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Deep Dive into AI Virtual Agents: Why are They in the Spotlight Today?

Artificial Intelligence (AI) is increasingly penetrating most areas of our lives. Many startups and companies claim to be actively developing and applying AI technology. Some of you encounter these apps and software directly, and some of you know about them from movies or YouTube videos.

When we hear about AI, we think of human-looking machines with superhuman intelligence (inspired by movies and fantasy stories) or something super-technological. For example, neural networks, robotics, computer vision and much more (what we hear from technology companies and AI experts).

Did you know that Virtual assistants can save a business up to 78% in operating costs? This is why we have to take a deeper look at AI virtual agents and the benefits they provide to  businesses across industries.

What is a “Virtual Agent”?

In IT, the concept of “agent” appeared almost simultaneously with the creation of expert systems in the 1970s, because these systems had a number of drawbacks. The concept has many definitions, but some are so general that they do not allow us to understand the specific properties of an agent. On the other hand, these definitions are so narrowly specialized that a virtual support agent falling under them corresponds to only one narrow task that they can solve, and are not applicable anywhere else.

However, all AI agents have a number of common and necessary properties. Is any decisive system capable of adapting to a changing environment? A decisive system is a system capable of independent reasoning, making decisions and taking actions to perform tasks, as well as changing its behavior when the environment changes.

Environment—a model of the environment for the agent, which is constructed based on information from the external environment and available knowledge. For example, for the system, you are an object of the environment and the system interacts with you based on the data it has about you. But if the information changes (environmental change), the system adapts its behavior.

The notion of agent can be used in different fields: in manufacturing a robot can be understood as an agent, in computer games – software modules, in programming – computing units that perform a certain set of operations, even a smart home vacuum cleaner is also an agent, and so on.

Of interest to us are virtual agents, which are artificial and have no physical form, but are in a software environment. In addition, looking at them as an artificial intelligence technology, the focus will be on an intelligent agent in AI.

With these features in mind, the term agent will refer to a computational system that functions in a complex dynamic environment, interacting with and influencing it, as well as possessing properties that endow the system with intelligence similar to a human’s.

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How a “Virtual Agent” Works

To understand how an agent works or functions, you must first consider how it is organized.

Its structure includes the following sets:

  • virtual agents’ goals necessary to solve the tasks;
  • roles that characterize the agent’s functions;
  • skills and abilities of a virtual agent required to perform its roles and tasks;
  • actions;
  • behavior strategies;
  • rules and constraints of the agent’s functioning;
  • natural languages;
  • AI agent’s states.

We can say that an agent consists of exterior architecture (computing devices and actuators) and internal architecture (programs). The external architecture includes the sensors by which the agent receives information (receptors) and the sensors by which the agent acts on the outside world and on other AI agents (effectors). For the most part, programs consist of a number of subprograms. One variant of the functional structure of a program or agent may be a set of subprograms:

  • Mental;
  • Modeling;
  • Analysis and planning;
  • Adaptive;
  • Coordination and collaboration;
  • Communications.

In general, the agent architecture is built from the purpose and the tasks that the agent has to perform. Thus, the agent architecture is a flexible design that can change (the composition and functional purpose of the subprograms can change).

However, there are basically three types of AI agent architecture:

  • Deliberative architecture is based on the principles and methods of AI.
  • Reactive architecture is based on behavior and reaction to events in the outside world.
  • The hybrid architecture combines the two previous types.

The knowledge-based architecture is characterized by the fact that the agent is able to make decisions about actions based on its reasoning and the available knowledge base. The second architecture means that the agent is given a set of behavioral patterns that are applied by the agent when certain triggers arise. Since these two architectures have a number of disadvantages, the hybrid type is the most popular.

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What are the Examples?

Here we present several examples of successful use of virtual assistants, by the way, you are already familiar with some of them and use them in everyday life.

  • Apple’s Siri
  • Google Now
  • Microsoft’s Cortana
  • Cleverbot
  • Eviebot
  • Boibot
  • PewDieBot
  • Chimbot

Specialties of an Intelligent Virtual Agent

As any invention has its own features in use, and because the virtual assistant is a relative new thing for mankind, you need to know how to use them. Let’s focus on their core features.

