AI Agents: From Assistants to Effective Leaders of Tomorrow?

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AI Agents: From Assistants to Effective Leaders of Tomorrow?

The rapid evolution of artificial intelligence from a mere tool for the execution of an experimental agent … and, possibly, leadership. As AI systems begin to be able to remember complex reasoning we must * face the big question: what is the next step? Here I explore the possibilities of stimulating AI as a leader, i.e. manager, consultant, CEO, or as head of state. Let's discuss the great potential of a Utopian Hyper-Perice-Elecper-driven, unenlightened society, while exploring the inherent dangers of algorithmic selection, of uncontrolled surveillance, and erosion of human response. Then a balanced system emerges, where the AI brain is governed by human-imposed limits on large bursts of intelligence.

It's no news that artificial intelligence is rapidly and continuously evolving and evolving. But let's stop thinking about this in detail. We are already moving well beyond the initial pleasures of conversations and image generators to the most complex AI programs that have permeated all of science, technology and entertainment. And now we're getting to deeper discussions about the role of AI in making complex decisions. Since last year, more advanced programs have been proposed and continue to be developed that can test the most complex subjects, even the quality of difficult scientific research, engineering problems, and coding. And this is just the tip of the iceberg. As the capabilities of AI grow, it is not a big place to think about these programs taking on roles as project managers, consultants, and “managers” in various fields – in the extreme, it is possible even as guys. Yes, I know it sounds hearty, but that's why we better talk about this now!

AI in the lab: The new scientific revolution

If you follow me, you know that I come from the world of academia, more precisely the world that revolves around the molecular biology of species that is done both on computers and in the wet lab. As I am witnessing first hand how the world of education is feeling the impact of AI and automation. I was there as a CASP official when Deepmind launched its alphafold models. I was there to see the evolution of protein structure prediction to expand beyond protein design (see my comment on the related NOBEL prize The Biology of Communication).

Emerging startups are now deploying automated labs (to be honest, they still rely heavily on human experts, there is still a way to go) to test new molecules at scale, or to allow competitions between an AI program or another type of AI for molecules. I use the power of AI to summarize, extract, find and process information, code, and more.

I also follow leaderboards and can be surprised by the development of thinking skills, and everything new that comes, many working in project planning, execution, maybe even managers – the last key to the discussion I present here.

As concrete, the most recent example, the conference called agent4Science 2025 is set on papers and reviews produced entirely by AII Agents. This “Sandbox” Environment will allow researchers to study how AI-driven science approaches human research, and to understand the strengths and weaknesses of these systems. All of this is directly in line with the human vision of a future where AI is not just an assistant or a special agent but actually a planner, and, why not, a leader.

And it goes without saying that this is not just a theoretical exercise. New startups like QED are developing platforms that use “critical thinking ai” to test scientific claims, force them into claims and reveal their underlying logic to find weaknesses. I've tried it on some manuscripts and it's amazing, even if they're not wrong to be honest – but they'll definitely improve. This automated approach can help reduce a lot of pressure on human reviewers and accelerate the pace of scientific discovery. As Oded Rechavi, the Creator of QED, puts it, there is a need for alternatives in a publishing system that is often characterized by delays and controversial reviews. And tools like QED can provide much needed speed and insight.

Google, like all the Giants are Tech Giants (although I'm still waiting to see what with the apple …), and pushes the boundaries with AI that can turn and improve scientific software, in some cases high artistic tools created by people. Have you tried their new AI search mode, and how to track the results? I've been using this feature for a week and it's still amazing.

All this you have seen, that I bring to the world of education but certainly (if not all) other TDS students also analyze only science (and any other work of the world) but also any other human work) but for the purpose of its development. Demonstrating this is the development of AI systems that can find their own “algorithms” that reach state-of-the-art operations that have never been encountered before.

Of course, there have been bumps along the way. Remember for example how the galactica of meta galacta went down shortly after its release due to its tendency to produce details that are trampiauneble but similar to the hallucinations of today's LLM programs but the orders are still huge! That was a true tragedy that serves as a critical reminder of strong validation and human oversight as we integrate AI into the scientific process, and especially to put it further.

From AI as a coder souman to ai as a manager

Of course, and here you will feel more identified if you are a programmer yourself, the world of software development has been radically transformed by the plethora of AI-Powered assistants. These tools can generate code, identify and fix bugs, and describe complex code in natural language. This not only speeds up the development process but also makes it available to a wider range of people.

The principles of AI-driven evaluation and execution of tasks are also being used in business and the Management World. AI-enabled project management tools are becoming more common, capable of work scheduling, resource allocation, and tracking. These systems can provide a level of efficiency and oversight that a human manager might not be able to accomplish alone. AI can analyze historical project data to create optimized schedules and predict potential roadblocks before they happen. Some say that by 2030, 80% of the work in today's project management will be eliminated as AI takes over traditional tasks such as data collection, tracking and reporting.

With all the AI algorithms?

The concept of “automated management” is a fascinating and controversial one. But … If AI can soon manage complex projects and contribute to scientific discoveries, can it also play a role in governing our societies?

On the one hand, AI can bring unprecedented efficiency and data-driven decision-making to governance. It can analyze big data to create more effective policies, eliminate human choice and deception, and provide personalized services. An AI-Powered system can even help anticipate and prevent conflicts, such as disease outbreaks or infrastructure failures. We are already seeing this in action, with Singapore using AI-powered Chatbots for citizen services and Japan using a powerful seismic system to predict earthquakes. Estonia has also been a leader in digital governance, using AI to improve government services in healthcare and transport.

However, the risks are equally important. Algorithmic Bias, the lack of transparency in “Black Box” Programs, and the potential for mass surveillance are all major concerns. A MASH BANK OR-DRICKING CARD CREMENT CORRECTION APPROVED SYSTEM WAS FOUND TO MAKE SECONDARY SIGNS VISIBLE TO MEN WITH SIMILAR FINANCIAL BACKGROUNDS, A CLEAR EXAMPLE OF HOW PRECISION HISTORICAL DATA CAN LEAD TO HISTORICAL RESULTS. There is also the question of accountability: Who is responsible when an AI system makes a mistake?

A hybrid future: Human-Ai management

Perhaps the most logical and desirable future is one of “disliked taxpayers” where AI supports human decision-makers rather than spending money. We can draw inspiration from existing political systems, such as the Swiss model of the collective head. Switzerland is governed by a seven-member Federal Council, with the Presidency rotating annually, a system designed to protect the balance of power and promote consensus-based decision-making. We can imagine a future where the same model is used to manage humanity This can allow a balanced and powerful decision-making process, with people providing behavioral guidance and understanding the content that AI asy. As such, people can be part of a board that makes decisions together in consultation with special AI systems, and then the final system, outputs and manages their performance.

The concept of decentralized governance has already been tested in the world of Blockchain and independent decentralized organizations (DAOS). These organizations operate on Blockchain protocols, with rules embedded in smart contracts. Decisions are made by a community of members, usually through governance tokens that provide voting power. This model removes the need for a Central Authority and allows for a more transparent and democratic form of government.

The decentralized nature of this system can also help reduce the risk of putting too much power in the hands of a single entity, be it human or machine.

The road to this future is still a long way off, but the building blocks are being laid today – which is why it's worth engaging in these types of consultation sessions now. As AI continues to evolve, it's important that we have an open and honest conversation about the role we want it to play in our lives. The potential benefits are great, but so are the risks. By proceeding with caution, and by designing natural systems rather than replacing human intelligence, we can ensure that AI is a force for good in the world.