AI agents don’t all think and act in the same way. They range from simple rule-followers to systems that learn and adapt. Each type marks a step forward in how machines perceive, decide, and act.
- Simple Reflex Agents: These follow condition–action rules. For example, if the temperature is high, turn on the fan. No memory, no thinking, just instant reaction. They are fast and simple.
- Model-based Reflex Agents: These maintain an internal understanding of their environment. They are not just reacting to immediate inputs, they have a model that helps them make sense of what is happening beyond what they can see right now.
- Goal-based Agents: Here, the focus shifts to goals. Decisions are made based on whether an action brings the agent closer to its objective.
- Utility-based Agents: These go a step further by weighing different outcomes. They choose the action that offers the best overall result, balancing trade-offs along the way.
- Learning Agents: These are the most advanced. They improve continuously, using feedback to adapt and perform better over time.
