Agentic virtual agent

Agentic AI in customer experience refers to AI systems that can understand customer intent, make decisions and take actions autonomously to resolve issues, rather than only responding with predefined answers. These systems operate within set guardrails, allowing them to handle tasks end to end while knowing when to involve a human agent.

They help organizations reduce operational costs, improve first-contact resolution, scale customer support efficiently and deliver faster, more consistent customer experiences across channels.

Agentic virtual agents can complete tasks such as answering complex inquiries, retrieving and updating customer information, processing transactions, troubleshooting issues, guiding customers through multi-step workflows and resolving requests without human intervention. When needed, they can also hand off to a human agent with full context.

Virtual agents can reduce hallucinations and incorrect actions by operating within defined guardrails, using trusted enterprise data sources, enforcing validation rules and limiting actions to approved workflows. Continuous monitoring, testing and human oversight further help ensure accurate and reliable behavior.

There are a number of different types of chatbots that businesses can use. The simplest are menu- or button-based chatbots, which offer users access to a fully scripted menu where their specific questions can be answered. A menu-based bot is basically a user interface for a decision tree; the chatbot can’t deviate from the script.

Rules-based chatbots are slightly more complex, using “if/then” logic to simulate actual conversation. If your service team encounters a number of specific questions again and again, a rules-based bot is useful for answering them.

AI-powered chatbots are another step up the ladder. They use tools like large language models (LLMs) and machine learning to grow in real time, even going so far in the case of the best AI chatbots as to ask questions to get more information from end users.

Chatbots work across a number of channels, including websites and mobile apps.

A virtual agent is the most sophisticated version of a chatbot or voicebot. It meets the same need — a 24/7 answer to customer questions — but it gets much closer to mimicking the experience of talking to a person. Virtual agents use AI systems and machine learning to not only surface the right answers to customer queries but to problem solve and learn as they work, and to narrow the gap between your digital offerings and your human agents.

They also offer seamless transition to human agents in cases where it’s needed with a summary of the interaction and wrap-up codes. This keeps the context intact and allows the agent to step in smoothly.

Virtual agents can work across channels, including social media, phone calls, mobile apps and more.

Chatbots, voicebots and virtual agents are all designed to answer customer questions quickly and easily, 24/7, with minimal human intervention. But most chatbots and voicebots need to be programmed with those answers and those answers need to be connected to the likely questions that would trigger those responses. Some have artificial intelligence, but in many cases that amounts to surfacing answers more easily or understanding questions better.

A virtual agent goes a step farther. The AI allows the virtual agent to respond to out-of-scope queries and makes it effortless to deploy — adaptive to natural human patterns of speech and ever-evolving so you can uncover CX gaps and potential improvement areas to help your call center evolve constantly. It also makes the transition process from virtual agent to human seamless by providing appropriate conversational context.

Hopefully, the difference between a customer asking a question of your virtual agent and asking a human should be negligible. For all simple questions — and a fair number of more difficult ones — the virtual agent should be able to handle answers with speed, intelligence and (a simulation of) empathy. And when the questions get too complex, the handoff from virtual agent to real agent should be seamless.

The differences are the things that only a human can provide. Virtual agents learn and can make connections that seem abstract, but real abstract thinking needs a human mind. And of course, while AI can simulate empathy and emotion, it cannot truly feel either. When a judgment call is required, the virtual agent will need to escalate to contact center live chat with a person.

An AI chatbot is a software application that uses artificial intelligence to simulate human conversation. It can answer customer queries, provide information, carry out tasks like booking appointments or processing orders, and learn from interactions to improve its responses over time.

AI chatbots can enhance user experiences by providing instant responses, being available 24/7, offering personalized interactions based on user history and freeing up human agents to handle complex issues. By learning from interactions, they can also improve their performance over time, leading to more accurate and helpful responses.

A good AI chatbot should accurately understand user queries, provide relevant and correct responses, handle a wide range of topics, and learn from interactions to improve over time. It should also provide a user-friendly experience, handle errors gracefully and, where necessary, seamlessly transfer the conversation to a human agent.

An AI chatbot handles more than just simple automation. Through its deep learning capabilities, it can go beyond a simple script. It uses natural language understanding and natural language processing to identify new customer queries, understand them and respond.

If a customer query is too complex or unique for the AI bot to handle on its own, it can seamlessly transition the query to a human agent, providing the context and customer information the agent needs to be able to quickly and easily help. This is one reason AI chatbots can also be referred to as virtual assistants or virtual agents.

One of the most important benefits of an AI chatbot is that they can get better at managing interactions over time. Part of this includes leveraging improvements to the knowledge base, acting as a guiding force to help ensure the chatbot stays within a company’s guidelines. Continuing to provide training data, such as historical conversations, can also aid the bot in better anticipating what questions to expect. This will help the chatbot perform better around utterances, which can vary tremendously, and the true intent from interactions.

AI bots can identify and understand new inputs using natural language understanding and natural language processing capabilities. Machine learning allows the bot to analyze data and find patterns, while admins can go in and perform manual improvements such as perfecting missing utterances that are highlighted.

Voicebots, also known as voice assistants or voice-enabled AI, work by processing spoken language input from users. They use technologies like automatic speech recognition to transcribe speech into text and natural language processing to understand the intent of the text, and then use an AI model to generate a response. The response is then converted back into speech using text-to-speech technology.

The safety of voicebots depends on their design and implementation. Reputable voicebot developers take measures to protect user privacy and data security. This can include encrypting data, anonymizing voice recordings and allowing users to manage their data. However, like all technologies, voice assistants aren’t immune to risks, so it’s important to use them wisely and understand the provider’s data handling policies.

Industries are using voicebots in various ways. In retail, they’re used for customer service and shopping assistance. In healthcare, they’re used for appointment scheduling, medication reminders and providing health information. In hospitality, they’re used for booking services and providing related information. They’re also used in banking for account management and transactional tasks, and in many other sectors for diverse applications.

While both voicebots and interactive voice response (IVR) systems interact with users through voice, they do so in different ways. IVR systems work through pre-recorded messages and touch-tone or simple voice inputs, offering a limited set of responses based on a pre-programmed menu. Voice assistants, on the other hand, use advanced AI to understand natural language, enabling more complex interactions and a wider range of responses.

Conversational AI voicebots offer several benefits. They can handle customer issues 24/7, providing instant responses and freeing up human agents for more complex tasks. They can improve customer experiences by offering convenient, hands-free and personalized interactions. They can also handle a wide range of tasks, from answering questions to performing actions like booking appointments or controlling smart devices.

Yes. AI can help improve First Contact Resolution (FCR) by helping customers get to the right answer, resource or resolution faster, without unnecessary transfers, repeat questions or follow-up interactions.

With Genesys Cloud AI, capabilities such as intelligent routing, virtual agents, Agentic Virtual Agents (AVA) and Genesys Cloud Copilot can work together to understand customer intent, preserve context and guide the next best action across the customer journey.

Agentic Virtual Agents can help improve FCR by moving beyond static chatbot flows. AVA is designed to understand customer goals, manage more complex or multi-step requests, and take action using approved enterprise knowledge, workflows and systems. This helps resolve more interactions in the self-service experience while reducing repetitive handoffs to live agents.