What are the types of AI agents? (And which split matters)

The five you will find everywhere

Nearly every list starts with the same five, grouped by how much the agent works out before it acts. A simple reflex agent follows fixed rules and remembers nothing, like a thermostat, or a filter that files every email with "invoice" in the subject. A model-based reflex agent keeps track of what has happened so far. A goal-based agent plans a route to an outcome. A utility-based agent scores several options and picks the best one. A learning agent adjusts from feedback. Russell and Norvig published that grouping in an AI textbook, and DataCamp stacks multi-agent and hierarchical setups on top, which is why some lists say seven and others ten.

The split that changes your setup

Two questions matter more than the label, and neither one is on that list. Does the agent need to remember earlier steps, and does it act before a person approves? An agent with no memory cannot pick up a job that failed halfway, so anything running across several steps needs it. Anything that emails a customer or moves money needs an approval stop in front of it. Databricks prints both lists on one page for exactly this reason: the textbook five describe mechanics, and its own four (extraction, knowledge assistant, custom, supervisor) describe jobs someone wants done.

More advanced is not better

Gartner counts about 130 vendors with real agent features among the thousands claiming them, and expects more than 40 percent of agent projects to be cancelled by the end of 2027. A learning agent on a task with clear rules buys upkeep and surprises for nothing. Start with fixed rules and an approval stop, then add memory once a job spans steps that can fail on their own.