Torque Clustering: An astounding paradigm shift towards the Agentic AI.

Literally weeks ago, Gartner published the top tech trend for 2025. NUMBER 1 was ‘Agentic AI’. It’s indicative that I had to ask my wife what it was. We both had to look it up. Somehow I feel this moment might be akin to when we saw our first website (for me, in 1993, my long suffering French tutor showing me how he could see what was on the menu at a Canadian University canteen.

Agentic AI refers to artificial intelligence systems that can make decisions, take actions, and pursue goals independently, without constant human intervention, based on learned patterns or programmed instructions.

Already, almost in the time it took me to find out what it was, Agentic AI has taken a quantum leap.

Researchers have introduced Torque Clustering, a groundbreaking AI algorithm that mimics how sentient beings learn by observing and interacting with their environment without explicit instructions.

Traditional AI, and I can’t believe I’m using the word ‘traditional’ here,  relies on supervised learning and human-labelled data. Who knew? Sounds labour heavy to me. Torque Clustering on the other hand is able to work autonomously to identify patterns in vast datasets. Naturally the comments about it being a game changer and feels like finance and medicine but nobody so far has mentioned it’s affect and Education. How much of the traditional career path of the students in your class will be directly affected by software that can basically think on its own and achieve the tasks on their own?

Agentic AI is already creating issues with availability of some employment. Any data driven tasks are rapidly disappearing, and educators are already trying to catch up to give their students an edge.

Some examples are

MIT’s Schwarzman College of Computing is embedding AI across disciplines, trying to ensure students gain hands-on experience working alongside AI systems.

Harvard Business School is incorporating AI-driven case studies, preparing students for a world where AI is a core decision-making tool.

Carnegie Mellon has deployed an AI curriculum to prepare its graduates for the future of work.

The stunning secret behind Torque Clustering lies in astronomical physics. Inspired by torque balance in gravitational interactions between galaxies, the AI now organises data based on two fundamental properties of the universe: mass and distance.

Just as galaxies merge due to gravitational forces, data points in a complex dataset naturally cluster together when torque is balanced. This self-organizing principle allows the algorithm to adapt seamlessly to diverse datasets with varying shapes, densities, and noise levels. An example might be think of data analysis like stirring a cup of coffee. Add sugar, and with each swirl, the granules find their place—not through strict rules but through natural forces acting upon them. Torque Clustering works the same way, allowing AI to uncover hidden patterns without the need for predefined labels.