How do AI agents work?

AI agents emerge as a logical extension of generative artificial intelligence, offering a new degree of autonomy and flexibility in task execution. They differ significantly from traditional chatbots, which are often static and unable to adapt to the user context. With the help of technologies such as machine learning, natural language processing, and contextual understanding, AI agents can work independently, collaborate with other agents, and add real value to the business by automating processes without requiring major internal transformations.

The sudden popularity of ChatGPT in 2022 prompted many companies to start experimenting with generative artificial intelligence in an attempt to automate activities such as customer service or content creation. But while useful, these tools are often limited—they don’t adapt based on interaction and aren’t deeply integrated into specific business systems. AI agents fill this gap. They don’t just respond to commands, they perceive tasks, reason, and make decisions, acting autonomously to achieve specific goals. 

In this context, Zoran Arsovski from VertoDigital explains that AI agents can be implemented to work alongside human teams, taking on repetitive and tedious tasks. This allows employees to focus on more important and strategic aspects of their work. Dejan Genovski from Appolica demonstrates how even a company using a simple messenger for communication can take advantage of the advanced capabilities of AI agents without drastically changing its internal processes.

A study by Mulesoft and Deloitte shows that 93% of IT leaders plan to introduce autonomous agents by 2027, and almost half have already started working in this direction. However, Gartner warns that the industry is moving from a phase of high expectations to a period of realization, in which it will face disappointments before reaching a stage of stable and mature solutions. This also applies to AI agents. Yasen Kiprov from SiteGround notes that although we cannot yet fully trust them, development is so rapid that soon the question may be reversed—whether agents can trust people.

The complexity of managing multiple AI agents simultaneously should also not be underestimated. Vera Tonkova from Visibilio.AI emphasizes that the true power of this technology is revealed when multiple agents coordinate their actions to perform more complex tasks. This builds on the ability of a single agent to perform only one specific task excellently.

The basis of AI agents is the transformer model – the same architectural principle behind GPT in ChatGPT. Transformers are trained on huge text arrays, learn to predict words, and thus prepare themselves to perform much more complex tasks without necessarily “understanding” the world in a human way. The agent uses this language model, combined with a software interface, to act autonomously, being able to plan, make decisions, and perform actions without constant supervision.

An example of this is the automation of the vacation request process—the agent not only enters the data into the HR system, but also automatically reschedules the relevant commitments when changes occur. According to Gartner, by 2027 70% of developers will use AI tools for coding, compared to less than 10% in 2023, and 80% of companies will have integrated AI tools for software testing.

Autonomous systems can already write basic code that can be reviewed and adapted by developers. This makes the technology easy to adopt and implement, even for organizations with less technical capacity. However, according to Sergi Sergiev of CatWing, the biggest challenge is not the technology itself, but organizational culture and readiness for change. He recommends that employees first gain access to AI accounts and training in writing prompts, after which they will begin to create agents according to their needs.

The difference between bots, assistants, and agents is also important. Google defines AI assistants as applications that interact directly with the user, understand natural language, and suggest actions, but the final decisions remain in the hands of the user. AI agents, on the other hand, involve a cycle of perception, thinking, and action—they collect data, reflect on it using an LLM model, make decisions based on memory and training, and perform specific actions, often through API integrations.

Depending on the tasks, there are different types of agents. Reactive agents respond to the environment without keeping a history – for example, in smart home appliances or simple customer chatbots. Deliberative agents rely on internal models for strategic decision-making—they are used in autonomous cars or logistics networks. Hybrid agents combine both approaches—for example, a robot that avoids obstacles in real time while planning its route to the goal. In a business context, reactive agents handle routine tasks such as emails, while deliberative agents optimize processes for long-term efficiency.

AI agents are no longer just a concept, but a real-world technology. Although implementation brings its own challenges, the potential for automation, efficiency, and strategic transformation is enormous. We are yet to see how these systems will enter the daily work of companies – from the smallest start-ups to large corporate structures.