What is a Virtual Agent? Everything You Need to Get Started

In the context of a service desk, a virtual agent is an automated, conversational tool designed to interact with users and assist with common service requests, incidents, or questions. 

Unlike static FAQs or ticket forms, virtual agents can interpret user input (usually through natural language processing) and provide real-time responses. They’re typically integrated into ITSM platforms to help users reset passwords, check ticket status, submit new incidents, or get help with known issues, without involving a human agent right away.

They’re increasingly used to automate IT support, taking over high-volume, repetitive interactions so support teams can focus on more complex tasks. Available 24/7, virtual agents help reduce response times, improve consistency, and keep service desk operations running smoothly even during off-hours.

Virtual agent vs. chatbot vs. virtual assistant

These terms are often used interchangeably, but they serve different purposes and offer varying levels of functionality:

• Chatbot: A basic script-based tool that responds to predefined commands or keywords. If you’ve ever interacted with a site bot that only recognizes exact phrases like “hours of operation,” that’s a service chatbot. It can’t understand much beyond what it’s been explicitly programmed to answer.
   
• Virtual assistant: A broader term for tools that help users complete tasks through voice or text. These can include anything from setting reminders to checking schedules. They're designed for general-purpose interactions and aren't tied to a specific business function like IT or HR.
   
• Virtual agent: More sophisticated than a chatbot and more specialized than a virtual assistant, a virtual agent is tailored for business environments. It uses natural language understanding to interpret user intent, integrates with backend systems (like a knowledge base or ticketing system), and resolves user issues or escalates them when needed.

4 benefits of implementing virtual agents

Virtual agents are already transforming service desk operations with measurable improvements to both efficiency and user experience. These benefits will only grow stronger as the technology advances — Gartner predicts that using AI for common support will lead to a 30% reduction in operational costs by 2029. 

Let’s review some of the benefits of using AI virtual agents as part of your ITSM automation efforts:

1. Offloads routine requests: Virtual agents handle things like password resets, access requests, and status updates — tasks that otherwise eat into your team’s time. It’s a way to reduce volume without changing how users interact with support.
    
2. Improves ticket quality: Because the virtual agent asks the right questions up front, tickets arrive with better context. Agents spend less time chasing down missing info and more time resolving the issue.
    
3. Extends support availability: Even when no one’s on the clock, the virtual agent can collect requests, answer common questions, or trigger predefined workflows to get things moving.
    
4. They create an entry point for automation: Each conversation can kick off actions behind the scenes: assigning tickets, sending updates, or escalating based on logic you define. It turns user input into structured tasks without manual effort.
    
5. They can surface gaps in documentation and processes: When users ask questions the agent can’t answer, those interactions highlight what’s missing in the knowledge base or what processes haven’t been formalized. It helps teams fix not just the one-off request, but the source of friction.

How do AI virtual agents work?

AI virtual agents function through a combination of natural language processing, machine learning, and automation technologies. At their core, these systems process user input through multiple layers of analysis to understand intent, retrieve relevant information, and generate appropriate responses.

The technical foundation begins with natural language understanding (NLU), which breaks down user queries into structured data. The system identifies key entities, determines user intent, and extracts context from the conversation. Machine learning models, trained on vast datasets of previous interactions, help the agent recognize patterns and predict what users actually need, even when their requests are vague or incomplete.

Service desk virtual agents can handle first-line support tasks that follow predictable patterns, such as account unlocks, software installation guidance, and basic troubleshooting. At the same time, they can create and update tickets and follow organizational workflows.

Behind the scenes, virtual agents need a knowledge base containing policies, procedures, troubleshooting steps, and frequently asked questions. When processing requests, the agent searches this repository using semantic matching algorithms that go beyond simple keyword searches. 

For example, the agent can understand that "my computer won't start" relates to hardware troubleshooting procedures, power issues, and system diagnostics. It can help the user through troubleshooting steps, and ask if the problem is solved. If not, it can capture conversationally all the history it needs to create a ticket with detailed context about what was already attempted.

How to get started with a virtual agent

Implementing a virtual agent doesn’t require starting from scratch, but it does call for thoughtful planning. Here are some steps to guide the process:

1. Identify high-volume, repetitive support tasks: Look at service desk data to find common requests, such as password resets, account unlocks, software access, etc. These are usually the best candidates for automation through a virtual agent.
    
2. Define the virtual agent’s scope: Decide what the agent should handle. Start small with a focused set of use cases, and expand as adoption grows.
    
3. Select a platform or vendor: Some ITSM platforms include built-in virtual agent capabilities. Others allow for third-party integration. Make sure the tool fits with your existing infrastructure and knowledge base.
    
