What is an AI research agent?

An AI research agent is an LLM workflow that can use tools to find information, read sources, and return structured answers.

A simple LLM prompt only processes the text you give it. An AI research agent can search the web, open pages, inspect content, and write findings back to your spreadsheet.

This matters when your data is incomplete.

For example, a spreadsheet might contain only company names. An AI research agent can search each company, visit websites, and return:

• Website URL
• Product summary
• Target customer
• Pricing page
• Recent hiring signal
• Competitor mention
• Short qualification note

AI research agent vs LLM prompt

Use a simple LLM prompt when the row already contains the information.

Use an AI research agent when the workflow must find the information first.

Examples:

• A prompt can summarize a company description stored in a row.
• An agent can find the company website, read it, and then summarize the company.
• A prompt can classify a job post already in your spreadsheet.
• An agent can search for recent job posts and extract the signal.

Common AI research workflows

AI research agents are useful for sales, recruiting, marketing, and data operations.

Common workflows include:

• Company research from names or domains
• Lead qualification
• ICP scoring
• Competitor research
• Website content extraction
• Case study discovery
• Local business research
• Job posting analysis
• Account personalization

Each workflow needs a clear prompt, clean inputs, and structured outputs.

🔍 Ask for evidence

Add a source URL field when the agent searches the web. It helps review answers and reduces blind trust in AI output.

AI research agent output

Good research output is short and structured.

Example fields:

• Answer
• Source URL
• Confidence
• Reason
• Extracted quote or evidence
• Needs review

Avoid asking for long prose unless a human will read it. If the result will feed another workflow, use structured LLM output.

Define the scope before researching

Give the agent an entity, one question, and a rule for accepting evidence. A company domain is a useful anchor when names are ambiguous. For account research across several sources, identify the company first, then keep each finding with the page that supports it.

For example, ask whether a company publishes a pricing page for its own product. A search result mentioning another company with the same name should not count. If no supporting page is found, the answer should be Not found rather than “the company has no pricing page”.

A request-a-quote page and a page with published prices are different findings. Keep that distinction in the evidence rather than making the model manufacture a price for every row.

Review the answer and the source together

In Datablist's AI Agent, define Status, Pricing Page URL, and Evidence under Expected Outputs. These are custom outputs you configure, not automatic fields supplied by every research run. The agent also offers Confidence Score and Error Msg outputs.

Check a sample by opening the returned pages. Confidence is an aid for review, not independent verification. Separate a completed search with no supporting evidence from a failed run; only the latter tells you the research did not complete as intended.

For an account brief, keep separate source-linked findings before asking a text model to summarize them. This makes it easier to spot a contradiction or a fact about the wrong subsidiary. See AI web research for source and date checks.

AI research agents in Datablist

Datablist includes an AI Agent enrichment and an AI Research Agent feature for running web research across rows.

It works with spreadsheet columns through prompt variables. For example, you can research {{Company Name}} and save results into columns such as Summary, ICP Fit, Source URL, and Personalization Angle.

For CSV workflows, read AI agent for CSV files.

For full workflows, read automating AI research on an Excel file, ChatGPT search on a spreadsheet, and AI search at scale.