There are no distinguishable hardware examples for each IA class. The same mobile robot architecture with proper sensors can be implemented to behave as any IA class. The way you can determine the class of an intelligent agent is from the way it processes the percept. Based on chapter 2 of Artificial Intelligent: A Modern Approach I will try to give a concise explanation for each class:
A simple reflex agent takes an action based on only the current environmental situation; it maps the current percept into proper action ignoring the history of percepts. The mapping process could be simply a table-based or by any rule-based matching algorithm. An example of this class is a robotic vacuum cleaner that deliberate in an infinite loop, each percept contains a state of a current location [clean] or [dirty] and, accordingly, it decides whether to [suck] or [continue-moving].
A model-based reflex agent needs memory for storing the percept history; it uses the percept history to help to reveal the current unobservable aspects of the environment. An example of this IA class is the self-steering mobile vision, where it's necessary to check the percept history to fully understand how the world is evolving.
A goal-based reflex agent has a goal and has a strategy to reach that goal. All actions are taken to reach this goal. More precisely, from a set of possible actions, it selects the one that improves the progress towards the goal (not necessarily the best one). An example of this IA class is any searching robot that has an initial location and wants to reach a destination.
An utility-based reflex agent is like the goal-based agent but with a measure of "how much happy" an action would make it rather than the goal-based binary feedback ['happy', 'unhappy']. This kind of agents provide the best solution. An example is the route recommendation system which solves the 'best' route to reach a destination.
A learning agent is an agent capable of learning from experience. It has the capability of automatic information acquisition and integration into the system. Any agent designed and expected to be successful in an uncertain environment is considered to be learning agent.