What is a virtual agent?

Key takeaways: A virtual agent is AI-powered software that handles service requests in natural conversation — understanding what the user needs, resolving common issues like password resets and access requests on its own, and handling the rest to a human with full context.

• What it is: A step beyond a rules-based chatbot: it uses natural language processing and machine learning to interpret intent and sentiment, works inside chat tools and portals, and answers from a knowledge base rather than a script.

• Why it matters: Tier-1 requests are most of a service desk’s volume; deflecting them gives agents time for complex work and gives users an answer in seconds instead of a place in a queue.

• How Jira Service Management helps: Its virtual agent, powered by Rovo, runs in Slack, Microsoft Teams, and the customer portal, answers from Confluence knowledge, and escalates to a human agent with the conversation attached.

A virtual agent is AI-powered software that handles service requests in a natural conversational style. Where a basic chatbot follows a fixed script, a virtual agent uses natural language processing (NLP) and machine learning to understand what a user is asking, answer from a knowledge base, complete routine tasks such as password resets and access requests, and hand anything it can’t resolve to a human agent with the full context attached.

For a service desk, that means automating most tier-1 interactions and deflecting repeat questions, while also supporting the human agents — summarizing requests, triaging by severity, and surfacing what they need to resolve complex issues faster. This guide covers how virtual agents differ from virtual assistants, their benefits, how to train and deploy one, and examples.

Virtual agent vs. virtual assistant

A virtual assistant can refer to either a person who works remotely on administrative tasks or a consumer voice assistant such as Siri or Alexa. In both senses, it's general-purpose; a virtual agent is built for a specific service context and connected to that organization's knowledge and workflows.

Benefits of a virtual agent

Virtual agents give time back to your team to focus on higher-value work. A common task eliminated by using a virtual agent is triaging. IT professionals waste time tagging teammates and reassigning tickets before work can start. A virtual agent can get the right ticket to the right person at the right time. Beyond automated messaging capabilities, machine learning enables a virtual agent to communicate with your customers in a more human-like way. As soon as a ticket is created, a virtual agent begins working with your customers to identify the root of the issue. And when an issue needs escalation, the triaging and communication with teammates begin immediately.

Natural language processing

Natural language processing (NLP) is the ability of a computer program to understand human language, whether spoken or written. Your customers will give clues to how they are feeling and the severity of the issue with the words they use in a service request. NLP enables a virtual agent to determine severity and sentiment with greater accuracy. A virtual agent will use sentiment analysis to escalate issues and seamlessly transfer them to humans who are available to help. 

Voice assistants

Do your customers have a hotline they can call? Voice assistants can offer high levels of service by learning as much as possible about the customer and their issue before escalating to a teammate. Virtual agents elevate this experience by listening to how your customers respond and interpreting the tone of the conversation. Imagine a voice assistant that communicates the customer's issue and level of distress before you even pick up the phone.

How to use a virtual agent

Virtual agents follow rules to provide answers and manage communication based on customer inputs. Many service management teams already have flow charts and customer escalation processes that you can train your virtual agent on. Training and testing are a collaboration between the virtual agent and the team. The team needs to decide how they want to collaborate and ensure the virtual agent interacts with customers in ways that meet company standards.

Training a virtual agent

Training has long been the domain of data scientists and machine learning experts. Today’s virtual agents can be trained without code or specialized knowledge. Training involves digesting and analyzing a rule set and creating a model for future use. Once trained, teams should interact with the virtual agent in a sandbox environment. If you're unsatisfied with your test interactions, you can adjust and reconfigure your training data until your interactions are satisfactory. 

Integration with third-party channels

One of the challenges of service management is the many channels your customers use to reach you. Most service management software centralizes service requests from phone, email, text, Slack, and web channels. Teams then work on the requests and use the same service management software to communicate back to internal and external customers. What’s been missing is the software’s ability to match the tone, length, and conversational style of each third-party channel. A virtual agent solves this problem by deep integration with third-party communication channels and an intelligent understanding of how to communicate effectively in each channel. 

Conversational analytics

Conversational analytics is the study of social interactions that investigates what it takes to create mutual understanding between two parties. This branch of study has identified trends in the content and tone of conversations that help us better understand our customers' satisfaction. Rather than sending someone a customer satisfaction survey, conversational analytics gives you a real-time view of customer satisfaction and identifies the moments when it increases or decreases. Conversational analytics can serve as an escalation tool, notifying human teammates when the virtual agent needs help. 

Multilingual support

Google Translate is great in a pinch, but it is not sufficient for customer communication. Until recently, automated translation services mostly replaced words with their translations. This led to embarrassing contextual errors and poor communication. Using AI and NLP, virtual agents can assess the full intent of a statement and find the linguistic equivalent. A virtual agent can navigate natural language with more proficiency and allow you to serve more customers in more areas. 

Examples of virtual agents

Knowing what you know now, have you worked with a virtual agent? It’s possible that you’ve emailed a virtual agent without even knowing it. Understanding where and when virtual agents are being used successfully can help you decide the best uses for virtual agents in your business. 

Virtual agent for customer service

The goal of a customer service agent is to understand a customer's problem, determine the severity, and gauge their emotional state. A well-trained virtual agent can quickly interpret all three. Imagine a virtual agent running a software company's service desk. An outage begins, and customers immediately flood the service desk with tickets. Human customer service agents need to open each ticket before confirming that they are all related. A virtual agent can do that in an instant, fast-tracking the escalation of the issue. 

Virtual agent for sales and marketing

Cold emails and lead generation are time-consuming activities done by some of our most expensive employees. Imagine a world where a virtual agent actively learns about potential customers and engages them with personalized messaging. When an opportunity arises during the virtual agent's communication, a human salesperson is brought in to close the deal. 

Virtual agent for finance

Financial teams field a steady stream of the same questions — expense policy, reimbursement status, payroll dates, and how to submit an invoice. A virtual agent in the finance service portal answers them from the team's knowledge base and routes exceptions to the right person, so the finance team isn't a help desk for its own processes.

Virtual agent for HR

HR service requests are high-volume and repetitive: benefits questions, PTO balances, policy lookups, and onboarding steps. A virtual agent handles these in Slack or Teams, points employees to the right form or article, and opens a ticket with an HR specialist only when a case needs one.

Virtual agents in Jira Service Management

Jira Service Management includes a virtual agent powered by Rovo, Atlassian's AI platform. The virtual agent operates inside Slack, Microsoft Teams, and the JSM customer portal, deflecting repetitive tier-1 requests - password resets, access provisioning, status updates, policy lookups - without requiring code. It draws on your existing knowledge base in Confluence, uses natural language processing to interpret each request in context, and either resolves the issue directly or hands it off to a human agent with the full conversation history attached.

Because Jira Service Management is part of Atlassian's Service Collection, a Rovo-powered virtual agent isn't limited to internal IT or HR use cases. The same bundle includes Customer Service Management for teams supporting external customers, letting you run a single AI service layer across IT help desks, HR service centers, and customer-facing support operations - all under one license.