How to use Fin Tasks and Data connectors to automate complex support

Let’s be honest, you’re here because you want your AI support agent to do more than just spit out FAQ answers. You need it to handle real, multi-step customer problems, things like processing a refund, changing an order address, or walking someone through a tricky troubleshooting process. That's the real promise of AI automation, isn't it?

Intercom’s answer to this challenge is a combination of "Data connectors" and "Fin Tasks" to fetch information from other systems and to execute the logic. When they work together, you can build workflows that actually take action for your customers.

This guide will walk you through setting them up. But more importantly, we'll explore how you can get all the power of custom automation without getting tangled in complex workflows that slow you down and require help from your engineering team every time you want to make a small change.

What you'll need before you start

Before diving in, it’s worth knowing that setting up this kind of automation in Intercom isn't exactly a one-click affair. Based on Intercom's own advice, you'll need a few things in place, which gives you a good sense of the project you're about to take on.

• Some technical know-how: While Fin Tasks are configured using plain English, the Data connectors that feed them are basically API calls. You’ll need a decent grasp of how APIs work, including things like endpoints, authentication, and JSON responses, to get them hooked up correctly.

• Help from other teams: This usually isn't a solo project for the support team. Getting the right data often means looping in your engineering or operations teams, especially if the APIs you need don't exist yet or need an update. Be ready for a bit of back-and-forth.

• A clear, high-impact use case: It’s best to start small. Find one or two repeatable, high-volume tasks your team currently does by hand. This focus is a good practice, but it also shows that this isn't a feature you can just switch on and have it work everywhere.

• Access to your other tools: Make sure you have the credentials, permissions, and documentation for any third-party systems you want your AI to talk to. Whether it's Shopify, Salesforce, or your company's internal backend, you'll need that info ready to go.

How to use Fin Tasks and Data connectors: A 4-step guide

Fin Tasks and Data connectors are designed to work as a pair. Think of Data connectors as single-purpose tools in your toolbox (like a screwdriver or a wrench), and a Fin Task as the instruction manual that tells your AI which tool to use and when.

Step 1: Set up your Data connectors to talk to other systems

First things first, you need to build your toolkit. Data connectors are the building blocks that let Fin interact with the world outside of Intercom. Each one is a single, specific API call you have to configure.

For instance, a "Check Order Status" Data connector would be one API call to your e-commerce platform that takes an order ID and returns its status. You'd need a completely separate connector to "Process a Refund" and another one to "Update Shipping Address."

Here's the general process:

1. Head over to the Data connectors section in your Intercom settings.

2. Create a new connector and set up the API call, which includes the endpoint URL, authentication method, headers, and any parameters it needs.

3. Define the structure of the data you expect to get back from the API.

You’ll have to repeat this for every single action you want your AI to take. It's the foundational step, but it's also where the initial technical work happens.

Step 2: Create a Fin Task to map out the workflow

Once you have your "tools" (your Data connectors), it's time to write the instruction manual. A Fin Task is where you lay out the logic, telling the AI how to handle a specific customer request from start to finish. It gets triggered when a customer's message matches an intent you've defined, like "I want to cancel my order."

The initial setup involves:

1. Navigating to the Fin Tasks area in your Intercom account.

2. Creating a "New task" and giving it a clear title and description. This tells Fin when it should consider using this particular workflow.

3. Training the trigger by giving it both positive ("cancel my order") and negative ("where is my order?") example questions. This helps Fin learn to tell the difference between similar but separate requests.

Getting this trigger logic right is a big deal. If it's too broad, the AI might start the wrong process; if it's too narrow, it might not trigger when it’s supposed to.

Step 3: Add instructions and connect your data

This is where you bring it all together. Inside the Fin Task, you'll use the "instructions block" to give Fin a step-by-step script to follow using natural language.

Let's imagine you're building an "Order Cancellation" task. The instructions might look something like this:

1. First, ask the customer for their order number and remember it.

2. Next, use the "Check Order Status" Data connector to see if the order can still be canceled.

3. If the status is "shipped," tell the customer it's too late to cancel. If it's "processing," ask them to confirm they want to go ahead.

