Yes. An AI agent can work on CSV files when each row becomes a task.

The CSV provides inputs such as company names, domains, URLs, lead details, product text, or questions. The agent uses those inputs to search, browse, extract, classify, or enrich data, then writes the result back to new columns.

AI agent vs LLM on CSV rows

A simple LLM workflow reads the row and returns an answer.

An AI agent can use tools. It can search the web, open pages, inspect content, and return evidence.

Use a simple LLM for:

  • Classifying text already in the CSV
  • Translating descriptions
  • Rewriting notes
  • Extracting fields from stored text
  • Scoring rows from existing columns

Use an AI agent for:

  • Finding missing websites
  • Researching companies
  • Reading web pages from URL columns
  • Checking whether a company matches an ICP
  • Finding source URLs for claims
  • Scraping data from pages that vary in structure

CSV inputs that work well

Good CSV columns include:

  • Company name
  • Domain
  • Website URL
  • LinkedIn URL
  • Job title
  • Location
  • Product description
  • Search query
  • Page URL
  • Existing notes

More context usually improves results. A domain is often better than a company name alone.

🔍 Give the agent anchors

When possible, include a domain or URL column. It reduces ambiguity and helps the agent avoid similarly named companies.

AI agent CSV workflow

A practical workflow:

  1. Import the CSV.
  2. Clean obvious duplicates.
  3. Choose the input columns.
  4. Write a prompt with prompt variables.
  5. Define structured output columns.
  6. Test on a small sample.
  7. Run the agent on the full file.
  8. Review failed or low-confidence rows.

For larger files, use batch LLM processing principles: sample first, track row status, and rerun only failed rows.

Define a result you can check

Keep the original columns and a stable row ID. For company research, useful output columns are a finding, an evidence quote, a source URL, and a review status. A confident answer without supporting evidence is harder to check than a short answer with a page you can open.

For example, a row might contain a company domain and this question: “Does the company sell inventory software for Shopify merchants?” The agent should return the stated product and customer, with the supporting page. If the website does not establish that fit, the result should say Unknown rather than infer it from a company name.

Distinguish three cases when reviewing results:

  • Missing input: the row has no usable domain or URL. Fix the input before rerunning it.
  • No supporting evidence: the research ran, but the answer was not found. Keep Unknown and inspect the sources.
  • Run error: the task failed. Read the error before deciding whether to retry.

A model's confidence score is a review aid, not proof that a claim is correct. Check a sample against the linked pages before processing the remaining rows.

AI agents for CSV files in Datablist

Datablist lets you import CSV files and run the AI Agent on rows. Use it for AI web research, AI data enrichment, and AI personalization at scale.

In the AI Agent, use Prompt to reference the row's columns and Expected Outputs to define each result's name, description, and type. For a source link, choose a URL output and ask for the page that supports the finding. Keep these results in new columns so you can compare them with the original data.

Importing a CSV does not give every AI enrichment web access. Use a text model for content already in the file; choose the AI Agent when the task requires search or browsing. See prompt variables for inserting column values into the prompt.

For workflows, read automating AI research on an Excel file, ChatGPT search on a spreadsheet, and running ChatGPT on Excel and CSV rows.