Customer Support AI Agents: What They Are and How to Build Them for Best Results

Customer support teams are feeling the increased pressure from a higher volume of customer tickets. Whether it’s resolving a complex service claim or simply answering a quick question about a purchase, customers expect timely and accurate responses. It's no surprise that 82 percent of service reps say customers are asking for more help than they used to. The big challenge now is finding a way to deliver excellent service without compromising on quality.  

But with customer support AI agents, the smart tools can help organizations meet this challenge, improve customer satisfaction, and cut response times. In fact, more than 66 percent of businesses are now adopting an AI agent for customer service.  

However, the success of an artificial intelligence (AI) agent depends on the quality of its underlying data, the workflows that are in place, and having clear goals. To make this happen, you’ll want a secure and reliable platform, such as Domo, that supports all these requirements. 

In this article, we’ll explain what a customer support AI agent is and demonstrate what it can do. We’ll then share a framework to help you build your own AI agent using Domo’s data connectors and Agent Catalyst. 

Let’s start with the basics and first define what a customer support AI agent really is. 

What is a customer support AI agent? 

A customer support AI agent is an intelligent system that can interact with customers, resolve their queries, and simplify support operations. It uses large language models (LLMs), machine learning, and natural language processing (NLP) to handle a range of tasks, from answering simple questions to multitasking. 

Unlike simple automation tools, an AI customer support agent can improve its own performance over time through self-learning. It can also handle omnichannel support (engaging customers through SMS, WhatsApp, and Facebook Messenger) and connect directly to your business data to make decisions and take actions. 

An agent’s ability is defined by how well it can solve problems as we do. To achieve this, it performs three key functions. 

1. Reasoning: An AI-powered customer service agent does more than keyword matching through its use of a language model. The agent draws on the LLM to understand the intent, sentiment, and context of the customer inquiry. For example, when it sees an email that says, “My package never arrived, and my next subscription renewal is in two days”, it recognizes that the customer is facing two separate issues that require different actions and responses. ‍
2. Planning: Once the agent understands the goal, it breaks it down into logical steps. For the example above, it might first check the shipping status of an order in the logistics database. Next, it would look up the customer’s subscription details in the CRM. Finally, based on its findings, it decides the appropriate actions for both the missing package and the upcoming renewal. ‍
3. Acting: The agents are usually integrated with external tools and systems to take various actions (carry out their plan). For example, it can log into your Zendesk or Salesforce Service Cloud, look up information in a Snowflake database, trigger a workflow, or even write and send a personalized email to a customer without a person having to do it. 

Types of support agents 

AI agents serve two key roles in a support organization, each providing distinct value: 

Autonomous agents: The frontline responders 

Autonomous agents take action without human intervention to handle entire workflows from start to finish. They’re perfect for resolving high-volume, predictable inquiries because once given a goal, they can generate tasks for themselves and work on them until the goal is reached.  

Autonomous agents can work around the clock (24/7), so customers get quick answers without having to wait for a person. 

Assistive agents: The agent’s co-pilot 

Assistive agents work alongside human agents to help them make decisions and provide support in managing tasks. For example, during a live phone call or chat, an assistive agent can listen in and transcribe the conversation in real-time. 

It can also surface relevant information on the agent’s screen and even write responses for the human agent to review, edit, and send. This helps reduce the time it takes to handle calls and ensures more consistent responses. 

With a solid grasp on the fundamentals of AI agents, let’s explore their impactful scenarios. 

When and why your support organization needs an AI agent 

Choosing to use AI agents is a strategic decision to meet specific business needs. As support teams face more pressure to do more with less, AI agents provide a scalable way to improve operations and offer better customer service.  

