In the AI industry, agents are not secret service BFFs, but basic software entities that make decisions on their own. We can think of them as digital intermediaries that use sensors (data, sensors, user interfaces) to observe their surroundings and use activators (APIs, commands, messages) to act within them to achieve a given goal. The art of building effective AI systems lies in knowing that there is no single universal agent.
Choosing the right architecture - from the simplest reactive to the most complex, goal-oriented - is critical to the success or failure of a project. As the data shows, mistakes at this stage are costly: according to a report by Gartner, as many as 40% of AI agent projects will be canceled by the end of 2027 due to architectural problems such as improper orchestration or poorly chosen solution types. This article is a guide to different types of agents that will help you make an informed choice and avoid the pitfalls that lead to project failure.
Before we analyze the different types of agents, it is worth establishing what the description and comparison of agents is all about. The PEAS model, which provides the basis for understanding any agent system, can be helpful here. It is an acronym for:
Defining PEAS for a given problem is the first and most important step, which naturally leads to the selection of the appropriate type of agent. Skipping this step is a sure way to end up with a project that will never see the light of day.
AI agents are typically classified according to the increasing complexity of their internal architecture and their ability to understand the world. This evolution reflects the path from simple, reactive automata to entities capable of planning and learning. In this guide, we will focus on three fundamental types that represent milestones in this evolution: simple reflex agents, model-based reflex agents, and goal-based agents. Understanding their mechanisms, strengths, and limitations is crucial. Despite the popularity of LLMs and so-called autonomous agents, even advanced frameworks struggle with reliability. As a recent benchmark shows, various popular solutions only correctly perform about 50% of tasks on a set of 34 realistic programming problems. Some of these errors stem precisely from the mismatch between the abstract power of LLM and a robust, appropriate agent architecture for a given task.
A simple reflex agent is the most basic component of intelligent systems. Its operation is based on a direct conditional “if-then” link. It does not analyze the history of events or create a model of the environment - at a given moment, it simply assigns a specific perception to a specific action. The operating scheme here is very simple: perception leads to the application of a conditional rule, which in turn triggers an action.
How does it work in practice? The internal logic of this agent is a set of static rules. For example: IF the temperature sensor signals >22°C, THEN turn off the heating. The whole process takes place without considering whether the temperature rose gradually or suddenly – the reaction occurs immediately after the reading.
The advantages of this type of agent include, above all, speed of response, ease of implementation, predictability of operation, and low computing power requirements. On the other hand, the lack of consideration of context and previous events can be problematic. In situations where the system has only partial observability of the environment and the sensors do not provide complete information about the state of the world, such an agent is helpless. It is also unable to take actions aimed at achieving a specific state in the future, which severely limits its scope of application.
Under what conditions does it work best? A simple reflex agent is suitable for working in stable and fully observable environments characterized by simple cause-and-effect relationships. Classic examples include various types of control devices, such as thermostats, simple spam filters, and safety features in mechanical devices. So, we can say that where there is a high degree of certainty about the state of the system, such an agent works really well.
When the environment is not fully visible, a simple reflex agent may fail. This is where the model-based reflex agent in AI comes into play. Its key innovation is that it has an internal model of the world that records what it cannot see directly.
What exactly is this “model”? It is an internal representation of how the world changes over time and how the agent's actions affect these changes. It can be a simple variable that stores the last state, or a more complex set of equations and relationships.
How does it work? The architecture of this agent develops the scheme of a simple agent: Perception -> World Model Update -> Current State Assessment (based on the model) -> Conditional Rule -> Action. Thanks to the model, the agent can distinguish between the same perceptions in different contexts. For example, when it sees a closed door, it “knows” (thanks to the model) whether it closed it itself (so it doesn't need to open it) or whether it has been closed for a long time (so it can try to open it).
Why is this so important? This type of agent introduces the concept of internal state and memory, which is a huge step towards more flexible and reasonable intelligence. It enables operation in real, non-ideal environments where information is fragmentary. Example: A simple vacuum cleaning robot that maps a room so it doesn't go over the same spot twice uses a basic model of the world.
Model-based agents operate in a more intelligent manner, but their responses are still based on rules about the current state. Goal-based agents in artificial intelligence go a step further: they use a model of the world not only to understand the present, but also to actively plan and simulate the future in the context of a specific goal.
The role of the goal: A goal is a description of a desired state of the world (for example, “packaging in the warehouse completed” or “customer received the correct answer”). For a goal-based agent, action is not the end, but a means to achieve that state.
Mechanism of action: The architecture is as follows: Perception -> Model Update -> Goal Analysis -> Search/Promotion -> Action Selection. The agent uses its model to simulate a sequence of possible actions (“what will happen if I do A and then B?”) and chooses the path that it believes will most effectively bring it closer to its goal. This introduces the concept of utility—not all paths are equally good.
Power and challenge: This type of agent is much more flexible and powerful than its predecessors. The same architecture can pursue different goals by changing their definitions, without the need to rebuild the rules. The challenge lies in the computational complexity of searching the state space and the need to precisely define the goal and success metrics. In complex goal-based agent projects (often implemented by teams of LLM agents), problems with orchestration and observability most often arise, which, according to analyses, block 40% of projects from going into production.
Beyond the trinity discussed above, the spectrum of agent types expands to include even more advanced forms:
Application of theory in real-world systems that we encounter every day
Choosing the right type of agent is not just an academic exercise, but a critical engineering decision that has a huge impact on the future of the project. Understanding the basic types of agents - from simple reactions to data-driven models to goal-oriented agents - provides a solid foundation. However, transforming this theory into a stable, efficient, and easy-to-monitor production system is another matter entirely. It requires deep practical knowledge, proven architectural patterns, and tools for orchestrating, monitoring, and managing the agent lifecycle. Therefore, in the face of such a high risk of failure, the best strategy is often to work with an experienced partner. Working with a supplier who not only knows the theory but also has a track record of successful practical implementations of various types of agents in real business scenarios allows you to avoid costly mistakes and inefficient development paths.