In the field of Artificial Intelligence, an intelligent agent is an entity that perceives its environment through sensors and acts upon that environment using actuators in order to achieve specific goals. Intelligent agents are fundamental components in AI systems because they enable machines to make decisions, solve problems, and interact with dynamic environments in an autonomous and rational manner. The concept of intelligent agents provides a framework for designing systems that can operate independently and respond intelligently to changes in their surroundings. An intelligent agent can be understood as a system that continuously observes its environment, processes the information it receives, and takes appropriate actions that maximize the likelihood of achieving its objectives. The environment in which the agent operates may be physical, such as robots functioning in the real world, or virtual, such as software programs interacting with digital environments. For example, a self-driving car is an intelligent agent that uses sensors like cameras, radar, and lidar to perceive road conditions and uses actuators like steering and braking systems to move safely. Similarly, recommendation systems on online platforms act as intelligent agents by analyzing user data and suggesting products or content that match user preferences. Intelligent agents operate according to an agent function, which maps the percept sequence received from the environment to an action. This function is implemented through an agent program running on an appropriate architecture. The percept sequence represents the history of everything the agent has perceived so far, which helps it make better decisions based on past experiences. In Artificial Intelligence and Machine Learning, intelligent agents are often designed to learn from their environment so that their performance improves over time. Agents can be classified into different types depending on how they process information and make decisions. Simple reflex agents act only on the basis of the current percept and ignore the rest of the percept history. Model-based agents maintain an internal model of the world to keep track of aspects of the environment that are not immediately observable. Goal-based agents make decisions based on specific goals they aim to achieve, while utility-based agents evaluate different possible outcomes and choose the action that maximizes their utility or satisfaction. Learning agents improve their performance through experience by updating their knowledge and strategies using machine learning