The move to use AI-powered customer service brings impact to situations where you face high volumes of repetitive queries, need multichannel support, and want to provide service across different time zones. These benefits translate directly into the key performance indicators (KPIs) that every support leader tracks: 

• Efficiency: Agents automate routine tasks that consume the majority of a support team’s time and allow them to prioritize more complicated cases. Customer service automation lets you handle higher volumes of inquiries and multiple customer interactions in parallel.  ‍
• Cost savings: You can greatly cut operational costs by automating Tier-1 support and minimizing the manual work human agents do. This also reduces costs associated with employee turnover, training, and the time spent on low-value, repetitive inquiries. ‍
• 24/7 availability and instantaneous responses: Customers want support whenever and wherever they need it. AI agents are available around the clock, so no question goes unanswered. The immediate responsiveness greatly improves customer loyalty and can give a business an edge over competitors. ‍
• Scale Easily: Equipped to manage large volumes of customer support requests, AI agents scale easily with expanding businesses, decreasing customer wait times, and resolving issues more quickly. ‍
• Data-driven insights: AI agents proactively collect data on customer interactions, their preferences, and behaviors that would be impossible for a single human to track. With this information, businesses can gain valuable knowledge about what customers are looking for and improve the quality of their service. 

To appreciate the advantages of AI agents, it’s essential to compare them with more conventional solutions. 

AI agents vs traditional automation and chatbots: A critical difference 

Many organizations believe an AI agent is simply a chatbot or the simple, trigger-based automation common in help desk software. While those tools are helpful, they aren’t the same as an AI agent. So, it’s important to understand the difference to see how powerful agentic AI can be. 

Let’s first see the difference at a glance: 

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Chatbots: The conversational front door 

Most chatbots are conversational tools and answer frequently asked questions through matching keywords to a pre-written script or knowledge base. While newer chatbots use NLP to hold more natural conversations, their primary function remains information retrieval and ticket deflection.  

They usually operate in a single channel (like your website) and struggle when a query requires them to interact with multiple backend systems. 

Automation rules: The simple “if-this-then-that” engine 

Automation rules are simple if-then logic commonly used in help desks and customer relationship management (CRM) systems. For example, if a ticket’s subject line includes the word “billing”, then the system assigns it to the finance team.  

This system operates strictly based on rules and can’t act on more subtle signals like sentiment, context, or complex situations. Automation rules also struggle with tasks that involve multiple steps or data from different sources. 

AI agents (Agentic AI) 

AI agents perform agentic operations, essentially becoming part of an organization’s workforce. Unlike other automation tools, AI agents can reason, plan, and act independently to reach a goal.  

AI agents can also handle predictive tasks and problem-solve, can be trained to learn industry-specific terms, and can pull relevant information from a company’s knowledge bases. For example, while a chatbot might deflect a query, an agent could analyze sentiment, update CRM records, and prevent future issues.  

What can a customer support AI agent actually do? 

AI agents are most useful when they’re applied in real-world situations. When integrated with your essential business systems, they can automate many support tasks, saving time and effort. 

Real-time agent assistance and guidance 

AI agents can act as a co-pilot to make your human team dramatically more effective. Instead of replacing people, this model augments their skills by doing the most time-consuming parts of their job. 

For example, we at Domo use AI to make our support ticket process more efficient, moving away from manual methods to an AI-driven workflow. 

We faced hundreds of daily support tickets, and our engineers struggled with the manual process of searching across various resources to find the right information. As volume grew, this created a bottleneck that slowed down response times. 

To solve this, we developed an AI-powered customer service agentic workflow directly within the Domo platform. This turns a lengthy manual search into a quick review, allowing our team to respond with incredible speed and accuracy. Over time, the agent gets smarter by adding the context from resolved tickets back into its knowledge base. 

Proactive support and issue prevention 

An AI agent can be configured to monitor your product usage data. If it detects that a user is having trouble with a feature, it can automatically open a support ticket and send the user an email with a tutorial link. This helps fix the problem before the user gets frustrated. 

Intelligent ticket triage and routing  

Agents automatically parse tickets from different sources, assessing sentiment, urgency, and topics to route them efficiently. This eliminates manual sorting and reduces wait times. 

For example, many shipping companies have used agentic AI that acts on their behalf to cut onboarding paperwork from four hours a week to just 30 minutes by intelligently directing documents. 

Sentiment analysis and escalation 

An AI agent can monitor all active, live customer conversations. If it detects escalating frustration, anger, or language that indicates a churn risk, it can immediately and automatically flag the ticket for priority handling. It ensures that your most at-risk customers receive immediate attention from a senior agent and prevents a minor issue from becoming a major one. 

With these insights into their capabilities, let’s see how to build your own AI agent. 

How to build a customer support AI agent: A 6-step guide 

Building your first AI agent is easy and you don’t need to write a lot of deep, extensive code. Instead, it’s about having a clear goal and using a modern AI platform like Domo that lets you go from idea to a working agent. Here’s how. 