4. If they confirm, use the "Cancel Order" Data connector to do the cancellation.

5. Finally, let the customer know the cancellation worked and a refund is on its way.

You can also use temporary attributes to hold pieces of information, like the order ID, as the AI works through the steps.

Step 4: Test your workflow with simulations

Before you let this run loose on real customers, you need to make sure it actually works. Intercom provides a simulation feature to help you test your Fin Tasks in a controlled setting.

Here’s how it works:

• You create a test case by writing a user's opening message and picking a user persona.

• You can add extra context and decide what data should be available, basically faking the responses from your Data connectors.

• You then set "Success criteria" to define what a successful outcome should look like.

This is a pretty helpful way to check if a specific path works as expected (the "happy path"). The catch is that it's a manual process you have to repeat for every single scenario you can think of.

A screenshot showing the simulation feature in Intercom for testing workflows built with Fin Tasks and Data connectors.

Real-world challenges of using Fin Tasks and Data connectors

While the process above seems logical enough, building and maintaining these workflows can bring some hidden headaches.

• You're going to rely on engineers... a lot: Even though the instructions are written in plain English, the whole system is built on Data connectors that require someone comfortable with APIs to set up, maintain, and fix. This often creates a bottleneck where the support team has a great idea for an automation but has to get in line and wait for an engineer to build the parts.

• Testing is tough to do well: Manually creating a simulation for every possible customer scenario is incredibly time-consuming. You can test if the workflow succeeds when everything goes perfectly, but it's hard to feel confident about how it will handle the thousands of unpredictable conversations where things don't go according to plan.

• The workflows can be a bit rigid: Workflows built as a series of linear steps can be brittle. If a customer asks an unexpected question in the middle of a task, or if an external API you rely on changes slightly, the whole thing can just stop working. This usually ends with a frustrated customer having to start over with a human agent.

A simpler way to build custom AI actions

What if you could get all the power of custom, multi-step automation without the technical hurdles and testing nightmares?

That’s the idea behind eesel AI. It’s a platform designed from the ground up to give you the same power as tools like Fin Tasks but with a focus on simplicity and letting you do it all yourself.

Here are a few key differences that tackle the challenges we just talked about:

• Go live in minutes, not months: Forget waiting on other teams. With eesel AI, the setup is genuinely self-serve. You can connect your helpdesk with a single click and have a working AI agent running in minutes, no mandatory sales calls or developer time needed.

• Full control with a true workflow engine: Instead of building individual Data connectors for every little thing, eesel AI has a dedicated system for AI Actions. From an intuitive interface built for support pros, you can easily set up API calls to look up order info, trigger workflows in other tools, or update ticket fields.

• Test with confidence on your real history: This is a huge one. Instead of manually creating a few simulations, eesel AI's powerful simulation mode lets you instantly test your setup on thousands of your actual past tickets. You get an accurate forecast of its resolution rate and can see exactly how the AI would have responded in each case. This gives you complete confidence before you ever turn it on for customers.

Feature	Intercom Fin Tasks & Data connectors	eesel AI
Setup Speed	Needs technical setup for APIs and help from engineering.	Truly self-serve; connect your helpdesk and go live in minutes.
Custom Actions	Requires creating individual Data connectors for each API call.	Intuitive AI Actions builder for real-time data lookups and workflows.
Testing	Manual simulation of individual scenarios.	Automated simulation on thousands of historical tickets with ROI forecasts.
Knowledge	Relies on manually connected sources and configured data.	Instantly unifies knowledge from past tickets, help centers, and docs.

From complex setups to confident automation

Automating tricky, multi-step problems is the next big step for AI in customer support. It’s what separates a simple chatbot from an agent that can actually solve things on its own.

While tools like Intercom's Fin Tasks and Data connectors offer a path to get there, the approach can be complicated, lean heavily on your technical team, and be difficult to launch with 100% confidence.

eesel AI offers a different way forward, one that empowers support teams to build, test, and launch powerful AI automations on their own terms. It’s all about getting better results for your customers with less effort and less risk for your team.

Ready to build powerful AI actions without the complexity? Try eesel AI for free and see how our powerful simulation and self-serve tools can help you automate your most complex workflows with confidence.