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

In the age of digital transformation, companies turn to AI to cut costs, streamline operations, and deliver better customer experiences. One of the most powerful tools driving this shift are AI agents—autonomous systems that can analyze data, make decisions, and adapt to dynamic conditions. Discover how they work and what benefits they bring to modern business.

In the era of digitization and automation, more and more companies are turning to artificial intelligence. Their goal is to streamline operations, reduce costs, and improve customer service. One of the most promising applications is AI agents—autonomous systems capable of making decisions and acting in dynamic environments. In this article, we will define what AI agents are, how they work, how they differ from other AI technologies, and what benefits and challenges are associated with their implementation in business.

What are AI agents?

Definition says they are autonomous computer programs designed to operate independently in a specific environment. This means they can:

Observe the environment – using sensors, input data, or communication channels, they gather information about the state of the environment in which they operate. The environment may be physical (e.g., in the case of robots) or digital (e.g., recommendation systems, chatbots, or virtual assistants)
Analyze data and make decisions – based on the collected information, agents can interpret the situation, identify problems or goals, and then choose the most appropriate course of action. They may use logic algorithms, heuristics, artificial intelligence (including machine learning), or planning techniques.
Act to achieve specific objectives – agents perform specific actions (e.g., send responses, change the state of a system, learn new solutions) aimed at achieving a desired outcome. Their actions are usually focused on maximizing efficiency, accuracy, speed, or another defined success criterion.
Act autonomously – once programmed, they do not require constant human supervision. They can independently respond to changing conditions and adapt to new situations.

AI agents can operate independently (e.g., a simple bot responding to customer inquiries) or as part of multi-agent systems in which they cooperate or compete with other agents to jointly solve complex problems.

What are AI agents used for? The applications are broad and may include simple reactive tasks (e.g., turning on lights when motion is detected) as well as complex decision-making systems (e.g., traffic management in smart cities, autonomous vehicles, computer games).

Currently, 88% of companies are actively exploring or testing AI agents, and 12% have implemented them at scale.

How AI agents differ from other AI technologies

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.

How do AI agents work?

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.

Role of large language models (LLMs) in AI agents

Now that we know what are AI agents and how do they work, it is worth considering how their current form has been shaped by the development of large language models. According to experts, LLMs have made agents significantly more flexible and „intelligent.” They can understand natural language, analyze the context of utterances, and make decisions in real time. Unlike earlier rigid systems, modern solutions can engage in complex dialogue, plan actions, and independently execute tasks.

Techniques such as ReAct or ReWOO are critical for enabling agent “thinking”. These allow problems to be broken down into stages and thoughtful decisions to be made. Additionally, integration with external tools and APIs enables agents to perform real-world actions (e.g., writing emails, analyzing data, or managing enterprise systems).

Thanks to LLMs, a new generation of autonomous AI agents has emerged that not only understand commands but also independently plan, act, and learn in real time. Without such models, this level of advancement would not be possible.

What are the types of AI agents?

What are the main types of AI agents?

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).

And what are vertical AI agents?

Unlike general-purpose agents, vertical systems are specialized in a single domain (e.g., finance, retail, HR, or healthcare). Their strength lies in being tailored to specific industry needs, taking into account data specifics, regulations, and typical decision-making processes. For example, in healthcare, a vertical AI agent can analyze patient data, predict disease risks, and suggest treatment paths in line with regulations such as HIPAA. In retail, such a system might analyze shopping behavior and recommend products in real time.

The development of vertical AI reflects a broader trend in the history of technology: a shift from general-purpose solutions to specialized platforms. Just as enterprise software evolved from general to specialized platforms, AI is now moving in the same direction. Vertical agents offer a significantly higher return on investment by automating tasks and processes with high precision and minimal integration effort.

What are the benefits of using AI agents?

Now that we understand what are autonomous AI agents, it is worth considering the benefits of implementing them in business. These include:

Process automation – AI agents can operate 24/7 without breaks. This greatly reduces task completion time and improves company responsiveness.

Scalability – the ability to handle thousands of events simultaneously. This allows companies to respond quickly to sudden demand spikes without hiring additional staff.
Personalization – tailoring offers, content, and recommendations. Agents analyze user data in real time. This enables more targeted marketing campaigns and product offers.
A learning infrastructure – improved performance over time. The longer agents operate, the better they understand the business environment and can more accurately predict needs and risks.
Increased productivity – by eliminating routine tasks. Employees can focus on activities requiring creativity or strategic decision-making.

McKinsey estimates that the phased implementation of generative AI across business functions could result in productivity gains equivalent to annual values of USD 2.6–4.4 trillion for the global economy.

Using AI agents – challenges

Despite their enormous potential, implementing modern solutions in a company requires significant involvement and expertise. What are the main challenges in developing AI agents?

Computational complexity and implementation cost – advanced models require high computing power. This generates significant costs related to infrastructure and system maintenance.
Lack of training data (for learning agents) – agent effectiveness depends on data quality and quantity, and many companies lack appropriate datasets or cannot access them ethically and legally.
Difficulties integrating with existing systems – many enterprises operate on legacy IT systems that are not compatible with modern AI solutions.
Ethical considerations in using AI agents – questions arise about the transparency of AI decisions, accountability for errors, and the need to minimize bias embedded in data.
Security – as agents become more autonomous, the risk increases that they may make unpredictable or undesirable decisions.

How to mitigate potential risks? Trust a provider who knows what they’re doing and has extensive implementation experience.

How to use AI agents in your business?

How to create an AI agent? What are the ethical considerations when using AI agents? With us, you don’t need to know the answers to these questions. We will assist in conducting a detailed analysis of internal business processes to identify those that can be automated. We will select the appropriate agent architecture tailored to the complexity of the task.

Likewise, we will advise on how to collect and prepare the necessary data to train the model, and how to ensure data quality and legal compliance. Before deployment, we will also conduct testing and validation in a controlled environment. Naturally, our support does not end with the system handover. After launch, we will assist with monitoring and updates.

RITS provides end-to-end services in the design and implementation of AI agents, tailored to the specific needs of each client. Contact us to jointly develop the optimal solution tailored to your needs

Warsaw, August 21, 2025

Interested in leveraging AI agents for your business? Get in touch with us! At RITS, we know how to turn this technology into real value.

We’ll help you identify the areas where AI agents can bring the most impact, select the right solutions, and run all necessary tests. Our team will handle the full implementation — ensuring seamless integration with your existing systems, processes, and workflows.

Aleksander Zamoyski, New Business Account Manager at RITS Professional Services, will be happy to share his expertise: a.zamoyski@rits.center.

Let’s stay in touch!