What are AI agents? How can they support processes in modern business?

There are many diverse technologies in the world of artificial intelligence. Chatbots, voice assistants, recommendation systems, and classic predictive models all fall under the broad AI category. Among them, AI agents stand out for their level of autonomy, flexibility in operation, and ability to make independent decisions.

While traditional AI systems largely operate reactively—responding to specific user queries and performing pre-defined functions—AI agents are designed as autonomous entities. They can independently analyze situations, identify goals, and select appropriate action strategies without constant human supervision. They are capable of making decisions even with incomplete data or in changing conditions. This is a significant advantage over solutions based on pre-programmed rules or conversational scripts.

Another major difference lies in the aspect of learning. Many traditional AI systems operate on static models, which do not change after training or require manual updates. AI agents, on the other hand, can actively learn from their own experiences, for example using reinforcement learning. As a result, they can modify their strategies depending on the outcomes of previous actions.

An additional advantage is the ability to function within multi-agent systems. AI agents can communicate and collaborate with other agents to solve more complex problems. In such setups, agents can negotiate, share information, coordinate actions, or compete. Traditional AI technologies usually function as single units performing specific, limited tasks.

AI agents are systems designed to operate autonomously and make decisions based on environmental data. To achieve this, an AI agent must have an internal structure that enables it to collect information, analyze it, plan, and execute actions. This structure is called the agent architecture, and its core modules perform specific functions:

• Profiling module (perception/observation) – this component is responsible for collecting information about the environment in which the agent operates. It can use sensors, input data from systems (e.g., user clicks, financial data, application signals), and messages from other agents or systems. The goal of this module is to provide the agent with up-to-date knowledge about the environment.
• Memory (short-term and long-term) – the agent has an information storage system that enables it to record its action history (logs, session records), remember important rules, and learn from experiences. Thanks to this function, it can not only respond to current stimuli but also use previous data.
• Planning module (decision/cognitive) – this is the “brain” of the agent. This component is responsible for analyzing the situation based on data collected by the profiling module and selecting the most effective strategy or set of actions. In more advanced agents, this module may use artificial intelligence algorithms (e.g., machine learning, fuzzy logic).
• Execution module – this part of the system is responsible for implementing the decisions made. Depending on the type of agent, this may involve making a physical move (e.g., in robotics), sending a message to a user, or modifying the digital environment (e.g., changing system parameters, performing a financial operation). This module ensures the agent not only „knows” what to do but also actually carries out the planned actions.

A key feature of intelligent agents is their ability to act rationally even without full knowledge of the environment. In this way, they can make decisions based on risk estimation and probabilities, adapt to new conditions, and modify their strategies in real time.

Autonomous decision-making units can be categorized based on complexity, information processing method, and ability to learn and operate in an environment. The most common types include:

• Simple reflex agent – responds to current stimuli using simple „if-then” rules. It does not analyze the past or predict the future. AI agents examples include thermostats that turn on heating when the temperature drops below a threshold.
• Model-based reflex agent – maintains a simplified internal model of the environment that allows it to predict the effects of actions and make more accurate decisions. This is how a robotic vacuum operates, remembering a home’s layout and avoiding already-cleaned areas.
• Goal-based agent – analyzes actions in terms of whether they bring it closer to a defined goal. It makes decisions by planning a sequence of steps. A GPS system that calculates the best route to a destination is a typical example.
• Utility-based agent – its task is to maximize utility. It can consider different scenarios and trade-offs. Autonomous vehicles, for instance, select optimal routes considering safety, time, and energy consumption.
• Learning agent – capable of self-improvement through experience. It consists of four components: performance module, learning module, critic, and problem generator. This is how recommendation systems (e.g., Netflix, Amazon) operate.
• Hierarchical agent – higher levels are responsible for strategy and planning, while lower levels implement actions. This helps manage complexity in large systems, such as production lines.
• Multi-agent systems – complex systems where many independent agents cooperate (or compete), sharing tasks and information (e.g., autonomous vehicles coordinating urban traffic).