There is little doubt: 2025 is rapidly emerging as the year of AI agents. Implementations seem to be skyrocketing in all industries, and new AI agent solutions are announced every day.
AI agents are often described as the more capable versions of AI assistants. They can “understand” situations and take action on their own. While AI assistants wait for human input and operate within predefined rules, agents are more autonomous. They can make decisions and complete tasks that consist of multiple steps. They take initiative and operate with limited human involvement. As such, they have huge potential for use by companies and organizations. They can add value at various points in business operations, including cost reduction, service improvement, customer satisfaction etc.
Yet, not every system marketed as an AI agent has agentic capability, and not every implementation will create business value. Analyst firm Gartner expects more than 40 percent of agentic AI projects to be abandoned within the next two years. Reasons include rising costs, unclear business value, and poor management of AI risk.
In light of that, it is useful to understand what AI agents are, and what types of things they can realistically do for companies. Most classifications see AI agents as falling into five or six categories.
They are the most basic version of AI agents, functioning like if-then statements. They get input, compare it to predefined action rules, and execute a response. Think chatbots that answer frequently asked questions and systems that turn on a light when motion sensors detect movement. The process is triggered by an action, and unfolds according to predefined rules.
Simple reflex agents are consistent in their behaviour, and therefore fast and reliable. They can be useful in highly-regulated industries, where it is important to ensure that rules are always enforced equally. These agents have no memory and cannot learn, so they are good only in situations where responses do not require context from previous interactions. Therefore, organizations have to define triggers up front, and update them as business conditions change.
These agents are more advanced, as they keep a “mental map” of their environment. They react not only to what they “see” at the moment, but also to what they “know” about the world. This internal model of the environment allows them to “understand” what is happening and “predict” what might happen. Because of this, they can make decisions and work well even in situations where they don't have all the information. An example of this is a virtual assistant that “remembers” earlier conversations and gives answers that are consistent with the whole conversation, not just the last prompt.
Model-based reflex agents constantly learn through new input. This improves their understanding and accuracy in decision-making. Their ability to take informed action using both past and present information makes them useful in dynamic environments. Think of a smart thermostat in a hotel room. If the temperature drops, it doesn’t immediately turn on the heat. It first checks whether the window is open, if there are guests in the room, and what the outside temperature is, then decides what to do based on its model of the environment.
These AI agents focus on achieving a specific goal. They evaluate possible actions and choose the one that brings them closest to the goal. They go beyond immediate reaction. They plan, anticipate future scenarios, and consider the implications of different actions in order to choose a strategy that will enable them to achieve their goal. Adaptability is their key advantage.
For example, in the hospitality industry, a booking optimization agent might need to maximize room occupancy. It evaluates different pricing or promotion strategies and selects the one that most effectively increases the number of confirmed bookings, adjusting its actions as market conditions change.
This group of AI agents is perhaps best understood in contrast with the previous one. Goal-oriented agents base their decisions on whether an action will bring them closer to their goal or not. Imagine a robot delivering a package to a specific location. It has one clear goal, and doesn’t evaluate the quality or cost of its actions. It simply takes the steps that lead to achieving the goal, even if they’re not optimal.
In contrast, utility-based agents evaluate different options and assign a value to each according to how much benefit it brings. A great example of utility-based AI agent is a recommendation system used by video streaming platforms. It doesn’t make random decisions; instead, it evaluates multiple options and selects the one that is most useful and interesting to the user.
Utility-based agents consider several factors (speed, safety, energy consumption, risk) and choose the best overall solution. They look not only at what is good right now, but also at how their decisions will affect them in the long term. For example, an AI-based financial advisor analyzes multiple investment options and factors (risk, profit, market stability) and chooses the combination that brings the highest overall benefit, not just the fastest profit.
These are intelligent systems that learn from their own experience. Based on feedback from their environment, they learn what works and what doesn't, change their behavior, and constantly improve their performance without the need for human assistance. Thanks to this ability, they can react quickly and adapt even in complex and changing environments. These agents have the ability to recognize and exploit patterns in data (repetitions, anomalies, or trends).
Learning agents can be applied in a range of industries, and in diverse ways. In customer support, they learn from previous conversations with users. Over time, they provide more accurate and useful answers. They help detect and prevent fraud because they can recognize patterns of suspicious behavior.
They consist of multiple intelligent agents that communicate with each other, share information, and collaborate to solve complex tasks together. Each agent has its own knowledge and role. Connected in a system, they work as a team towards the same goal. Tasks are shared among agents, so the system uses collective intelligence to make better decisions and achieve more efficient results. Imagine a smart hotel system where one agent manages reservations, one handles room allocation, another one takes care of maintenance etc. Each agent has its own task, but they all work in sync to ensure hotel operations are optimized.
Multi-agent systems are adaptable and flexible. New agents can be added quickly, and expand the system without interruption. Companies using them can quickly adapt to changes and introduce innovations. Multi-agent systems can be implemented in various industries, and help increase efficiency, automate processes, and speed-up innovations.
The ability to do things without constant (or any) human involvement is the ultimate promise of AI agents. Many companies are looking to improve their business in this way, but not all implementations will give the desired result. It is important to keep in mind that the success of any agentic system - regardless of scale - will be measured by its ability to create business value. Gartner has already warned that many solutions presented as “agentic AI” are just simple AI assistants or chatbots, without real agentic capabilities. For companies looking to innovate and truly “get on the AI train”, the key to success is in two areas: careful planning to ensure AI agents are applied to the right problems, and leadership that merges deep industry knowledge with expertise in AI integration.
If you’re looking for a results-driven, innovative software development partner to help capitalize on new, profitable opportunities, reinvent your brand, or deliver incremental value to your business, we can help.
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