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Connect your AI Agent to Google Sheets<br>Piotr Grudzień on June 24, 2026 • 19 min read

Introduction

Your AI Agent talks to every visitor on your site. Some are ready to buy, some ask a question it cannot answer, some request a feature you do not have yet, and some want a demo. By default, all of that is gone when the chat closes .

This guide shows you how to connect your AI Agent to Google Sheets so each of those moments becomes a row in a sheet your team already uses. No Zapier, no webhooks, no engineer. You need two things:

a Quickchat AI Agent (sign up here and use for free )

a Google account

The mechanism is AI Actions : custom HTTP requests your Agent can make during a conversation. Quickchat AI has a one-click Google Sheets connection that creates a sheet and a starter logging action for you, and from there you shape it and add as many reports as you want. By the end you will have four working reports , and you will have tested each one yourself .

This is a long, exact walkthrough. The canonical reference for AI Actions lives in the docs at docs.quickchat.ai/ai-agent/actions. For another worked example of a custom action, see Send Slack notifications with AI Actions.

What you will build

Four reports, each writing to its own tab of one Google Sheet:

ReportTabWhen the Agent writes a rowLeadsLeadsA visitor shows buying intent and shares an emailUnanswered questionsUnansweredThe Agent cannot answer a factual questionFeedbackFeedbackA visitor requests a feature, reports a bug, or gives praiseDemo requestsDemosA visitor asks for a demo or a call<br>The screenshots below come from a test Agent built for a fictional company, Tideline , a subscription-analytics platform for SaaS businesses. The company is invented so the example stays neutral, but every conversation and every row shown here was produced by a real Agent running the real reply pipeline. Use your own company’s details when you follow along.

How the Google Sheets integration works

The whole feature rests on one idea: an AI Action is a described HTTP request, and a Google Sheet row is one such request.

An AI Action has four parts. The description is what the model reads to decide whether to call it; the parameters are what it fills in from the conversation.

A few more facts make the rest of the post easier to follow.

Writing a row is one API call. Google Sheets has an append endpoint that adds a row to a tab. Each report is a POST to that endpoint with the row values in the body.

The columns are not fixed. They are whatever your action sends. You decide the columns by editing the action’s body and the sheet’s header row, so the same integration can log leads, bug reports, or anything else .

Least-privilege access. The Google connection requests only the drive.file scope, which grants access exclusively to files the app creates . It cannot see the rest of your Drive.

The Agent never sees your credentials. The request carries an Authorization header whose value is a placeholder, {{google_sheets_access_token}}. Quickchat AI fills that placeholder with a real, auto-refreshed token after the model has done its part. The token never enters the prompt.

Two built-in variables are available to any action without being defined as parameters: {{conversation_url}}, a deep link back to the conversation in your Inbox, and {{conversation_channel}}, the channel the visitor used (web widget, Slack, WhatsApp, and so on).

Step 1: Create your AI Agent and give it knowledge

A Quickchat Agent’s behavior comes from two places: its Identity (the main prompt) and the knowledge you give it to answer from. Actions & MCPs is where you extend what it can do, such as writing to a Google Sheet. This guide works in Identity (this step and Step 4) and Actions & MCPs (Steps 2, 3 and 6), and tests everything in AI Preview (Step 5).

After you sign up, open Identity in the left sidebar. The AI Main Prompt is where you describe what your Agent is and how it should behave. Give it a short, accurate description of your product, and put the facts it should be able to state (plans, prices, which integrations exist) into its knowledge so it can answer questions directly.

The Agent’s name and its main prompt: a short, accurate description of your product and how the Agent should behave. You add the reporting block to the end of this prompt in Step 4.

Do not worry...