These are the most basic agents that operate on predefined if-then rules. Think of a simple chatbot that gives fixed responses to specific keywords or a thermostat that adjusts temperature based on preset conditions. They can't learn or adapt but are reliable for straightforward tasks.
These agents respond directly to their current environment without maintaining memory of past experiences. Like a chess program that evaluates the current board position to make moves, but doesn't learn from previous games or develop long-term strategies.
These agents can store and reference past experiences to inform current decisions. They maintain a history of interactions and outcomes, similar to a customer service AI that remembers previous conversations with a specific user to provide more contextual responses.
These agents work toward specific objectives, planning steps to achieve desired outcomes. They can evaluate different paths and choose the most efficient route to their goal, like a delivery routing AI that optimizes multiple stops while considering traffic and time constraints.
These agents can improve their performance over time through experience and feedback. They use machine learning to adapt their behavior based on outcomes, similar to recommendation systems that get better at suggesting products as they gather more user interaction data.
These agents can operate independently within their domain, making decisions without constant human oversight. They can handle complex scenarios and adjust to changing conditions, like an AI trading system that monitors markets and executes trades based on multiple factors.
The most sophisticated level involves multiple AI agents working together, each with specialized roles but coordinating to achieve common goals. Think of a smart building system where different agents manage security, energy, maintenance, and occupancy while communicating with each other to optimize overall building operations.
Each level represents increasing complexity and capability, from simple programmed responses to sophisticated collaborative systems. The key distinction between levels is the agent’s ability to perceive, reason, learn, and interact with its environment and other agents.
