7 types of AI agents explained with examples

27 August 2026 · 5 min read · JetLevel

Not all AI agents work the same way: depending on how they perceive the environment and make decisions, they fit into different types. Knowing them helps choose the right approach for each business problem. Here are the seven main ones, with practical examples.

Reactive agents: simple reflex and model-based

Simple reflex agents react to immediate stimuli, with no memory: if A happens, they do B. Practical example: automatic reply to WhatsApp messages out of hours — it doesn't understand context, but solves the initial silence.

Model-based reflex agents maintain a world model — history, business rules — and perceive context. Practical example: customer support that checks the order and knows the customer already contacted yesterday, without asking them to repeat data.

Goal-based and utility-based agents

Goal-based agents plan the steps to reach a target. Practical example: sales agent that qualifies a lead (budget, urgency) and schedules a meeting in the sales calendar.

Utility-based agents look for the best possible solution according to preference criteria. Practical example: logistics agent that chooses the ideal carrier, weighing price, deadline and reliability.

Learning agents

They improve with experience: they analyse results and adjust. Practical example: agent that classifies leads and, seeing which ones converted, learns to prioritise profiles with higher purchase intent.

Collaborative and hierarchical agents

Collaborative agents work as a team, sharing tasks. Practical example: in an online store, one agent welcomes the customer, another checks stock, another processes payment and another sends confirmation.

Hierarchical agents organise themselves in levels: a top-level agent defines goals and delegates to subordinates. Practical example: campaign management that delegates ads, segmentation and analysis to specialised agents.

How to choose the right type for your business?

Most projects combine several types: reactive for FAQs, goal-based for qualifying leads, learning to improve over time. The first step is to map a repetitive process with measurable impact.