Types of AI Agents: 6 Categories and When to Use Each One [2026]
Not all AI agents are equal. Some react to simple stimuli; others coordinate entire teams of specialized agents. Choosing the right type determines whether your investment generates results or becomes an expensive experiment. The AI agent classification you're about to see is based on Russell and Norvig's taxonomy, updated with 2026 advances in LLM, orchestration, and multi-agent systems. If you need prior context, check what is an AI agent or our complete AI agents guide.
1. Simple Reactive Agents
A simple reactive agent operates with direct rules: if X occurs, execute Y. It has no memory, doesn't maintain an internal model of the environment, and doesn't learn from previous interactions. Its behavior is deterministic and predictable.
The clearest example is a spam filter that analyzes keywords in an email subject and classifies it as legitimate or unwanted. Another common case: a monitoring system that triggers an alert when a server exceeds 90% CPU usage. It doesn't interpret, doesn't anticipate, only reacts.
When to use them: repetitive tasks with clear conditions and no need for context. They're fast, cheap, and reliable within their limited scope. If the problem is solved with a finite set of rules, a reactive agent is the most efficient option. Complicating it with an LLM would be oversizing the solution.
2. Model-Based Agents
Model-based agents maintain an internal representation of the world that allows them to anticipate changes and make more informed decisions. Unlike reactive ones, these agents remember previous states and use them to predict what will happen next.
Tesla and Waymo are the most visible references: their autonomous vehicles build an environment model from sensors, predict the trajectory of other vehicles and pedestrians, and act accordingly. In the business realm, an inventory management agent that predicts seasonal demand peaks and adjusts orders automatically applies the same principle.
When to use them: dynamic environments where conditions change and anticipatory planning adds value. If your business depends on forecasting trends or demand flows that evolve, this type of agent makes the difference versus the reactive one.
3. Goal-Based Agents
A goal-based agent has an explicit goal that guides all its decisions. For each possible action, it evaluates which one brings it closer to its objective and executes it. It doesn't follow fixed rules or react to stimuli: it reasons about which path to take.
Practical example: a project management agent whose goal is to complete a sprint on time. It identifies blocked tasks, detects dependencies, reallocates resources, and prioritizes based on impact on the delivery date. Reasoning about multiple options is what distinguishes it from previous types.
When to use them: when the goal is clear but there are multiple paths to reach it. They're ideal for customer support automation where the goal is to resolve the ticket, but routes vary: query the knowledge base, process a return, or escalate to a human.
4. Learning Agents
Learning agents improve their performance over time through machine learning and reinforcement learning techniques. Each interaction generates data that the agent uses to adjust its future behavior. They don't remain static: they evolve.
Netflix and Spotify are paradigmatic examples. Their recommendation systems analyze consumption patterns, test variations, and continuously optimize what content to show each user. In customer service, a learning agent analyzes which responses generate higher satisfaction (CSAT), which formulations reduce resolution time, and adapts its communication style accordingly.
When to use them: when you have sufficient data volume and performance benefits from progressive personalization. If each interaction is unique and not predictable with rules, continuous learning separates a mediocre agent from an excellent one. Higher implementation cost, but return scales with volume.
5. Hierarchical Agents
A hierarchical agent functions like a manager who orchestrates specialized agents (sub-agents). The main agent receives the request, determines what type of task it is, and delegates to the sub-agent with the appropriate specialization.
Real example: a main support agent that receives all queries and delegates to a technical sub-agent (product problems), a commercial one (questions about prices and plans), and a claims one (returns and complaints). Frameworks like CrewAI and LangGraph facilitate building these hierarchies by defining roles, capabilities, and communication protocols between agents.
When to use them: complex tasks requiring specialized knowledge in different areas. A single generalist agent doesn't master all domains with the same depth. The hierarchical structure allows optimizing each sub-agent for its area while the main one manages routing. It's the dominant pattern in contact centers with AI agents.
6. Multi-Agent Systems
In a multi-agent system, several agents collaborate horizontally without strict hierarchy. Each agent is responsible for one phase of the process and passes the result to the next: triage, resolution, follow-up, quality control. Coordination occurs between equals, not by vertical delegation.
Example: a large-scale customer service system where one agent classifies the query, another resolves it by accessing internal systems, a third schedules post-resolution follow-up, and a fourth evaluates the interaction quality. Frameworks like CrewAI, LangGraph, and LangChain allow designing these flows with declarative definitions of agents and their interactions.
When to use them: high-volume operations with multiple differentiated stages. If you process hundreds of daily interactions that go through different phases, a multi-agent system distributes the load, scales each component independently, and reduces bottlenecks. It's the model for companies that already surpassed the "one agent for everything" phase.
Comparison Table
| Type | Memory | Autonomy | Complexity | Ideal for |
|---|---|---|---|---|
| Simple reactive | No memory | Low | Low | Alerts, filters, simple rules |
| Model-based | Internal state | Medium | Medium | Prediction, inventory, driving |
| Goal-based | Contextual | Medium-High | Medium-High | Project management, support |
| Learning | Cumulative | High | High | Recommendations, personalization |
| Hierarchical | Distributed | High | High | Multi-domain support, contact center |
| Multi-agent | Distributed | Very High | Very High | Large-scale operations, pipelines |
Which One to Choose for Your Company
The rule is simple: start with the simplest thing that solves your problem. If your queries are answered with clear rules, a reactive agent is enough. If you need context and anticipation, a model-based agent is the next step. For complex operations with high volume, hierarchical and multi-agent systems are where 2026 companies are investing.
It's not about implementing the most sophisticated architecture, but the most appropriate one. To move from theory to practice, our guide on how to create an AI agent takes you step by step. And if your main channel is WhatsApp, check AI agent for WhatsApp.
Conclusion
The six types of AI agents cover from simple rule-based tasks to distributed systems coordinating dozens of specialized agents. The AI agent classification you've seen gives you a framework to evaluate what your company needs and avoid oversizing or falling short. For the complete context, return to our AI agents guide.
GuruSup deploys AI agents on WhatsApp and web adapted to the type of architecture your business needs. Without custom development, with direct integration to your CRM and native Spanish support. Try GuruSup free and discover which agent type fits your operation.
