Although both AI agents and generative AI utilize artificial intelligence technology, there are clear differences in their purposes and functions. Below, we will explain the characteristics and roles of each.

What are AI agents?

An AI agent is an intelligent system designed to accomplish a specific task. It makes decisions and takes actions autonomously while interacting with the external environment. It has the ability to observe the environment and react appropriately accordingly, and is characterized by a mechanism for planning and executing actions to achieve a goal. A typical example is a self-driving car, which understands the surrounding situation and drives autonomously to reach its destination safely. Other examples of AI agents include smart home assistants and chatbots used for customer service in companies.

In this way, AI agents aim to achieve given goals and tasks, and their adaptability to the environment and autonomy are important. Their essence is the ability to judge the situation through information gathering and analysis, and adjust their actions.

What is generative AI?

Generative AI is an artificial intelligence that has the ability to learn huge amounts of data and generate new content based on it. It is characterized by its ability to generate content in a variety of formats, including text, images, audio, and video. It is particularly being applied in the fields of natural language processing and image generation.

This technology has evolved significantly with the development of neural networks and deep learning. At the core of generative AI is a model trained on a large data set, which provides creative output based on input information, rather than simply imitating patterns. For example, a text generation model understands words and grammatical rules to create new text that is indistinguishable from human-written text.

In addition, generative AI is not limited to creative fields. It is used in a wide range of fields, including customer service, education, design, and medical simulations. At the same time, there is also a growing concern that generated content may be misleading and that ethical issues may arise.

What is the difference between AI agents and generative AI?

AI agents and generative AI have different roles and characteristics within artificial intelligence technology. When distinguishing between them, the differences become clear when you focus on their purpose, functions, and working methods.

An AI agent is a system that plans and executes actions to achieve a specific goal. It works with the external environment and makes decisions while analyzing data in real time. Its work focuses on adaptability and autonomy to the environment, and is characterized by its ability to proceed with actions on its own without waiting for user instructions. For example, self-driving cars and smart home devices choose the optimal action according to the situation while minimizing user intervention.

On the other hand, generative AI specializes in generating content. Its main role is to generate new information and ideas, producing deliverables in a wide variety of formats, including text, images, and audio. It is characterized by imitating the creative process carried out by humans based on given input data and conditions. Examples of this include text creation tools and image generation models. Generative AI is evaluated based on the quality and creativity of the output, rather than on goal achievement.

The two also have different working styles. AI agents place emphasis on the process of executing tasks, and excel at interacting with the environment and judging situations. On the other hand, generative AI specializes in the process of creating something new, and as a result, the generated content itself is emphasized. The former can be described as "task-oriented," while the latter as "creativity-oriented."

However, these two are not completely independent, and can complement each other. For example, an AI agent can use generative AI to generate flexible responses and customized content according to the situation. In this way, by combining the features of both depending on the purpose, it is possible to realize even more advanced systems.

If we think of the AI ​​agent as a "manager" and the generative AI as a "creator," their respective roles and relationships become clearer. The manager AI agent grasps the overall goal of the project and considers strategies for efficiently completing tasks. Its role is to monitor the situation and make appropriate decisions. Meanwhile, the creator generative AI is responsible for producing concrete deliverables following instructions from the manager.

If we use the analogy of movie production, the AI ​​agent would be like a director or producer, directing the overall flow and allocating the necessary resources. Meanwhile, the generative AI would be like a screenwriter or designer, handling the creative aspects such as the actual visuals, scenarios, and character design. The AI ​​agent would decide "what kind of creativity is needed in which scene," and the generative AI would provide the deliverables in response to those requests.

What is important in this relationship is for the AI ​​agent to utilize the generative AI at the right time and maximize its capabilities. Conversely, the generative AI must also flexibly respond to the instructions and requests from the AI ​​agent and provide output that meets the purpose. In other words, the two have complementary roles and work together to improve the overall results.

Expected success in combination with robots and digital twins

By combining AI agents and generative AI with robots and digital twins, it is expected that they will be used in an even wider range of fields. This fusion will realize efficient collaboration between the physical and virtual worlds, and has the potential to create new value.

When combined with a robot, the AI ​​agent manages the robot's movements and tasks, while the generative AI supports flexible responses and actions according to the situation. For example, in the service industry, a customer service robot can provide a more human-like service by having an AI agent analyze the needs of the customer from their facial expressions and tone of voice, and the generative AI generate natural, friendly conversation. Another possible application would be in controlling robot arms in a factory, where an AI agent designs an efficient production plan and the generative AI dynamically optimizes the parts blueprints and manufacturing processes to increase productivity.

When combined with digital twins, the possibilities become even more diverse. Digital twins are faithful reproductions of physical objects or systems in virtual space, a technology used for simulation and monitoring. AI agents monitor the operation of digital twins, detect anomalies, and develop optimal operational plans. Meanwhile, generative AI can prototype new design proposals and operational scenarios in virtual space.

For example, in urban planning, digital twins are used to recreate the structure and movement of an entire city. Based on that data, AI agents analyze traffic flow and infrastructure usage, and generative AI proposes new layout plans to alleviate traffic congestion. Such systems can help support efficient city operations while reducing environmental impact.

It can also be used to manage complex assets such as aircraft or power plants. The digital twin will allow the AI ​​agent to understand the equipment's operating status in real time and identify abnormalities or the need for maintenance. At the same time, the generative AI will suggest new repair procedures and improvements to minimize downtime.

The appeal of this combination is that it combines the real information of physical robots and digital twins with the knowledge and creativity of AI agents and generative AI, dramatically increasing the speed at which problems can be discovered and solved, and dramatically increasing efficiency and flexibility.

As robotics and digital twin technologies evolve, new applications are expected in all areas of society and industry through collaboration with AI agents and generative AI. The transformation brought about by this combination goes beyond mere efficiency improvements, and has the potential to realize more sustainable, human-centric systems.