What are AI agents?

An AI agent is a software program or system capable of per­form­ing tasks autonom­ously using ar­ti­fi­cial in­tel­li­gence. It acts on the basis of data, rules or models and adapts its behaviour to its en­vir­on­ment.

What are AI agents by defin­i­tion?

An AI agent is a software system, or in some cases, a hardware-based system, that perceives and processes in­form­a­tion from its en­vir­on­ment and makes autonom­ous decisions based on that data.

AI agents, a key element within agentic AI, are defined by their ability to act autonom­ously – that is, without direct human control. These systems use sensors or other input methods to capture data and rely on al­gorithms or models to determine the most ap­pro­pri­ate course of action. The goal of an AI agent is to solve problems or optimise processes based on pre­defined ob­ject­ives.

Another defining feature is their ability to learn: many AI agents use machine learning to refine their strategies and improve decision-making over time. Unlike basic programs, they can adjust to changes in their en­vir­on­ment and adapt their behaviour ac­cord­ingly.

How do AI agents operate?

AI agents follow a con­tinu­ous cycle of four main stages:

1. Per­cep­tion
2. Pro­cessing
3. Decision-making
4. Action

First, they gather in­form­a­tion from their en­vir­on­ment using physical sensors, digital in­ter­faces or external data streams. This raw data is then processed by AI models such as decision trees, prob­ab­il­ist­ic methods or neural networks, con­vert­ing it into an abstract rep­res­ent­a­tion of the current en­vir­on­ment or situation. The agent then evaluates possible actions by comparing them against pre­defined goals to select the most effective action. The key advantage of agentic AI becomes clear when we compare it with gen­er­at­ive AI: AI agents actively develop strategies and adapt their behaviour dy­nam­ic­ally, while gen­er­at­ive models primarily respond to input.

What dis­tin­guishes AI agents is their autonomy: they operate without constant human oversight and adapt their decisions based on changing con­di­tions. Ad­apt­ab­il­ity, meaning the ability to learn from past ex­per­i­ences and improve decision-making, is another critical component. Many agents use re­in­force­ment learning, which involves using positive and negative feedback to adjust their strategies over time. This com­bin­a­tion of in­de­pend­ent per­cep­tion, model-based pro­cessing and adaptive learning makes AI agents well suited to dynamic and un­pre­dict­able en­vir­on­ments.

What types of AI agents are there?

AI agents vary depending on how they work and their level of com­plex­ity. They range from simple reactive systems to advanced, learning-enabled solutions. Below is an overview of the five key types.

Simple reflex agents

Simple reflex agents react to current stimuli without con­sid­er­ing prior states or future outcomes. They follow straight­for­ward ‘if–then’ rules: when a specific condition is met, the agent performs the cor­res­pond­ing action. Simple reflex agents work best in stable, pre­dict­able en­vir­on­ments. This makes them less effective in complex or un­pre­dict­able situ­ations.

Model-based reflex agents

Model-based reflex agents differ from simple reflex agents in that they maintain an internal model of their en­vir­on­ment. This allows them to account for factors that are not directly ob­serv­able. As a result, they can respond more ef­fect­ively to their en­vir­on­ment and adapt to con­di­tions that change over time. For example, a robot vacuum might create a map of a room, or a security system may monitor multiple entrances. Despite these en­hance­ments, model-based reflex agents are still reactive: they respond to the current situation and can only plan ahead to a limited extent.

Goal-oriented agents

Goal-oriented AI agents base their actions on clearly defined goals. Instead of just reacting to what’s going on around them, they evaluate which sequence of actions is most likely to get the result they want. To do this, they use search and planning al­gorithms to compare different possible courses of action. Typical examples of goal-oriented agents include nav­ig­a­tion systems that calculate the fastest route to a des­tin­a­tion or chess programs that plan several moves ahead. Compared to reflex-based agents, they can handle more complex decision-making but require more computing power.

Utility-based agents

Utility-based agents expand the goal-oriented approach. Their aim is not only to achieve a goal, but also to optimise the outcome. To do this, they use a utility function that evaluates how ‘good’ each possible action is. This allows the agent to balance competing goals and make decisions that account for those trade-offs. A common example is a self-driving car that must take safety, travel time, and energy con­sump­tion into account when deciding how to act.

Learning agents

Learning agents, as the name suggests, are known for their ability to improve over time. They observe the outcomes of their actions, adapt their strategies ac­cord­ingly, and use that feedback to make better decisions going forward. This kind of learning is often im­ple­men­ted using ap­proaches such as su­per­vised learning and seen in re­com­mend­a­tion engines, chatbots and AI-driven game programs that change how they act based on past results. Learning agents are par­tic­u­larly useful in en­vir­on­ments that change over time, where fixed rules are not suf­fi­cient and systems must con­tinu­ously adapt to new con­di­tions.

What are some typical use cases for AI agents?

AI agents have long moved beyond the research en­vir­on­ment and are now widely used across a range of in­dus­tries. Their potential lies in their ability to process complex datasets, identify patterns, and make autonom­ous decisions based on that in­form­a­tion. As a result, they are used in a wide range of areas:

• Customer service and com­mu­nic­a­tion: AI agents are used in chatbots and virtual as­sist­ants to answer enquiries, resolve simple issues and reduce the workload for human service staff.
• Health­care and medicine: In health­care, AI agents are used to support diagnosis by analysing patient data and identi­fy­ing patterns in large datasets. They are also used in robots that assist with surgery and to monitor patients.
• Industry and man­u­fac­tur­ing: In pro­duc­tion en­vir­on­ments, AI agents monitor and control machines, predict main­ten­ance needs and improve resource use. This helps prevent downtime and improve pro­ductiv­ity.
• Mobility and logistics: Autonom­ous vehicles and drones use AI agents to sense their sur­round­ings and make decisions in real time. In logistics, agents also support route planning and warehouse man­age­ment.
• Business and finance: In business and finance, AI agents are used to analyse market data, support trading decisions and assess risk. They are also used to provide automated advice and detect fraud. AI agents based on Agentic RAG are par­tic­u­larly powerful in this area as they combine the strengths of gen­er­at­ive models with targeted knowledge retrieval.

What op­por­tun­it­ies and chal­lenges do AI agents present?

AI agents offer sig­ni­fic­ant potential but also come with new chal­lenges. They can boost ef­fi­ciency, reduce costs and support in­nov­a­tion in almost every area of life. At the same time, their use in­tro­duces risks related to tech­no­lo­gic­al de­pend­ence and data pro­tec­tion, as well as ethical issues. Poorly trained agents can make incorrect decisions or reproduce un­in­ten­ded bias. That’s why re­spons­ible de­vel­op­ment is essential, with a strong focus on trans­par­ency and security.

Overview of the pros and cons of AI agents

Why AI agents matter

AI agents represent an important step in the de­vel­op­ment of in­tel­li­gent systems. By combining autonomy, ad­apt­ab­il­ity and learning cap­ab­il­it­ies, they can be used across a wide range of fields. As AI research continues to advance, their practical value will keep growing. At the same time, it is essential to recognise the risks and lim­it­a­tions and to use this kind of tech­no­logy re­spons­ibly. Over the long term, AI agents are set to play a central role in in­tel­li­gent auto­ma­tion and shape the way people interact with tech­no­logy in everyday life.