What is a virtual support agent? (And why B2B teams need one)

If your customer support team struggles, you’re not alone. Research shows 77% of customer support reps report increased workloads and greater complexity in customer issues since the previous years. 

Virtual support agents reduce that pressure. They handle routine tasks more efficiently and give customers faster responses and more consistent interactions. 

Let’s look at what a virtual agent is, how it works, and how it helps you offer better B2B customer support.

What are virtual agents?

Virtual agents — also known as AI agents or intelligent virtual agents (IVA) — are AI-powered systems that combine natural language processing (NLP), intelligent search, and automation to handle customer or employee requests. Here’s how they differ from other tools. 

Virtual agent vs. chatbot vs. virtual assistant

Virtual agents are often confused with chatbots and virtual assistants, but each serves a different purpose. Chatbots rely on simple rules and scripted responses, so they can only answer questions they’re programmed to handle. 

AI virtual agents understand complex questions and can take multi-step actions to resolve customer issues. They can also automate tasks across connected systems. This makes them far more effective in technical B2B support environments and highlights the differences between chatbots and conversational AI.

Virtual assistants are typically humans who provide online administrative support. The terminology confuses some because tools like Siri and Alexa are thought of as virtual assistant technology. You also see the term “intelligent virtual assistants” in some platforms, even though these systems function more like advanced virtual agents.

How does a virtual agent work?

These are the core technical components that power a virtual agent.

Natural language processing

NLP is the foundation of conversational AI. NLP-powered virtual agents interpret free-form text and understand what customers want, even when people use different terminology. It breaks a customer’s question into parts, called tokens, and uses large language models to work out the intent. 

For example, if a customer asks, “How do I export all invoices from last quarter?”, the virtual agent uses NLP to break the question into intent (export) and data (invoices, last quarter). If the customer uses different phrases, like “I can’t see where to export last quarter’s invoices,” the AI agent still recognizes it’s the same query.

Intelligent search and knowledge retrieval

Once the virtual agent understands the request, it uses those key terms to search your company’s knowledge base, documentation, and internal systems. It focuses on definition rather than exact phrase, which helps it surface accurate answers even when the terminology differs. 

The interactive virtual agent then generates an answer in plain language to guide the user through the workflow. So it might respond with: “Go to Billing > Reports, select ‘Invoices’, and choose the date range. Then click ‘Export CSV.’”

Automation and system integrations

When connected to internal systems, a virtual agent does more than just answer questions. It automates tasks that reduce manual work. For example, if it has access to billing information, it can export last quarter’s invoices and provide the file directly to a customer.

You can also integrate a virtual AI agent with your help desk ticketing system so it automatically creates tickets and routes them to the appropriate team member. You can also give it access to your CRM so it updates the customer’s records after every interaction.

Types of virtual agents

These are the main types of virtual agents that a B2B customer support team can use:

• End-to-end solutions. You buy a fully managed solution, and the provider handles maintenance, upgrades, and integrations with your current systems. This option is reliable, but it typically costs more than other types of AI agents and offers less flexibility for custom development. 
• Developer/API platforms. These are better for technology companies that want full control over data and design. You get access to APIs and build your own virtual agent based on your product and customer experience, but this requires technical expertise and continued maintenance.
• No-code/low-code SaaS. The SaaS option is fast and easy to configure and requires no coding skills or engineering resources. It’s a cost-effective plug-and-play solution, and it usually includes customization options, although fewer than if you build it yourself.
• Embedded/integrated agents. Modern B2B customer support platforms often provide virtual agents as a built-in feature. This is the easiest option for B2B support teams because the AI agent naturally integrates with your ticketing and other apps.

Key benefits of virtual support agents for B2B teams

These are the main benefits to expect from virtual AI agents in a B2B support team:

• Instant, 24/7 responses. AI-powered virtual agents handle customer questions at any hour, which reduces customer wait times without the need to hire more team members.
• Reduced ticket volume. When AI agents answer simple, repetitive questions, they deflect large volumes of tickets. This frees up your team to work on more difficult tasks that require human judgment, like if someone needs to grant access to a secure production database.
• Consistent answer quality. Some team members are more knowledgeable and can find information for customers faster. AI isn’t dependent on individual expertise, so customers consistently get reliable answers.
• Faster support resolution. Your team can also use AI agents to find relevant information or context more quickly. This shortens resolution times and improves efficiency.
• Scalable customer support. As your customer base grows, virtual agents absorb more volume without proportional recruitment. 
• Continuous improvement. AI agents use machine learning, so they learn from interactions and improve accuracy through feedback loops and better retrieval, which leads to stronger performance over time. 

Virtual agent use cases in B2B support

Here are some examples of how B2B SaaS teams deploy virtual agents:

• Answer product and API questions. A virtual agent can access your knowledge base and other documentation to give customers precise information for technical workflows.
• Provide integration guidance. Conversational AI can help new customers get set up with guided, conversational walkthroughs of a product.
• Triage support tickets. AI-powered virtual agents can analyze the intent of customer messages, prioritize them based on urgency, and route support tickets to the right team member for fast resolution.
• Give status updates. Customers can easily check whether the system works, get usage updates, and track the status of open tasks without the need to open tickets. When AI agents are connected to back-end systems, they can provide real-time updates.
• Support self-service for internal teams. AI agents also support internal workflows. Team members can reset passwords, request access to new apps, and check HR policies.

Turn your virtual agent into a competitive advantage with Pylon 

A virtual agent is an essential tool for a modern B2B support team. It allows you to offer better customer experiences, with faster resolution and lower wait times. Virtual agents also deflect repetitive tickets and free up your team to work on more important issues. The simplest way to incorporate a virtual agent into your support workflow is to use a B2B support platform that comes with sophisticated AI agents already built in.

Pylon is the modern B2B support platform that offers true omnichannel support across Slack, Teams, email, chat, ticket forms, and more. Our AI Agents and Assistants automate busywork and reduce response times. Plus, with Account Intelligence that unifies scattered customer signals to calculate health scores and identify churn risk, we're built for customer success at scale.

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FAQ

What is the difference between a virtual agent and a chatbot? 

A chatbot follows scripted, rule-based logic and can only respond to pre-programmed inputs. A virtual agent uses AI and NLP to understand freeform requests, take multi-step actions, and integrate with enterprise systems to resolve issues end-to-end.

What are the main use cases for virtual agents in B2B support? 

B2B virtual agents reduce the workload of support teams and improve response consistency. They commonly handle:

• Ticket triage
• Product FAQs
• API documentation queries
• Onboarding guidance
• Account status lookups

How do virtual agents use AI? 

Virtual agents rely on NLP to interpret user intent, machine learning to improve over time, and automation to execute tasks within connected systems — without the need for human involvement in each interaction. 

Can a virtual agent replace human support teams? 

Virtual agents handle repetitive, high-volume requests at scale, but they work best alongside support teams. They take on tier 1 deflection and free people to focus on complex, high-value interactions.