The video explores the fundamental anatomy of AI agents, breaking down their core components into sensing, thinking, and acting stages. AI agents gather information from the real world through various inputs such as text, sensors like cameras and microphones, or APIs. This sensory perception is akin to human senses, allowing the agent to receive data that it will later process. These inputs form the foundation for the agent’s understanding of its environment and tasks.

Once the data is collected, the AI agent moves into the thinking phase, where it processes the information with the help of a knowledge base. This knowledge base contains facts, rules, and contextual information essential for decision-making. It may also include policy guidelines, goals, and priorities that the agent must consider. The thinking stage involves reasoning, planning, and task decomposition, often leveraging machine learning and large language models to interpret and analyze the data effectively.

The reasoning process involves applying logical structures such as “if-then-else” conditions and breaking down complex goals into manageable tasks. Machine learning techniques, including reinforcement learning, help the agent improve its understanding and performance over time by recognizing patterns and learning from feedback. Large language models play a significant role in handling text-based inputs and supporting chain-of-thought reasoning, enhancing the agent’s ability to make informed decisions.

In the action phase, the AI agent generates outputs based on its reasoning. These outputs can take various forms, including text, speech, alerts, or even control signals for actuators in physical systems like robots or self-driving cars. The agent interacts with external systems, such as booking platforms or databases, to execute tasks in the real world. This stage translates the agent’s decisions into tangible actions that fulfill the user’s objectives.

A crucial aspect of AI agents is the feedback loop, which allows continuous evaluation and improvement of their performance. Through reinforcement learning with human feedback, the system receives ratings or corrections that help it refine its actions. The agent can also self-assess by testing different scenarios to optimize outcomes. This ongoing feedback mechanism ensures that AI agents become more personalized, efficient, and effective over time, ultimately freeing users from complex tasks and enhancing productivity.