LLMs have capabilities beyond simply predicting the next word. Their usecases extend past Question Answering or Chatting. LLMs can be tasked with complex activities like booking a flight ticket. They can make a plan by breaking the task into steps or sub-tasks, execute each step, monitor the outcomes, reason through successes or failures, and adapt the plan accordingly. They can also adjust their actions based on feedback. Such systems are known as Autonomous Agents.

These intelligent systems can think and act independently and are designed to execute specific tasks without constant human supervision. They use reasoning, which we can reinforce with prompts and instructions. Virtual assistants like Siri and Alexa are also types of agents that we control through voice commands.

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Feedback is crucial for enhancing an AI agent's performance over time and that can come from either the critic or the environment. The critic–which can be the user–evaluates the agent's performance, while the environment provides feedback through the results of the agent's actions. This feedback loop enables the agent to adapt, learn from its experiences, and improve its decision-making. As the agent encounters more tasks, it refines its ability to achieve better outcomes and can effectively adapt to rapidly changing environments.

Features of AI Agents:

  • Agent Functions - The agent function enables the AI to decide on actions based on the information it has collected. This is where the agent's "reasoning" lies, involving reasoning and choosing actions to meet its objectives. For example, if we ask the agent to book the cheapest flight ticket from Florida to Barcelona, the agent function will decide that it needs to use the flight ticket-booking tool and the sort tool.
  • Knowledge base - The knowledge base is where an AI agent keeps its initial information about the environment, which is usually pre-defined or acquired during training. It acts as the foundation for the agent's decision-making process.
  • Percepts - Percepts are the sensory inputs that an AI agent receives from its environment, offering details about the current state of the observable surroundings in which the agent operates. For instance, for an AI agent handling restaurant table reservations, percepts might include messages, user location, user preference history, and the time and date.
  • Actuator - Actuators are devices or tools that enable AI agents to interact physically with their environment. These actions can vary from steering a self-driving car to typing text on a screen. In short, actuators act as the muscles of the AI agent, carrying out the decisions made by the agent function.