor and market sources for this research question. Return a curated source table with links, relevance notes, freshness, and gaps that need follow-up.Suggested workflow steps
The agent runs targeted searches, filters weak or irrelevant sources, groups results by competitor and theme, and identifies missing evidence. It should avoid presenting discovery as final analysis.
Expected handoff
The final output should include a source table, summary of themes, weak evidence warnings, and recommended next analysis. It can feed a Notion or Drive research brief.
Questions this workflow answers
Where can our team find the primary pages behind a competitor claim before repeating it in a strategy deck?
Ask the agent to run a source-discovery pass before it writes any conclusions. Exa can search for product documentation, pricing pages, changelogs, job listings, customer stories, technical posts, policy pages, and other public material related to a defined competitor question. The agent’s job in this workflow is to assemble the evidence set and explain what each page can establish, not to rush from a search result to a market narrative.
State the companies, products, time period, countries, and source types that matter. Add exclusions for affiliate roundups, scraped copies, undated summaries, or pages outside the question. For a claim about a new capability, the strongest source may be a product changelog or documentation page. For positioning, the relevant evidence may be the company’s current landing page plus archived or dated campaign material. A third-party article can add context without replacing the underlying source.
The source table should preserve the URL, page title, publisher, visible date, retrieval date, relevant passage, and a note explaining why it belongs. It should also identify redirects, inaccessible pages, likely duplicates, and material whose date cannot be established. If a search snippet makes a claim that the page itself does not support, the agent should reject the snippet. When two sources disagree, both stay in the packet.
Researchers can then decide which pages are strong enough for analysis and which claims need another search. This separation makes review faster: the team can inspect discovery quality before debating conclusions. It also exposes a common and useful result, which is that the available public evidence does not support the original assumption. The agent returns a curated reading set, gaps, and next queries; the strategist decides what the evidence means.
For recurring work, retain rejected domains and duplicate clusters so the next search does not spend its budget rediscovering the same low-value pages.