Simple reflex agents respond solely to the current situation, following a set of predefined rules. They make decisions based on the immediate input from their environment without considering past experiences or future consequences. When a certain condition is met, they trigger a specific action. Example: A basic light sensor system turns on the lights when it detects low ambient light. It operates based on predefined thresholds without considering past lighting conditions or anticipating future needs.

Model-based reflex agents build an internal model of their environment, using it to understand how their actions impact their surroundings. By incorporating both current data and past experiences, these agents can anticipate future changes and adjust their behavior accordingly. Example: Autonomous drones use model-based reflex agents to navigate through environments. They continuously gather data from their sensors, track obstacles, and adjust their flight paths in real-time to avoid collisions and reach their destination.

Goal-based agents are designed to achieve specific objectives. While the goals are defined, the path to achieving them is not strictly set by rules. These agents evaluate various possible actions and choose the one that best leads them to their desired outcome. Example: In robotics, goal-based agents can be used to navigate a maze. The robot evaluates different paths and selects the one that most efficiently leads it to the exit.

Utility-based agents assess different scenarios by weighing the pros and cons of each option. They choose actions that maximize overall benefit or satisfaction according to predefined criteria, making them suitable for complex decision-making tasks. Example: A personalized shopping assistant compares various product options based on factors like price, quality, and user preferences to recommend the best purchase.

Learning agents continually improve their performance by learning from experience. They have mechanisms to evaluate their actions and outcomes, allowing them to adapt and refine their strategies over time. Example: Adaptive spam filters learn from user behavior to better identify and filter out unwanted emails, improving accuracy with each interaction.

These types of AI agents can work independently or be combined into hierarchical, multi-agent systems. In such systems, higher-level agents manage complex tasks by breaking them down into simpler tasks assigned to lower-level agents. The lower-level agents execute their tasks, and their progress is monitored and integrated by the higher-level agents to achieve the overall goal.