Why Agentic AI is the True Artificial Intelligence
In recent months, while attending conferences and events dedicated to technology, I have noticed that one term recurs with increasing insistence: agentic AI. Not as a trendy word destined to disappear in a season, but as the center of gravity around which the presentations of major industry players revolve, from cloud multinationals to the most innovative startups. It is the theme that dominates the stages of events such as Google I/O, Microsoft Build, and Computex. It is the direction toward which everyone, some openly and some less so, is converging. And there is a precise reason why this is happening now.
What is Agentic AI?
To understand this, let's start from the beginning. The artificial intelligence we know today, namely that of chatbots, virtual assistants, and models that answer our questions, essentially behaves like a very sophisticated echo. We ask a question, it processes an answer, and stops. It is a powerful tool, capable of summarizing, translating, writing, generating images, but it remains fundamentally reactive: it waits for someone to prompt it and responds to what is asked, without any initiative of its own.
Agentic AI is something structurally different. An AI agent does not wait for step-by-step instructions: it receives a goal, autonomously decomposes it into subtasks, decides how to approach them, uses tools like search engines, corporate databases, external APIs, other agents, and completes the work, reporting the results. It is the difference between a collaborator to whom every single step must be explained and one to whom a project can be entrusted with the certainty that it will find its own way to complete it. This is not a refinement of traditional AI: it is a role change.
In a complex agentic system, multiple agents work in parallel under the supervision of an orchestrator. Each one takes care of a specific task, communicates with others, and contributes to a result that none of them could have produced alone. It is an architecture that, as we will see, resembles something very familiar.
What Does “Intelligence” Really Mean?
Before moving forward, it is worth pausing on a word we use every day without questioning its real meaning: intelligence. The common idea is that intelligence is a unitary and total quality (you either have it or you don’t), a sort of general cognitive ability that scales uniformly across all domains. But that’s not how it works, neither in humans nor in machines.
A chess champion is not necessarily a good surgeon. A brilliant mathematician may struggle to read emotions in a room. A three-year-old child learns its mother tongue with a facility that even today challenges any artificial system, yet struggles with a quadratic equation. In reality, intelligence is the ability to perform specific tasks well within a defined context. And the more precise the context, the more refined the performance.
If we want to find an operational definition of intelligence that makes sense for both biological beings and machines, we can put it this way: something can be defined as intelligent when it operates in a manner as similar as possible to the human brain. It perceives the context, adapts behavior, learns from experience, and produces useful outcomes in the real world. With this lens, the question of how intelligent an AI system is becomes much more interesting.
Why the Human Brain is Still the Reference
Human beings are the most intelligent species on the planet not because they have the strongest muscles, the sharpest senses, or the greatest immediate survival prowess: indeed, on these fronts, many other species far surpass us. Our cognitive superiority depends on an organ weighing about 1.4 kg that consumes just 20 watts of energy: the brain. And the secret of the brain lies not in its size but in its connected architecture.
The human brain contains about 86 billion neurons. Each neuron can be connected to thousands of others through structures called synapses, the points of contact between nerve cells. It is estimated that the total number of synaptic connections in the adult brain exceeds 100 trillion. It’s not the number of neurons that determines the complexity of thought, but the network of relationships between them. The signal propagates through this network like an electrical impulse generated when a neuron receives enough stimuli from incoming connections and ‘fires’ to outgoing ones. This is how information is transmitted.
What makes the brain extraordinary is its plasticity: connections strengthen or weaken based on use, experience, and learning. Each time something new is learned, something changes physically in the structure of the brain. This ability to reconfigure its architecture in response to experience remains one of the most ambitious and difficult goals to replicate in artificial intelligence.
The frontier of this challenge is well illustrated in projects like Neuralink, the company founded by Elon Musk that is developing direct interfaces between brain and machine, implantable devices capable of reading neuronal electrical activity and transmitting it to a computer, or vice versa. Initial human tests have shown that it is possible to control a cursor on the screen with just thought or to type text without moving the hands. It is still a nascent field, but it demonstrates how understanding brain electrophysiology is opening doors that just a few years ago seemed like science fiction. Studies of this sort, of which Neuralink is just the most commercial embodiment, highlight how there is a correlation between the way the brain operates and computer systems.
AI Agents Like Neurons in the Brain
Let’s return to agentic AI and try to look at it through the same lens we used to observe the brain. The parallel is more precise than it appears. In a multi-agent system, each agent specializes in a specific task: one searches for information, one analyzes it, one interacts with the user, one monitors security, one executes code. No agent knows everything, but together they produce a result that no one could achieve alone. Exactly like neurons: no single nerve cell ‘knows’ how to do one thing, but the network they form with each other generates thought, memory, language, emotion.
