THE’Agentic AI represents one of the most interesting and discussed developments in the artificial intelligence sector. This technology, in fact, introduces the possibility for AI systems to make autonomous decisions and carry out complex objectives with minimal human supervision. If generative artificial intelligence models – such as those that write texts (e.g. OpenAI’s ChatGPT) and produce images (e.g. Google’s Nano Banana) – have revolutionized content creation, agentic AI moves to the next level: it doesn’t just generate output on demandbut use this content and skills to act, plan and coordinate with other AI agents or tools.
A system of this type, therefore, not only processes data, but is also able to interact with external applications, carry out online searches, query databases, book services or modify an operational plan based on new information received. This ability derives from a set of distinctive characteristics: autonomythat is, the possibility of acting without continuous supervision; proactivitywhich allows agents to anticipate scenarios and propose actions; specializationwith agents dedicated to different tasks that can collaborate with each other; adaptabilitythanks to the ability to learn from feedback and improve over time; and finally intuitivenessbecause users can interact with these systems using natural language, without needing to access complex interfaces.
Agenetic AI and its advantages
From the description given in the previous lines, it can be seen that the use of agentic AI can have numerous advantages. Let’s give you just a few examples. In the financial sector, an agent can analyze markets in real time and make autonomous transactions. In self-driving vehicles, the ability to process data from GPS and sensors makes navigation safer and more precise. In the healthcare sector, intelligent agents can monitor patient parameters and suggest therapeutic changes based on the latest tests performed. In cybersecurity, systems of this type identify anomalies in network traffic and react promptly to potential cyber threats.
Compared to “traditional” AI, which operates within rigid limits and provides predefined outputs, agentic AI introduces the dimension of autonomous choice. It is capable of formulating strategies, redefining the necessary steps and asking for support from humans or other systems only when needed. And this is where the difference also lies with generative AI: if the latter needs input to produce content, agentic AI integrates that same content into a broader process, aimed at achieving an objective.
All this therefore produces concrete advantages in terms of productivitybecause interfaces can be replaced by simple voice or text commands; in terms of efficiencyas systems can operate without interruption and handle repetitive tasks at reduced costs; and in terms of innovationthanks to the ability to discover connections or solutions that a human could discover over longer periods of time than an AI agent.
How Agentic AI Works and the Challenges
The functioning of agentic AI is divided into several phases. First of all there is the perceptioni.e. the collection of data through sensors, APIs, databases or direct interactions with users. Followed by reasoningwith which the system interprets the data and extracts significant patterns. At that point the goalselaborated one strategy and, through a decision-making process, selected the optimal action among different possibilities. THE’execution leads to concrete interaction with external tools or people, while the learning and adaptation cycle allows you to continuously improve performance. A crucial role is played byorchestrationi.e. the coordination of multiple agents who collaborate towards a common goal, avoiding bottlenecks or resource conflicts.
Of course they also exist challenges to take into account. The main risk concerns the definition of objectives and reward functions. A poorly configured system could find unwanted shortcuts in order to maximize a score, for example favoring sensational content to increase online engagement or sacrificing product quality to speed up a logistics process. Then there are the technical problems related to data management, privacy, transparency and the need for robust infrastructures to support multi-agent architectures. For this reason, early implementations are usually tested on low-risk tasks, until the systems demonstrate some consistency and reliability.
