AI Agents were introduced in 2025. They started as simple automation and chatbot assistants. Now they evolved into systems that observe the environment, decide what to do next, and take action across real workflows.
Table of Contents
The AI Agents execute jobs, call tools, update systems, and influence decisions that once required human judgment.
People often use the terms bot, AI assistant, and AI agent as if they mean the same thing.
- A chatbot usually follows a script. It does what it’s been told to do, nothing more.
- AI Assistant is more flexible, but it still waits for you to ask something. It responds, then stops.
- AI Agents can observe what’s happening, decide what to do next, act on that decision, and then adjust their behavior based on the result. In many cases, it doesn’t need constant instructions.
What is in the AI Agent?
Even though AI Agents can look complex, the idea behind them is quite simple.
- An AI Agent first needs a way to understand what’s going on around it. This is done through a profiling or perception layer. This layer collects data from the environment.
- An AI Agent needs memory. This is where it keeps facts, rules, and past experiences. Without memory, an AI Agent can’t improve or adapt.
- Next comes planning. Based on what it sees and remembers, the AI Agent decides what action makes the most sense.
- Finally, there is the action. This is the part where the AI Agent actually does something in the real world or in a digital system.
The AI Agent is smart, depending on how well these parts work together. Some AI Agents work alone, and some others coordinate with other agents by sharing information and tasks.
Make offers the possibilities to make your own AI Agent, without coding. Here is another interesting our Blog Post about Make Automations.
The five main types of AI Agents
Not all AI Agents are equally advanced. Some are very basic and reactive. Others can plan, optimize, and learn over time. Here are the main types of AI Agents, starting from the simplest one.
Simple Reflex Agents
Simple Reflex Agents live in the present moment. They look at the current situation and react immediately. There is no memory and no understanding of what happened before. If a condition is met, an action is triggered. This works well in environments that are predictable and fully observable.
Example of a Simple Reflex Agent: a thermostat. If the temperature drops below a threshold, heating turns on. If it goes above, heating turns off. The problem is that as soon as the environment becomes more complex or unpredictable, these agents struggle. They can’t adapt, and they can easily get stuck repeating the same actions.
Model-Based Reflex Agents
Model-Based Reflex Agents add one important improvement: they remember. They keep a simple internal model of the world and update it as things change. This allows them to handle situations where they can’t see everything at once.
Example of Model-Based Reflex Agents: a robot vacuum. It doesn’t just react to obstacles. It remembers where it has already been and where it still needs to go. That memory makes its behavior much more useful.
These agents are still mostly reactive, but they are far more reliable in real-world environments than simple reflex agents.
Goal-Based Agents
Goal-based agents don’t just react. They think ahead. Instead of asking “What should I do right now?”, they ask “Which action moves me closer to my goal?”. They evaluate different possible actions and choose the one that seems most promising.
Example of Goal-Based Agents: the navigation app. They don’t just follow the next road. They plan a route based on distance, traffic, and estimated time. The goal-based agents can handle more complex tasks because they consider future states.
Utility-Based Agents
Utility-based agents take things a step further. Reaching a goal is not always enough. Sometimes there are many ways to reach it, and some are better than others.
Utility-based agents evaluate how good an outcome is. They balance trade-offs. Speed versus safety. Cost versus quality. Risk versus reward.
Example of Utility-Based Agent: the self-driving car. It doesn’t only try to reach its destination. It constantly balances comfort, safety, efficiency, and time. Utility-based agents are designed for exactly this kind of decision-making.
Learning Agents
Learning agents are the most flexible. They don’t rely only on fixed rules or predefined models. Instead, they learn from experience. They observe the outcome of their actions and adjust their behavior over time. This means they can adapt to new situations, changing environments, and unexpected behavior.
Example of Learning Agents: recommendation systems. The more you interact with them, the better they understand your preferences. The same idea applies to chatbots that improve through feedback or game AI that discovers better strategies by playing again and again. Learning agents are especially valuable in environments where nothing stays the same for long.
Knowing the 5 types of AI Agents will make possible that you choose the best for your everyday tasks and business.
Which type of AI Agent is the right one for your task?
When people choose an AI agent, they often start with tools. That’s usually the wrong place to start.
The better question is simple: what problem are you actually trying to solve?
Different types of AI Agents work well in different realities. Some assume the world is stable. Meanwhile, other types of AI Agents assume the environment changes. When those assumptions don’t match the real environment, things break in very confusing ways.
If a task is repetitive and clearly defined, simpler agents usually work better. They’re easier to build, easier to test, and easier to trust.
As soon as a task requires planning multiple steps, goal-based or utility-based agents start to make more sense.
But here’s a common mistake: assuming that “complex problem” automatically means “learning agent“.
Another thing people underestimate is the environment. In stable systems, simple agents can work well for years. In systems that change very fast, what matters is adaptability.
There’s also the human side. Sometimes decisions need to be explained. In those cases, predictable behavior is often more valuable than flexibility. A slightly less smart system that you understand can be better than a very smart one that you don’t.
A simple rule can help:
- If you can clearly define the rules, don’t learn.
- If you can clearly define the goal, don’t over-optimize.
- If you can clearly define what “better” means, then optimize carefully.
Signs you picked the wrong AI Agent
When an agent is a poor fit, the problems usually show up quickly.
- Outputs feel unstable.
- You are retraining the AI Agent all the time.
- Failures are hard to explain.
Most of the time, this is because the agent design doesn’t match the problem.
Fix the design, and many of those issues disappear.