4. Prepare content and workflows: A virtual agent is only as good as the knowledge and processes behind it. Review existing articles, templates, and automation rules to ensure the agent can respond accurately.
    
5. Test and train: Use historical queries and user feedback to refine how the agent responds. Adjust language models, tune workflows, and run internal pilots before rolling it out more broadly.
    
6. Monitor and iterate: After launch, continue updating the agent based on how users interact with it. Improvement is ongoing — especially if the agent uses AI or machine learning.

5 virtual agent use cases

Virtual agents can support a wide range of IT tasks, especially those that are repetitive, time-sensitive, or easy to automate.

Here are several common use cases where virtual agents can make a noticeable impact:

1. General troubleshooting: Users often encounter common technical issues, such as slow performance, application errors, or connectivity problems. A virtual agent can guide them through basic troubleshooting steps or direct them to relevant articles without creating a ticket unnecessarily.
    
2. Service and support requests: Instead of navigating a portal or filling out forms, users can ask the virtual agent for help directly. It can collect the necessary details, create the request, and provide updates as it progresses.
    
3. Status updates and follow-ups: Virtual agents can answer questions like “What’s the status of my ticket?” or “Has the issue been resolved yet?” without involving a human agent. This improves transparency and cuts down on repeated follow-up requests.
    
4. Policy and how-to questions: Virtual agents can be connected to a knowledge base to respond to recurring questions about IT policies, software usage, or internal processes, helping users find accurate information on their own.
    
5. Incident reporting: When something’s not working, users can report the issue directly through the virtual agent. The agent helps collect the right context upfront and routes the incident to the correct team automatically.

“In the IT Service Management space, what we have seen is that one of the main issues is to get the right strategy on the multichannel and omnichannel strategies within the organizations. (...) And this is going to be an additional channel of communication for you. This “robot” will be connected to your ticketing system, it will go through incidents, through requests, through change, through configurations."

Mauricio Corona, BP Gurus - Episode 10 of Ticket Volume

How to measure an AI virtual agent’s success?

Knowing whether the virtual agent is working well goes beyond counting how many tickets it handles. Some useful indicators include:

• Containment rate: How many conversations did the virtual agent resolve without needing to hand off to a human? A high containment rate means it’s handling tasks effectively.
   
• Time to resolution: Track whether interactions handled by the virtual agent are resolved faster than those handled by agents. Shorter times usually mean users are getting what they need more efficiently.
   
• User satisfaction: Use quick thumbs-up/down feedback or short surveys to see how users rate their experience. However, qualitative feedback should also be gathered beyond simple yes/no responses, including details about tone, helpfulness, and specific aspects of the interaction.
   
• Volume deflection: Compare support ticket volume before and after implementation. A noticeable drop suggests that the agent is successfully handling tasks that used to create tickets.
   
• Adoption and usage trends: Low usage might mean users don’t know it exists, or that it’s not meeting their needs. Steady growth over time is usually a good sign.

Discover InvGate’s Virtual Service Agent

InvGate’s Virtual Service Agent offers a modern, AI-driven support experience built for everyday service desk needs. It’s designed to interact naturally with users, understand their requests through plain language, and provide help instantly, without needing specific commands or form-based input.

Here’s what sets it apart:

• Natural conversation flow: The Virtual Service Agent offers an intuitive chat experience. Users can describe their issues just like they would to a coworker, and the agent interprets their intent using natural language processing (NLP), even if they don’t use formal request terms.
   
• Smart question handling: It pulls answers from your organization’s knowledge base and other sources to respond to common questions. Whether it’s a policy query or a how-to, users get reliable information instantly.
   
• Automated request creation: When a user describes an issue or need, InvGate’s Service Agent automatically fills out the relevant request fields based on the conversation — no manual form entry required. If the issue can’t be resolved via the self-service portal, it can help move the request forward by creating a ticket.

InvGate’s virtual agent supports a broad range of use cases, starting with common IT support needs such as basic troubleshooting (“my printer is not working”) or equipment requests (“I need a new keyboard”). It can guide users through solutions using knowledge base content, and if needed, create a ticket with the relevant information.

But you don’t have to stop at IT. If your organization adopts an Enterprise Service Management (ESM) approach, the virtual agent can assist across other departments as well. For instance, an employee asking “How do I request a PTO?” will receive step-by-step guidance and the option to submit the request directly.

"Every industrial revolution didn’t replace people; it changed the way they worked and allowed them to deliver more value."

Ariel Gesto, CEO and co-founder of InvGate