Research agent
A research agent is an AI system that reads public sources, web pages, filings, press coverage, to answer a specific research question about a company or person, as distinct from a static enrichment lookup that returns whatever a database already has on file.
Ask a research agent for a company's latest funding round and it will usually hand one back: a dollar figure, a round letter, sometimes an investor's name, stated with the same flat confidence as the founding date. None of that has to be true for the agent to say it that way. An LLM asked a question with no grounding data doesn't reach for a 'don't know' button by default. It doesn't have one. What it has is a pattern-completion problem: given everything it's seen about funding announcements, what's the most statistically plausible answer for a company that looks like this one.
Retrieval versus pattern completion
A properly grounded research agent works differently: it fetches a specific page, finds a specific sentence, and reports what that sentence says. That's retrieval, a different operation from generating a plausible-sounding guess. Most agents don't distinguish the two in their own output. A pattern-completed guess and a retrieved fact come back in the same confident sentence, same punctuation, same tone, and nothing in the raw text tells a reader which one they're looking at.
Two tools built for this job split the work differently. A Clay-native research agent runs row by row inside a table, reading public web pages to fill in one column across hundreds or thousands of records, well suited to the same lookup repeated identically at scale. A terminal-based coding agent works the other way: no row limit, but it needs a person steering the session, asking the next question, and catching it when it wanders down a branch that doesn't matter. Neither replaces the other. They fit different shapes of research question.
In practice
Before a research agent's output reaches personalization copy or a CRM field, check whether the prompt forces a real source and quote behind each claim, or whether it just asks a question and takes whatever comes back. The second pattern is where fabricated signals slip through undetected.
What people get wrong
Teams treat a research agent's output as retrieved fact by default, when a meaningful share of it is generated to sound retrieved. The fix isn't distrusting every answer. It's requiring the agent show which specific source and quote back each claim, so a person can tell the two apart before the claim reaches a live prospect.
