We’ve just gotten used to one type of AI, and another is already coming: Here’s what the increasingly popular AI agents are

The difference between talking and acting

The main difference that separates agent artificial intelligence from generative, on which popular chatbots are based, is autonomy. While ChatGPT is reactive and waits for a human command for every step, AI agents are proactive. They can independently plan, reason and implement multi-level actions to achieve a given goal. It’s like the difference between an assistant who answers your questions and a project manager who takes a goal, breaks it down into tasks and carries it out to the end.

These systems use the same powerful language models (LLM), but are enhanced with components for memorization, planning, and the ability to use other software and tools. This enables them not only to generate text, but also and to take concrete actions, such as updating CRM systems, sending personalized e-mails or even scheduling meetings based on responses, all without constant human intervention.

How they work, who develops them and what they are not good at (yet).

Srž the work of an AI agent is a cyclical process: based on a given goal, the agent first creates a plan, and then breaks it down into a series of steps. It chooses the best tool for each step – for example, browsing the Internet to gather information, writing and executing code to analyze data, or using an API to send e-mail. After each action, the agent analyzes the result, learns from it and adjusts the next step, repeating the process until it reaches the final goal.

Pioneering projects like Auto-GPT and AgentGPT were the first to show this potential to the general public, while specialized agents like Devin, focused on software engineering, appeared in the meantime. Big tech companies are also developing their own versions, and Microsoft’s Copilot is increasingly gaining agent capabilities.

Their greatest strength lies in the automation of complex digital tasks that require the use of multiple tools. However, they are still not perfect. They can often get stuck in a loop repeating unsuccessful actions, misinterpret ambiguous instructions or “hallucinate” actions that do not lead to a solution. Reliability is still their biggest challenge.

Agents in practice: from smart home to finance

Application of AI agents moreć is visible in a range of industries. Analysts from Gartner predict that by the end of 2026, as many as 15 percent of everyday business decisions will be made autonomously through agent artificial intelligence. For example, an agent can manage energy consumption in a smart home by coordinating the operation of a smart thermostat, lighting and home appliances in real time.

In finance, agents analyze market trends and automatically adjust client portfolios to protect investments and achieve higher returns. In sales, instead of just writing an e-mail draft, the agent can identify potential clients in the database, send them a customized message, track responses and independently schedule a meeting in the calendar.

Impact on the workplace

The arrival of agents also dramatically changes the working environment. Microsoft’s annual “Work Trend Index” report for 2026 shows that employees are increasingly using AI for more complex cognitive tasks such as information analysis, problem solving and creative thinking. Almost half (49 percent) of all recorded interactions with AI assistants fall into that category.

However, research also reveals the so-called “transformational paradox”: employees are often more ready for change than the organizations in which they work. Data show that organizational factors, such as culture and manager support, have twice as much influence on the successful application of artificial intelligence as individual effort. The companies that are the fastest to enter and adapt their processes in order to use the potential of agents achieve a significant advantage.

Risks of autonomous action

While they bring enormous productivity benefits, the autonomous nature of AI agents also opens up new risks. Experts warn that, if left unchecked, agents can behave like malware, acting independently and potentially causing harm. New security challenges are emerging, such as “agent hijacking”, where attacksč trick the system into performing unauthorized actions. In addition, complex ethical questions arise about responsibility when things go wrong. Who is to blame – the user, the developer or the artificial intelligence itself? These are challenges that we have yet to solve as a society.

*with the use of AI