What if every inbound support email got an instant, intelligent first response - without your team lifting a finger?

With Front and Kapa.ai, you can build exactly that. Kapa is an AI platform trained on your documentation, and Front is the team inbox tool used by thousands of support and success teams. Together, they make it possible to wire up an AI agent that reads incoming emails, queries your knowledge base, and drafts a response automatically.

This tutorial walks you through the full setup: creating the Kapa Connector app in Front, configuring the API integration in the flow builder, and writing the rules that trigger the agent whenever a new conversation lands in your inbox.

No custom code required. Let's get started.

Prerequisites

Before diving in, make sure you have the following:

• A Front account with access to developer settings (Admin or Developer role)

• A Kapa.ai account with an active project and access to your API key and project/integration IDs

• Familiarity with Front's flow builder is helpful but not required

You'll also want your Kapa credentials on hand:

• Your API key (from the Kapa dashboard)

• Your project ID (used in the API URL path)

• Your integration ID (used in the request body)

Setup

Step 1: Create the Kapa Connector App in Front

The first thing you need to do is set up a custom app inside Front. This app will handle the outbound API request to Kapa every time it's triggered.

1. Open Front and navigate to Settings → Developer Settings

2. Click Create New App

3. Inside the app, click Add New Feature and select App Request

4. Name the feature: Kapa Connector

This creates the request handler that your Front rules will call later. Think of it as the bridge between Front and the Kapa API.

Step 2: Configure the Flow Builder

Once your app and feature are created, you'll land in the Flow Builder - a three-step visual editor that defines what happens when the app is triggered. Here's how to configure each step.

Collect Input

This step captures the inbound email content so it can be passed to Kapa.

1. Click Collect Input

2. Add a new input and name it query

3. This variable will hold the message body from the incoming email - it's what gets sent to Kapa as the query

Send Request

This step makes the actual API call to Kapa.

1. Click Send Request

2. Set the Method to POST

3. Under URL, click Add New Server and configure it as follows:

   • Origin: https://api.kapa.ai

   • Authentication strategy: API Key

     • Property name: X-API-KEY

     • Send as: HTTP Header

     • Enter your Kapa API key when prompted

4. Set the Path to: /query/v1/projects/:project_id/chat/

   • Replace :project_id with your actual Kapa project ID

5. In the Body section, paste the following JSON - replacing your_integration_id with your real value and making sure query is passed as a dynamic variable (not hardcoded):

{
  "integration_id": "your_integration_id",
  "query": "You are a Kapa.ai support agent, respond to this email: {{query}}"
}

⚠️ Note: The {{query}} placeholder above represents where your dynamic input variable should be inserted. In Front's flow builder, this should be wired as a variable reference, not typed literally with curly brackets.

6. Click Run Test to send a sample request and verify the connection is working

7. From the test response, click on the answer and is_uncertain fields to save them as dynamic variables - you'll use both of these in the next step

Return Data

This step defines what data the app sends back to Front after querying Kapa.

Add two outputs:

Output 1 - The AI's answer:

• Type: String

• Name: answer

• Data: The answer variable captured from the API response

Output 2 - Confidence signal:

• Type: Boolean

• Name: is_uncertain

• Data: The is_uncertain variable captured from the API response

The is_uncertain flag is particularly useful - you can use it downstream to route low-confidence responses to a human agent for review instead of sending them directly.

Once both outputs are configured, click Save.

Step 3: Set Up the Rule to Trigger the Agent

Now that the app is ready, you need to tell Front when to run it. This is done through Rules, which are Front's automation engine.

1. Go to Settings → Rules & Macros

2. Click Create New Rule and select Create Linear Rule

3. Configure the rule as follows:

Trigger:

• Select Inbound message is received (new conversation)

Conditions:

• Add any conditions that determine when the rule should apply - for example, limiting it to a specific inbox, tag, or sender domain. This ensures the AI agent only fires where it makes sense.

Actions:

1. Click Add Action and select Send App Request

   • Choose the Kapa Connector app you created

   • In the query field, click Add Variable:

     • Create a new dynamic variable for Message Body - this pulls the email content from the incoming message

     • Then click on Message Body, choose Add a Step, and select answer - this chains the message through Kapa and retrieves the response

2. Click Add Action again and select Add Comment

   • In the body of the comment, insert the App Request variable (which contains the answer from Kapa)

   • This posts the AI's response as a comment on the conversation thread, visible to your team before it's sent

3. Click Create to activate the rule

Unfortunately it is not possible to automatically send Kapa's response to the customer but dropping it as a comment means your team can quickly copy-paste it into a response.

Conclusion

You've now set up a fully automated email AI agent inside Front, powered by Kapa. Every new inbound conversation will automatically be analysed by Kapa's AI, which generates a response grounded in your documentation. That response lands as a comment on the conversation - ready for your team to review, edit, and send.

From here, you can extend this setup further: use the is_uncertain flag to route low-confidence responses to a human review queue, add conditions to scope the rule to specific inboxes, or chain additional actions to close or tag conversations automatically.

If you run into any issues or want to explore more advanced Kapa configurations, check out the Kapa documentation.