According to IBM, there are currently five (5) main types of AI agents:
- Simple reflex agents
- Model-based reflex agents
- Goal-based agents
- Utility-based agents
- Learning agents
1.Simple reflex agents
These agents are basic but reliable, like a thermostat that turns heat on when it’s cold. They follow simple, “If this, then that” rules. These agents don’t store past information, so they may struggle in dynamic, complex scenarios.
2.Model-based reflex agents
These agents remember what just happened and use it to make better decisions. Think of a chatbot that recalls earlier messages in a conversation. This means a better customer experience, because the AI doesn’t treat every interaction as brand new.
3.Goal-based agents
These agents have a target and figure out the best path to reach it, like a robot that finds the fastest way to get to a destination. Instead of reacting to immediate obstacles only, it plans a path that minimizes detours and avoids known obstacles.
4.Utility-based agents
When there’s no one “right” answer, an utility-based agent will weigh multiple factors to find the best option. It considers a range of possible outcomes and assigns a utility value to each, to decide the best course of action.
An example is an AI pricing tool that uses consumer demand, competitor prices, and inventory levels to price items dynamically. This agent enables you to maximize your returns by weighing multiple business priorities at once.
5.Learning agents
These agents continuously improve from experience, like a recommendation engine that learns your customer preferences over time.
The longer you use them, the better they perform, giving you an edge that compounds month after month.
Learning agents are highly flexible and capable of handling complex, ever-changing environments. They are useful in applications like autonomous driving and virtual agents assisting human reps in customer support.
