How to Connect AgentQL to Excel (4 Methods Compared)

The Problem With Getting Sheet Data In and Out of AgentQL

You have an Excel workbook full of URLs — competitor landing pages, company domains, career portals. You need structured data pulled from each one: prices, headlines, job titles, employee counts. The kind of data that lives on public web pages but doesn't come with a download button.

AgentQL is good at querying any website with natural language and returning clean JSON. But closing the loop between an AgentQL extraction and your Excel workbook takes more plumbing than it should. The default path is: run the query in AgentQL, copy the JSON output, parse it somewhere, paste the values into the right columns, repeat for the next URL.

Below are the four common ways teams handle this. Only the last one scales.

Method 1: Manual Copy-Paste

The default. You export a CSV from whatever system holds your URL list, open it in Excel, run an AgentQL query against the first URL, inspect the JSON response, and manually transfer each field value into the correct column.

That's manageable when the list has a handful of entries.

At 50 URLs the session becomes: run query, switch windows, find the field in the JSON, copy value, switch back to Excel, find the row, paste into column B, go back, find the next field, paste into column C. The process is correct but fragile. You will miscopy something. You will lose track of which rows are done. The data will be mostly right.

Method 2: Power Automate

Power Automate has AgentQL connector support. You can build a flow that triggers on a new row added to your Excel file, passes the URL from a specific column to AgentQL, and writes the structured response values back into adjacent cells.

Before you keep going — do you know what a flow trigger is? A dynamic content expression? How to extract a nested field from a JSON response and map it to a specific Excel column? If any of those are unfamiliar territory, this path requires more onboarding than the task is probably worth. Skip to Method 4.

For those who are still here: the flow works. AgentQL returns a JSON object, Power Automate maps selected fields to columns, and the workbook gets populated. The friction is in the setup: picking the right trigger condition, defining the JSON schema so Power Automate can parse the response, and debugging a partial run when the automation wrote to the wrong column for rows 12 through 31.

One flow trigger also fires once per row — it is not a batch operation.

Running 200 URLs through Power Automate means 200 separate API calls and a run history that becomes very difficult to audit when something fails midway.

You probably just need the structured data from those web pages. You probably have no idea how to write a JSON schema expression in Power Automate — and that's a completely reasonable position for someone whose job is competitive analysis, not automation engineering. So you either delegate it to the person on your team who knows Power Automate, or you're spending a Tuesday learning something you'll use once.

Method 3: The Previous Generation — Connector Add-Ons

Until recently, the best option for repeatable Excel ↔ web-scraping workflows was a category of add-ons that let you configure URL columns, field mappings, and saved templates. You pointed the tool at a column, defined the fields you wanted extracted, and ran it.

That was a real step up from manual work. Output was consistent, configs were reusable, and the team didn't have to reconstruct the extraction setup each time.

But you were still responsible for the field mapping, the selector configuration, the schedule, and the conditional logic about which rows to include. The tool moved the data, but the structural thinking was still on you. And when a source site changed its page layout, your config broke until someone fixed it.

This is the previous generation. It worked, but it asked a lot of the operator.

The Easy Way: Using SheetXAI in Excel

There is a different way entirely. SheetXAI is an AI agent that lives inside your Excel workbook. It reads the workbook, understands what you are looking at, and through its built-in AgentQL integration it can query websites for you and write the results back into the right columns. No selector configuration, no automation glue, no copying JSON by hand. You just ask.

Example 1: Bulk-scrape competitor pricing from a URL list

Scrape the pricing table, plan names, and monthly prices from each URL in column A using AgentQL and write the results into columns B, C, and D

AgentQL queries each URL in sequence, extracts the pricing fields, and writes the values row by row into the workbook — matched to the originating URL.

Example 2: Extract headline and subheadline from product pages

For each product URL in column A, extract the headline, subheadline, and primary CTA button text using AgentQL and fill in columns B through D

The pattern: instead of exporting data first and then cleaning it into a workbook format, you ask for both the extraction and the placement in one prompt. SheetXAI handles the mapping inline.

Try It

Get the 7-day free trial of SheetXAI and open any Excel workbook with a column of URLs, then ask it to scrape and populate the columns next to them. The AgentQL integration is included in every SheetXAI plan.