How to automate customer service with AI agents in 2026

Automating customer service means using software and AI agents to handle repetitive support work, like answering FAQs, checking order status, or processing returns, without a person doing it manually each time.

Most teams start by connecting a helpdesk, such as Zendesk or Freshdesk, to an AI agent that can reach into the other systems a ticket touches, like an ecommerce platform or a CRM.

Make lets you build that connection visibly, with full control over what the agent can see and do at each step. 

This guide shows you how to automate customer service so routine requests, like order status checks and return questions, get handled without a person doing it by hand each time.

What does it mean to automate customer service?

Automating customer service means using software and AI agents to complete support tasks that don't need human judgment.

That includes answering FAQs, checking order status, tagging tickets, and starting a refund.

In practice, most teams automate a mix of the following:

• Answering repetitive questions from a knowledge base

• Routing tickets to the right queue or agent

• Looking up order or account status in another app

• Starting a refund, replacement, or return

• Notifying a customer of a delay or status change

The goal isn't to remove people from support.

It's to free them up for the tickets that need empathy or judgment, while automation handles the rest.

How do AI agents automate support, orders, and ecommerce differently?

Each area of customer service has a different data source and a different risk if automation gets it wrong, so the agent's job changes across the three.

Support automation mostly answers questions.

Order and ecommerce automation mostly takes action in another system, which is why the AI agent's tools matter as much as its instructions.

What are the building blocks of a customer service automation?

Every customer service automation needs four parts, whether it's a single reply bot or a full agent.

The first is a trigger: a new ticket, a form submission, or a scheduled check. The second is routing, which sorts requests by type before anything acts on them.

The third is the action layer, where an AI agent reasons about the request and calls the tools it needs, like looking up an order or drafting a reply. The fourth is an escalation path, so anything the agent isn't confident about reaches a human with full context.

Skip any one of these and the automation either mishandles edge cases or never scales past a handful of FAQ answers.

What can you automate in order and ecommerce support?

Order and ecommerce tickets are a good place to start because the answer usually lives in one system: your store platform.

On Shopify, that means an agent can resolve most of these without a human touching the ticket:

• Order status and tracking lookups

• Delayed shipment alerts sent before the customer asks

• Return and refund initiation once a return is confirmed

• Abandoned cart follow-up messages

• Review requests sent a few days after delivery

Each of these pulls from the same order record, which makes them a reliable first automation, before moving on to messier, open-ended support questions.

How do you build a customer service automation in Make?

The build below routes new support tickets, resolves order questions with an AI agent, and escalates anything the agent can't handle. It uses Zendesk for tickets, Shopify for order data, and Slack for escalations, but the same structure works with Freshdesk or Intercom and any ecommerce platform.

Step 1: How do you trigger the automation from a new ticket?

Start the scenario with Zendesk's Watch Tickets module, which fires each time a new ticket comes in.

Step 2: How do you route tickets by type?

Add a Router directly after the trigger, then filter the connector running into it so only tickets tagged as order-related or general support continue.

From there, filter each route leaving the Router separately: one route for order questions, one for everything else.

Step 3: How do you set up the AI agent and its tools?

On the order-question route, add a Make AI Agents module and give it two tools: Shopify's Search Orders module to find the order, and Zendesk's Update a Ticket module to post the reply.

Step 4: How do you handle the order status branch?

When the agent finds the order, it drafts a reply with the current status and tracking link, then posts it as a comment through Zendesk's Update a Ticket module. 

Keep the ticket open for one more customer reply before auto-closing it, in case the answer doesn't resolve the question.

Step 5: How do you handle the escalation branch?

If the agent can't find the order or isn't confident in the reply, route the ticket to a human instead of guessing. 

Add a Slack Send a Message module to notify the assigned queue, then use Zendesk's Update a Ticket module to tag the ticket for human follow-up.

Native helpdesk AI vs. Make: what's the difference?

Zendesk, Freshdesk, and Intercom each ship their own AI layer, and it's worth using for what it's built for.

Neither replaces the other. Most teams keep the native AI for first-line replies and add Make when a ticket needs data from more than one app, with no separate Pro-tier add-on required since Make AI Agents ships on every paid plan.

What are two real-world use cases for automating customer service?

These two examples show the same pattern at work: an AI layer reads the request, decides what it's worth acting on, and only pulls in a human for what actually needs one.

How did Stellantis&You UK cut 151 hours of work with sentiment-based escalation?

Stellantis&You UK, a UK car dealership group, built a two-way SMS support flow in Make using Knack, ClickSend, and OpenAI's sentiment analysis. 

Over 12 months, the automation analyzed more than 47,000 customer messages and auto-closed over 18,000 of them that needed no action, saving an estimated 151 hours of manual review.

Even when expressed politely, genuine dissatisfaction was flagged directly to the Aftersales team, as shown in Make's Stellantis&You UK success story.

How can a fraud check stop refund abuse before it costs you?

Refund fraud, like a customer falsely claiming an order never arrived, is a well-documented cost of doing business in ecommerce.

One athletic apparel retailer that routed "item not received" claims through automated risk decisioning, rather than manual review, prevented four times more abuse and saved $600,000 a year, recovering 12% more refunds, according to Riskified's refund fraud data.

That retailer used a dedicated fraud-decisioning platform, not Make, but the same branching logic is buildable in a scenario: a filter checks a customer's order and claim history before a refund route runs,.

This sends low-risk claims to auto-approval and flagging repeat "item not received" patterns for a human to review.

What are common pitfalls when automating customer service?

A few mistakes show up often enough to call out before you build.

• No escalation path, so the agent guesses instead of handing off a ticket it can't resolve

• Automating a question before checking that the data behind it is accurate and current

• Auto-closing tickets the moment a reply is sent, before confirming the customer's issue is solved

• Treating every ticket the same, instead of routing by type and risk first

Most of these come down to skipping the routing or escalation building blocks covered earlier, not a flaw in the AI agent itself.

How do you know if the automation is working?

Ticket volume handled isn't the metric that matters. Confirmed resolution is.

Track how many tickets the automation closes without a re-contact, alongside CSAT on those tickets specifically, not just overall CSAT.

Watch escalation quality too: when the agent hands off a ticket, check whether the human agent had the context to resolve it quickly, or had to start over.

Review these numbers monthly for the first quarter, then quarterly once the automation is stable.

What's your next step to automate customer service?

Start with one route: order status questions, handled by an AI agent with two tools and a clear escalation path.

Once that route is resolving tickets reliably, add the next one, whether that's returns, delayed shipments, or general FAQs.

Explore Make's customer experience automation tools, browse the best AI agents for customer support, or follow the guide to building your first AI agent to get started.

Frequently asked questions

Q1: What's the difference between a chatbot and an AI agent in customer service?

A chatbot follows a fixed script or decision tree. An AI agent reasons about the request and chooses which tool to use, like looking up an order, rather than following one set path.

Q2: Can you automate customer service without replacing your helpdesk?

Yes. Make connects to Zendesk, Freshdesk, Intercom, and most helpdesks directly, so you add automation and AI agents on top of the system you already use.

Q3: Which customer service tasks should you automate first?

Order status lookups and other single-system questions are the easiest starting point, since the answer lives in one app and the risk of a wrong answer is low.

Q4: Does automating customer service require a specific Make plan?

No. Make AI Agents run on every paid plan, from Core through Enterprise, so there's no separate tier to unlock the feature.

Q5: How do you stop an AI agent from giving a wrong answer to a customer?

Build an escalation route for low-confidence answers and missing data, and review a sample of agent replies weekly until you trust the pattern of what it gets right.