The video explains the five main types of AI agents—simple reflex, model-based, goal-based, utility-based, and learning agents—highlighting their decision-making capabilities and applications in real-world scenarios. It also notes the importance of multi-agent systems and human oversight to ensure effective and safe AI deployment.
The video introduces the concept of AI agents, emphasizing their growing prominence in 2025, and explains that AI agents are classified based on their decision-making capabilities and interaction with their environment. It begins with the simplest type, the simple reflex agent, which operates based on predefined rules and sensors, similar to a thermostat. These agents are effective in predictable environments but struggle with dynamic scenarios because they lack memory and cannot adapt to new situations.
Next, the video discusses model-based reflex agents, which are more advanced. These agents incorporate an internal model of the world that they update based on observations, allowing them to remember past states and understand how their actions influence the environment. For example, robotic vacuum cleaners use this approach to remember which areas are clean or where obstacles are, enabling more effective navigation and cleaning in changing environments.
The third type, goal-based agents, build upon model-based agents by adding decision-making driven by specific goals. These agents simulate future outcomes to determine the best course of action to achieve their objectives. An example provided is a self-driving car that plans routes based on its destination, predicting future states to select actions that best help it reach its goal. This approach allows for more flexible and adaptive behavior in complex environments.
The fourth type, utility-based agents, further refine decision-making by evaluating the desirability of different outcomes. Instead of simply achieving a goal, these agents consider factors like safety, efficiency, and energy consumption, assigning utility scores to potential outcomes. For instance, an autonomous drone might choose a route that balances speed, safety, and energy use, selecting the option with the highest utility score. This enables more nuanced and optimized decision-making.
Finally, the most advanced are learning agents, which improve over time through experience. These agents use feedback mechanisms, such as rewards, to update their knowledge and strategies, making them highly adaptable. An example is an AI playing chess, which learns from each game to refine its tactics. The video concludes by noting that multi-agent systems, where multiple AI agents collaborate, are common, and that while AI agents are becoming increasingly capable, human oversight remains important for ensuring effective and safe deployment.