In the context of Generative AI, the term ‘agent’ typically refers to an autonomous entity or algorithm that is capable of performing actions to achieve specific goals within a given environment. The agent interacts with the environment, receives feedback in the form of rewards or penalties, and adapts its behavior to maximize some notion of cumulative reward. This is particularly relevant in the context of Reinforcement Learning, a subfield of AI, where an agent learns by interacting with its environment.

In Generative AI, an agent can be thought of as a generative model that is tasked with creating new data or content. The generative agent has the ability to produce a sequence of actions, which in this context, corresponds to generating new data points such as images, text, or sounds. The generation process is often guided by a set of rules, objectives, or constraints that the agent must adhere to.

For instance, in Generative Adversarial Networks (GANs), the Generator can be considered as an AI Agent. It generates new data and seeks to fool another agent, the Discriminator, into believing that the data is real. The Discriminator, which can also be considered an agent, aims to distinguish between real and generated data. In this scenario, both the Generator and Discriminator are agents that are engaged in a competitive game.