Why the new form of intelligence is learning how to command AI agents
By Ali Ansari
For most of modern professional history, intelligence was measured by how much complexity a person could personally manage. The smartest people in the room were those who could hold entire systems in their heads, financial models, legal frameworks, operational processes and manipulate them faster or more accurately than others.
That definition is quietly collapsing.
I was reminded of this recently at a gathering of London’s AI community. What stood out was not raw intelligence in the traditional sense, but a different kind of fluency. The most effective people in the room were not those doing the work themselves. They were those designing, directing, and supervising systems that do the work for them.
The centre of gravity has shifted from execution to orchestration.
From Doing the Work to Directing the Machines
You could survive thirty years ago without using a computer. Fifteen years ago, you could survive without using the internet. That is no longer true.
We are now at the beginning of a similar transition with AI agents. You may not be using them today, but the world being built around you increasingly assumes that you are.
This is not about novelty or hype. It is about leverage. When machines can execute cognitive tasks cheaply and continuously, the advantage moves to those who can define objectives, set constraints, and evaluate outcomes.
Those machines are increasingly referred to as agents. Think of an agent as software that can make decisions and take actions without you clicking buttons or writing instructions each time. It’s the difference between a calculator waiting for input and an assistant that knows what you need done.
Three Layers of Agents in Everyday Life
To understand what agents actually do and why they matter, it helps to think about them in three layers. Not as abstract technology, but as capabilities that are already seeping into daily life.
1. Automated Process Agents: Removing Friction
The first layer is the least visible and the most widespread.
Consider something as mundane as managing meetings. An automated process agent can:
- Receive a meeting request
- Check availability across calendars
- Schedule the meeting
- Generate and send a Zoom or Teams link
- Update all participants automatically
Nothing about this feels intelligent. And that is precisely why it is powerful. These agents eliminate small but constant sources of friction. They reduce administrative burden and free attention for higher-value work.
At scale, the same pattern runs payroll, reconciles transactions, monitors systems, flags anomalies, and triggers remediation.
Most organisations already depend on these agents, even if they still call them “automation.”
2. Research Agents: Turning Complexity into Choice
The second layer is where agents begin to feel distinctly capable.
Imagine booking a holiday to Zurich. You care about price, but also about comfort. You have preferences on airlines, hotel quality, proximity to transport, and timing. A research agent can scan flights, hotels, reviews, pricing patterns, and availability, then present a small number of options, each with clear trade-offs explained.
The agent does not decide for you. It sharpens your judgment.
This same dynamic applies to market research, regulatory analysis, medical literature, or investment due diligence.
Research agents compress vast amounts of information into something a human can act on. They fundamentally change the cost and speed of understanding.
3. Autonomous Agents: Acting on Your Behalf
The third layer is where agents move from advising to acting.
Imagine an agent managing your investment portfolio in the way a private banker might. You define the objectives and constraints: risk tolerance, time horizon, ethical exclusions, liquidity needs.
The agent monitors markets continuously, rebalances positions, harvests tax losses, and adjusts exposure as conditions change.
Human involvement shifts from execution to oversight.
This layer also introduces new risks. Agents fail unpredictably, they misread context, or optimize for the wrong objective. When they act on your behalf, you still carry the consequences.
Effective orchestration requires understanding the work deeply enough to catch errors. You must grasp what good looks like before you can safely delegate it.
Today, these systems are constrained and carefully supervised. Over time, they will become more reliable, more personalised, and more economically significant.
What This Means for Work
The obvious question: if machines do the work, what’s left for people?
The answer isn’t comforting for everyone. Some roles, particularly those involving pure execution of clear, repeatable rules will simply vanish or shrink dramatically. That displacement is real and won’t be painless.
But new leverage also creates new opportunities. The person who learns to deploy agents effectively can accomplish in an hour what previously took a team a week. A small business can suddenly operate with capabilities that once required enterprise scale. Individual practitioners can compete with institutions.
Ten years ago, being good at your job meant being fast in Excel. Today it increasingly means knowing what questions to ask, which tools to point at the problem, and whether the output makes sense. The skill isn’t calculation, it’s judgment about what needs calculating.
The advantage now lies with those who:
- Define goals rather than perform tasks
- Set constraints rather than follow rules
- Supervise systems rather than execute instructions
- Judge outcomes rather than produce first drafts
Those who cling to execution as the primary marker of intelligence will find themselves outpaced; not by smarter machines, but by better-orchestrated ones.
Ownership, Control, and the Open Question
It is tempting to reach for historical metaphors about labour and ownership of slaves, but the reality is more nuanced.
Today, we do not truly own agents. We rent them. We pay companies for access to models, compute, and orchestration layers that remain firmly under platform control. In that sense, commanding agents is less about ownership and more about effective delegation within constraints set by others.
That may not always be the case. A fully open-source agency model, where individuals and organisations host, govern, and adapt their own agents also exists but this is for those who have learnt how to master the machines.
Whether agents become rented services or owned capital will determine who captures the value. Platform control means recurring costs and dependency. Open-source ownership means agents as assets. This is not a technical question, it’s about who builds wealth from intelligence, and who merely pays for access to it.
This tension helps explain why large technology platforms are moving aggressively to define standards, tooling, and ecosystems. Control over agents is quickly becoming control over economic leverage.
What This Shift Really Means
I did not leave that room concerned. I left recalibrated.
The world has changed what it means to be competent. Intelligence is no longer about how much complexity you can personally carry. It is about how effectively you can deploy systems that carry complexity for you.
Agents are not arriving with drama or fanfare. They are becoming an assumption; quietly embedded in how work gets done. And soon, not having them will feel as limiting as not having access to the internet does today.
The future belongs to those who learn to command machines, not compete with them.