Step 1: Define your goal and start small 

The most common mistake is trying to automate everything at once. It’s better to start with one specific task that is high-volume, repetitive, and has a clear path to resolution. Good starting points include handling WISMO inquiries, resetting passwords, or answering simple questions.  

Also, clearly define what success looks like using measurable KPI. For example, do you want to cut the first response time in half? Or automate 30 percent of all incoming tickets? A clear target will guide your entire building process. 

Step 2: Prepare your data foundation 

An AI agent's intelligence and reliability depend on the data it can access. You should gather data from all relevant sources, including your help desk (Zendesk, Intercom), CRM (Salesforce), knowledge base, product usage logs, and logistics databases, into a single, unified location. This data must be clean, up-to-date, and organized in a way the agent can understand.  

• How Domo helps consolidate data: Our platform provides an extensive library of over 1,000 pre-built data connectors, which allow you to easily pull live data from all your critical systems into one governed environment. Using our intuitive low-code tool, Magic ETL, your teams can then clean, combine, and transform this data to create the single source of truth your AI agent needs to make accurate and trustworthy decisions. 

Step 3: Choose your architecture and tools 

Next, you’ll select the core components for your agent. This includes an LLM to serve as the “reasoning engine” (like OpenAI’s GPT-4 or Anthropic’s Claude), a method to securely connect your private data to the LLM, and a set of “tools” (APIs) the agent can use to take action in other systems. 

• Domo solution: Domo’s Agent Catalyst is a purpose-built framework that simplifies this step. It allows you to bring your own model (BYOM) from any major provider or use DomoGPT. Within Agent Catalyst, you can easily point the agent to your trusted Domo data sets for knowledge and assign it tools by connecting it to Domo’s Intelligent Automation Workflows, enabling it to perform actions securely, like sending an email or updating a database. 

Step 4: Design the agent’s logic and guardrails 

Here, you map out the agent’s exact workflow and give it clear instructions through natural language (prompting). You’ll define its persona, its goal, and the steps it should follow.  

Most importantly, you must establish its safety limits and escalation paths. What should it do when it’s uncertain? What actions is it prohibited from taking without human approval? These guardrails are essential for building trust and ensuring responsible automation. 

• How Domo helps design the agent’s logic: Agent Catalyst provides a structured environment to define these rules. You can create detailed prompts that instruct the agent on its tasks and constraints. Because the agent is built natively within the Domo platform, it inherits Domo’s governance features. You can set strict, permission-based “guardrails” to ensure the agent only ever accesses the data and takes the actions you’ve explicitly authorized. 

Step 5: Prototype, test, and iterate 

Before deploying an agent to interact with customers, build a prototype and test it rigorously with an internal pilot team (possibly using your support team as “customers”). Log every decision the agent makes, analyze its successes and failures, and gather feedback from the pilot team to continuously refine its prompts, logic, and tool usage.  

• How Domo helps in prototyping and testing: The integrated nature of Domo makes prototyping fast and safe. You can build and test your agent within a sandbox environment using the same governed data it will use in production. You can then write the agent’s outputs and decision logs directly back into Domo DataSets, which helps you to create real-time monitoring dashboards (Alert Center) to track its performance, accuracy, and error rates throughout the iteration phase. 

Step 6: Deploy, monitor, and govern 

Once you are confident in the agent’s performance and reliability, you can deploy it into your live support workflow. But you must continuously monitor its real-world impact on your KPIs. Track its accuracy, resolution rates, and compliance with your established guardrails to ensure it continues to operate as intended and delivers the value you expect. 

How to learn more about AI agents for customer support 

Customer support AI agents are valuable assets that can change how your support team works. They free up your workforce to focus on building relationships, solving complex problems, and providing quality customer service by taking care of routine, predictable tasks. 

Getting started with your first AI agent doesn’t have to be complicated or take many years. If you start with a clear goal, use quality, unified data, and choose a platform built for responsible AI, you’ll yield benefits for your team and customers in weeks.  

To see how AI agents are transforming business workflows, watch our Future of AI series.  

And for a more technical overview, check out our complete guide on How to Build an AI Agent and explore Agent Catalyst to create one.