How to Connect Agenty to Excel (4 Methods Compared)

The Problem With Getting Excel Data In and Out of Agenty

You have an Excel workbook full of URLs — competitor product pages, blog posts, directories, domains you're monitoring — and you need to run those URLs through Agenty and get the scraped output back into your workbook in a usable format.

Agenty is good at extracting structured data from web pages at scale without writing a single line of code. But the round-trip between a workbook and an agent job is where most people lose an hour they didn't plan to lose. The default flow is: export the URLs to CSV, upload them into Agenty, configure the agent, run the job, download the output, reformat it, and paste it back into the right columns.

Below are the four ways teams handle this. Only the last one actually fits into a normal workday.

Method 1: Manual Copy-Paste

The default. You export your URL column as a CSV, switch to Agenty, create or configure a scraping agent, upload the file or paste the URLs, run the job, wait for it to finish, download the results, open them in a separate spreadsheet, and paste the relevant columns into your original workbook.

That description has ten steps. And that's before you discover Agenty returned a column called "product_name" while your workbook expects "Name," or that the row order shifted during the CSV round-trip.

The first time you do it, you tell yourself it's a one-off. The third time, you've written out the steps on a sticky note next to your monitor. By the sixth run — same workbook, same agent, same columns — you've turned data collection into a part-time job nobody budgeted for.

Method 2: Power Automate

Power Automate has connectors that can reach Agenty's API. You can build a flow that reads a row from your workbook, triggers an Agenty scraping run, and writes the output back when the job finishes.

Before you keep reading — a quick self-check. Do you know what an HTTP action is? A scheduled trigger? JSON parsing? Dynamic content mapping? If those terms sound abstract, this path isn't for you right now — skip to Method 3 or 4. Power Automate flows reward people who build them regularly, and that's a distinct skill set.

Still with it? The flow works when it's built correctly. You configure a trigger — either scheduled or row-based — call the Agenty endpoint, parse the response, and map the fields back to your workbook columns.

The problem is the row-by-row ceiling.

Each URL in your workbook is a separate flow run. Three hundred URLs means three hundred API calls, three hundred operations logged, and a debug experience that asks you to search through three hundred individual run histories when row 147 quietly returns a null and the rest of the rows shift.

You probably just need the pricing data — or the metadata, or the redirect status — and you have no idea how to write a Power Automate HTTP action. And that's fine. So you send a message to whoever handles IT or ops automation, and now the whole project is parked while you wait. Meanwhile the competitor data you needed for Tuesday's pitch is still sitting uncollected in column A.

And once you need any filtering — scrape only URLs where column C says "active," skip rows where column B is blank — you've exceeded what the flow can evaluate inline.

Method 3: The Previous Generation — Connector Add-Ons

Until recently, the best option for repeatable workbook ↔ Agenty workflows was a category of add-ons that let you configure API calls and column mappings inside Excel. You tagged your URL range, mapped your output fields, saved the config, and ran it on a schedule.

That was a real step up. Consistent formatting, reusable configs, no reformatting on every pass.

But every piece of logic — which rows to include, what to do when a page returns a 403, how to handle a missing field — was still yours to encode. The tool got the data through. It did not think. And when your workbook structure changed — a new column, a renamed header — the saved config broke and waited quietly for someone to notice.

This is the previous generation. It was better than copy-paste and still required a lot from the person running it.

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 Agenty integration it can kick off scraping jobs, retrieve results, and write them back into your columns — based on a plain-language instruction.

Example 1: Bulk product data extraction

Scrape the product name, price, and in-stock status from every URL in column A using Agenty and write the results into columns B, C, and D

SheetXAI reads the URL list, creates the Agenty scraping job, waits for results, and writes the extracted fields back into the specified columns — one row per URL, aligned with your existing data.

Example 2: Flagging missing metadata

For each URL in column A, use Agenty to pull the meta description and H1 and write them into columns B and C — then flag any row where either field came back empty in column D

The pattern: instead of scraping the data and then running a second pass to find gaps, you ask for both in one prompt. SheetXAI handles the conditional check inline.

Try It

Get the 7-day free trial of SheetXAI and open any Excel workbook with a list of URLs in column A, then ask it to scrape a field from each one using Agenty. The Agenty integration is included in every SheetXAI plan.