The next generation of AI will no longer be a single omniscient system, but a network of smaller, specialized models – so-called “nano agents” – connected by an orchestrator. How does this work, where is it already in use, and why is it a step closer to human intelligence?
When artificial intelligence gets it wrong, it has a special term for it – “hallucination.” A friendly name for simply making something up. Sounds familiar, right? Humans do it all the time. Except now we’re not alone in this.
Just as we have friends who warn us when we're talking nonsense, artificial intelligence has its own "controllers" - digital watchdogs who check whether its answers are correct. But the story doesn't end there: AI is learning to orchestrate itself.
Literally. In the background, they are being born orchestrators, digital conductors that coordinate multiple smaller models, each with its own knowledge. Instead of one “omnipotent” system, an orchestra of smart specialists is now being formed, which together create a more accurate and meaningful result. So – To err is human – this also applies to artificial intelligence.
Orchestrators – digital conductors
Large models like ChatGPT-5 are now more like a symphony orchestra than a single brain. Each part of the system has its own role: one model understands language, another recognizes images, a third analyzes data, a fourth verifies the truth of claims. But above them all stands the conductor – AI orchestrator.
This orchestrator coordinates which model will play at the right moment. In practice, this means that the system itself chooses which tool is appropriate for a given task and how to connect their outputs. This reduces errors, duplicates fact-checking, and increases accuracy.
This already works in medicine. Fujifilm's system Synapse Orchestrator combines the outputs of several different diagnostic algorithms (MRI, CT, X-ray) into a single result. This way, the doctor does not see ten different graphs, but a single, consolidated summary. Companies such as Adobe and Microsoft are also developing similar orchestration systems that combine different AI modules into a meaningful whole.
Nano models – small but ingenious
If the orchestrator leads, then they are nano models those who play. Small, specialized, but surprisingly effective.
Instead of one giant model that knows “everything,” the new generation is based on the crowd mini models, each for its own area: one for the calendar, another for legal documents, a third for medical reports, a fourth for communication.
These models are designed to run quickly, efficiently, and often directly on the device—without a connection to the cloud. Qualcomm and NVIDIA have already introduced small language models that can run on smartphones or laptops and still think almost as well as their bigger brothers.
Imagine: your phone notices that you're going to miss a meeting. It checks traffic, suggests a new route, sends an apology, and plays you a summary of the presentation on the way. All this in a matter of seconds, without you having to open a single app. That's the power of nano agents.
When orchestra and nano models work together
The real magic happens when the orchestrator connects multiple nano models into a common task.
Let's say you want to book a vacation. The orchestrator sends a command to five agents: one checks the weather, another checks flights, a third checks hotels, a fourth checks your calendar, a fifth checks your budget. It then connects them and presents you with the optimal solution – the cheapest flight, a hotel with a pool, a free date, and a list of restaurants nearby. In the meantime, you just choose a date and sip your coffee.
This is no longer science fiction. Platforms built on so-called “agent ecosystems” are already experimenting with such orchestrations, where agents talk to each other, check each other, and even correct each other if one makes a mistake.
Intelligence that can repair itself
The biggest difference between today's and tomorrow's artificial intelligence will not be that what does he know, but how he knows how to think about his mistakes.
New systems are capable of self-correction – checking whether their answers are consistent with other models, and if not, initiating revision. This means that AI can generate an idea today, and tomorrow it can check it, improve it, and only then present it to a human.
In practice, it is a transition from the "big brain" that tells everything, to network of smart assistants, who work together and check each other. The result: fewer errors, more reliability and a more human decision-making logic.
Where is this taking us?
If we live in adolescence today artificial intelligence – a period when he still often talks nonsense – tomorrow we will enter an age of maturity. Then every problem we have will have its own digital expert.
The big models will still exist, but they will play the role of generalists. Specialized nano-agents will operate around them, and orchestrators will connect everything together into a harmonious network of collaboration.
Instead of one “super-smart” AI, we’ll have a network of digital companions who can think together—and fix themselves when they go wrong. Which, if you think about it, isn’t that far off from what humans do.
Conclusion: To err is human – and so is artificial intelligence
In a few years, when we watch our digital agents in the background over our morning coffee arranging vacations, reviewing documents, and planning our day, we may just smiled.
Mistakes won't disappear. They'll just disperse. Only this time – fortunately – we won't be the only ones making mistakes. We'll have an interlocutor by our side who, like us, can admit that they're not always right.
And this is perhaps the most human trait that artificial intelligence has ever developed.
