Agents in the context of artificial intelligence can be simple and reflexive, continuously monitoring levels of internet traffic to determine when an alert needs to be sent based on historic data, or they can be complex, with agents having a mechanism to perceive their environment and take some kind of action. These agents have two fundamental components: sensors to perceive their environment and actuators to interact with their environment. The ultimate purpose of an agentic AI is to act upon its environment, and these actuators can range from simple data inputs like a camera at an intersection to more complex inputs like natural language or other symbolic forms that then have to be processed. Agents can also have reasoning and a goal, such as the racers in Mario Kart who perceive their current place in the lineup and take actions to reach their assigned rank. The taxonomy of agents includes simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, and learning agents, each with its own characteristics and capabilities. Agents can be used in various applications such as virtual assistants like Siri and Alexa, robotics, cybersecurity, gaming, healthcare, smart home monitoring, environmental monitoring, and many other areas where AI is already being utilized. Despite the remarkable progress made by AI agents, challenges persist, including ethical decision-making, biases in algorithms, and the explainability of AI agent decisions, which are areas of active research.