  • Autonomy. The virtual agent acts independently without external interference, controlling its actions and state. However, the user can set the degree of autonomy manually.
  • Communication. Virtual agents interact not only with the external environment, but also with other agents in AI using special languages. AI Agents can exchange information with the environment and other assistants. Based on the information received, the system builds its internal and external model of the world.
  • Adaptability. The ability of a virtual agent to sense the environment and respond correctly to its changes to achieve their goals.
  • Self-learning. The virtual agent is able to obtain information not only about its environment but also about itself, and its history of interaction with the environment. Thus, the agent receives a more complete experience and has the ability to analyze it, which is reflected in the more thoughtful behavior of the virtual agent.

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Using Intelligent Virtual Agents

Due to the technological capabilities of virtual intelligent agents, their application is large. Thus, they can be applied in the following fields:

  • Call centers;
  • E-commerce (e.g., marketplaces, online stores);
  • Social networks and messengers;
  • Advertising and promotion;
  • HR;
  • Reception at restaurants, hotels, and companies;
  • Voice virtual assistants;
  • Mobile applications;
  • Medicine;
  • Banking and finance;
  • Smart home management technologies.

Virtual support agents are most in demand for the creation of consumer services and information technology support services. Many retailers use them to support and guide customers through the product selection and purchasing process. With elements of artificial intelligence, AI virtual assistants help learn and analyze customer behavior, particular preferences and dislikes, to provide personalized suggestions for shoppers and enhance their experience.

Thanks to AI virtual assistants, you can order food, follow sports events (the virtual agent sends current scores, videos, ticket offers, etc.), women can pick up cosmetics and much more. A virtual agent can also be useful for organizing work processes in a company, for example, it can check who is late for work, collect reports from each employee, and monitor meetings and events.

Another common application of virtual agents is technical support. This technology is mainly used in companies to allow staff to get the necessary help and information 24/7. For example, an employee has a problem with the sound on his computer and decides to use a virtual support agent. First, the agent needs to ask the employee correct questions, such as “Are you using headphones or computer speakers?” and then give an answer based on the situation. If it turns out that the difficulty is headphones, then the virtual agent sends a request for new headphones automatically.

All the examples above were about chatbots. Simply put, they are agents in AI, and virtual assistants that communicate with you (asking and answering questions). Also, they search for information on demand, promote products or services, assist you in the process of purchasing goods, and so on in a social media chat format.

Many people are confused and think that chatbots are the same as intelligent virtual support agents, but this is far from it. Chatbots are a rules-driven service and sometimes are powered with AI that you interact with through a chat interface. Intelligent AI virtual assistants  are a more advanced technology with many features and cognitive function similar to humans that are not inherent to chatbots (see above about what a virtual intelligent agent is). Moreover, at least currently, there are no chatbots that are free to communicate with humans at the same level.

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AI Agent: How New Technologies Are Shaping the Future

As the need for intelligent and efficient decision-making continues to grow, so does the importance of artificial intelligence (AI) and machine learning (ML). AI and machine learning are essential components of open AI ecosystems, which are becoming increasingly popular as organizations seek to create AI-enabled solutions quickly and efficiently.

Industry leaders have started recognizing the growing role of machine learning in open AI ecosystems and are taking steps to ensure that their organizations can benefit from this technology. For example, Microsoft recently announced the launch of its Open AI virtual platform, which aims to make it easier for developers to deploy AI assistant models, including those based on machine learning, in their applications. The platform provides access to a comprehensive set of tools and services, including open source and machine learning platforms, model training and optimization, and real-time artificial intelligence services.

Google also recently announced Cloud AutoML Vision, a cloud-based machine learning platform that allows organizations to easily create their own AI models for tasks such as image recognition and object detection. Cloud AutoML Vision uses Google’s powerful machine learning technology to create high-quality models tailored to each organization’s specific needs.

As organizations look for ways to leverage machine learning in open AI ecosystems, it’s important to remember that these technologies are evolving rapidly, and that organizations must keep pace. Industry leaders continue to invest in research and development to ensure that their AI solutions remain competitive and that they can leverage the latest advances in machine learning.

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Wrapping Up

Virtual intelligent agent technology is indeed a trend of the years to come and is capable of radically changing the level of interaction between companies and consumers. As well as significantly increasing the efficiency and productivity of business in many fields. The widespread application of virtual intelligent agents in various industries will accelerate the processes of collecting, processing and exchanging information. Also, it will increase the speed and quality of decision-making.

The most promising application of this technology is seen in creating smart interfaces, for the formation of accurate personal recommendations. It is used for quick information search and navigation, for providing continuous full support for people in education, medicine and other areas of importance, as well as in creating smart corporate assistants.

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