AI agent types

AI agent types encompass distinct categories of artificial intelligence systems classified by their capabilities, autonomy levels, and operational characteristics. Simple reflex agents respond to immediate perceptions using condition-action rules without memory or learning capabilities. Model-based reflex agents maintain internal state representations to handle partially observable environments. Goal-based agents pursue specific objectives through planning and decision-making processes. Utility-based agents optimize actions based on performance measures and preference functions. Learning agents continuously improve through experience and feedback mechanisms. Multi-agent systems involve collaborative or competitive interactions between multiple AI entities. Hierarchical agents operate within structured command relationships, while reactive agents respond to environmental stimuli without complex reasoning. Conversational agents specialize in natural language interactions, operator agents execute system-level tasks, and autonomous agents function independently with minimal human oversight across various domains.