The analogy becomes even more interesting when considering the problem of hallucinations in traditional AI models. A large generalist language model like those underpinning the most widely used chatbots must respond to any question on any topic with a single system. The result is that the model tends to ‘fill in the gaps’ with plausible but not necessarily accurate information, producing errors that the user does not always identify. It’s the cost of the ambition to know everything.
A specialized agent, on the contrary, works within a defined perimeter with precise tools and verified data. It does not have to pretend to know what it doesn’t know, because its job is not to know everything but to do one specific task well. This delimitation of context drastically reduces errors, increases reliability, and makes the behavior of the system much more predictable and controllable. The more constrained the context, the more precise the agent can be. The more specialized agents collaborate, the more powerful the overall system becomes. Just like in the brain.
What Happens Behind the Scenes: CPU, GPU, and Tokens
To truly understand how an agentic AI system works and why it requires so much computing power, it’s useful to delve briefly into the technical side, without getting lost in details. When you write a message to an AI system, the words are not processed as they are. They are first converted into tokens, numerical units that represent chunks of text, sometimes an entire word, sometimes part of it. This is the language that machines really understand: not letters, not words, but sequences of numbers.
This conversion, and all the mathematical computation that follows—namely billions of multiplications between matrices of numbers that represent the relationships between tokens—is the job of GPUs, or graphics processing units. GPUs were not originally designed for AI, but their architecture, composed of thousands of small cores capable of performing calculations in parallel, makes them perfect for these kinds of massive and repetitive operations. A modern GPU can perform tens of trillions of operations per second, and the most powerful AI systems use thousands of them in parallel.
But GPUs alone are not enough. In a complex agentic system, someone must orchestrate everything: decide which agent to activate, in what sequence, with what data, monitor the state of each process, manage exceptions, and ensure that the results of one agent are correctly passed to the next. This logically sequential coordination work, rich in conditional decisions and memory management, is the domain of the CPU. The CPU is the conductor: it does not play any instrument, but without it, musicians produce noise, not music. In a modern AI system, CPUs and GPUs work in close synergy: the CPU manages the logical flow and orchestration of agents, while the GPUs handle the heavy computation of inference. Removing either one breaks the balance.
And it is for these reasons that, with the overwhelming advent of agentic AI, CPUs are becoming crucial again, even in data centers where all these computations must be managed. Not coincidentally, Jensen Huang, CEO of NVIDIA, centered his keynote at Computex on the new technologies Vera and Vera Rubin.
The Gap that Still Separates Machines from Humans
At this point, the question that naturally arises is: how far are we from the moment when artificial systems will be able to equal or surpass human intelligence? The honest answer is: much farther than headlines would suggest. Not because AI is not progressing at an extraordinary pace, but because the human brain is a system of a complexity we have yet to replicate even remotely.
Faithfully simulating even a mouse’s neural network would require computational power and energy consumption vastly exceeding what is available today. The human brain, with its 100 trillion synapses, each dynamic and modifiable, operating in parallel with a consumption of 20 watts, remains the most efficient and complex processing system we know. Building something equivalent in silicon would require a technological leap of proportions we still cannot quantify precisely.
But there is a different perspective from which to view this issue, and perhaps it is the most important one. In the last 150 years, humans have invented penicillin, the airplane, the atomic bomb, the computer, the internet, cryptocurrencies, and artificial intelligence. None of these things existed in nature. None were deducible from the observation of the world as it was. They were imagined, built, and made real by the human ability to conceive what has never existed, to make a leap into the void towards something that, before being invented, could not even be described. Humanity still has much to explain—about its evolution and its role in the world: none of this is deducible from observing reality as it is, and hypothetically solvable with artificial intelligence.
Artificial intelligence, no matter how powerful, is still fundamentally a system that extracts patterns from what has already been. It learns from existing data, generalizes from past examples, optimizes within already defined problem spaces. It is extraordinarily good at doing this better and faster than humans. But the question that no one yet has an answer to is another: will an artificial system ever make the leap that humans have made every time they invented something that had no precedents? Will it be able to imagine what has never been? Will it one day invent something whose shape we today cannot even begin to fathom?
Perhaps yes. Perhaps it is right there that agentic AI, with its ability to combine specialized agents in always new ways and to explore solution spaces that no single human brain could traverse, carries the seeds of something radically new. Or maybe imagination, understood as the ability to conceive the yet non-existent, will remain for a long time the most exclusive territory of biological intelligence. In the meantime, the game has just begun.