From Knowledge to Allocation: How AI Agents Are Reshaping the Future of Work Time doesn't move in a straight line. It loops, folds, and echoes—sometimes so subtly that we don't notice the future whispering in the present. But if you know where to look, you can catch these glimpses, trace their patterns, and see what's coming next.

Time doesn't move in a straight line. It loops, folds, and echoes—sometimes so subtly that we don't notice the future whispering in the present. But if you know where to look, you can catch these glimpses, trace their patterns, and see what's coming next.

I believe I have caught a revealing moment, and I want to share it with you.

Our working environment is dramatically evolving with every passing day. AI, especially Large Language Models (LLMs), Agentic AI, and intelligent automation technology, is revolutionizing the future of work. Humans are leaving behind the age of knowledge and skills and stepping into a new era where the ability to allocate tasks to autonomous systems is taking the lead. We should recognize this development not merely as a modern technological advancement but as a fundamental shift in how human labor is structured and how productivity is measured in this modern AI era.

The Knowledge Economy: A Legacy & Contemporary Model

For decades, we have been living in a knowledge economy. If you knew the right information, possessed specialized skills, and could use them effectively, you thrived. The rise of personal computing and the internet made access to knowledge the ultimate asset. Being a maker or creator—someone who wrote creatively, programmed, analyzed data, or crafted go-to-market strategies—was the golden ticket to career success.

But what happens when machines get better and better at performing these specialized knowledge tasks and almost reach parity with human intelligence?

AI is accelerating beyond being a passive listener—it is now becoming an active collaborator. It synthesizes, translates, and summarizes knowledge at speeds and scales that were unimaginable just a few years ago. The specialized skills that once made workers indispensable—technical skills, creative ideation, and strategic brainstorming—can now be handled by AI.

And so, the value of human labor is shifting.

The Allocation Economy: Where Work Is Going

Managers in corporations have always done this to some degree—overseeing teams and directing strategy. But now, even junior employees must learn to think like managers—not of people but of AI systems and models. The skills that once defined leadership—strategic vision, autonomous decision-making, clear communication, and the ability to handle tasks concurrently—are becoming necessary for everyone.

Agentic AI is not replacing work; it is merely an abstraction layer over work. The people who can best leverage it will thrive. Those who resist it or fail to understand how to integrate it may struggle.

The Role of Agentic AI in the Allocation Economy

Agentic AI is the driving force behind the emergence of the allocation economy. AI agents can actively participate in planning and creating solutions through autonomous execution without requiring step-by-step human instructions. Eventually, companies will be able to "hire" them as full-fledged workers. These systems exhibit employee-grade capabilities without experiencing fatigue, losing focus, or requiring motivation.

The key advantage of AI agents is that they do not need constant supervision—they can carry out tasks in the background while you focus on other things.

I recently had the opportunity to explore one such AI agent: Perplexity AI. It's an AI search tool that functions as an application using LLMs as its fundamental base layer. Here's how Perplexity could help:

Use Case: E-Commerce Market Research

Suppose you have a business selling eco-friendly home products online. Your team requires research on what consumers want, what your rivals charge, and the possible options for eco-friendly packaging. Instead of spending hours searching through multiple sources, Perplexity can:

1. Aggregate & Summarize Reports

You pose the question, "What are the latest consumer trends in eco-friendly home products?"

Perplexity will look into trustworthy sources, scan for relevant information, and give you an answer that is straight to the point but has everything you need to know.

2. Compare Competitor Pricing & Strategies

You type in, "How does our pricing for reusable kitchen towels compare to top competitors?"

Perplexity looks at the available public pricing information and provides a summary of what you need, enabling you to set better pricing for your products.

3. Identify Appropriate Suppliers & Materials

Instead of looking up everything yourself, you ask, "Where can I find cost-effective, sustainable packaging suppliers?"

Perplexity shows selected sources, lists them, and gives advice without much effort on your part.

4. Use Different LLMs for Tailored Approaches

You can select from various LLMs. For example, OpenAI's GPT works well for general overviews, whereas DeepSeek is preferred for in-depth searches.

Seeing how skill-intensive, specialized, and advanced tasks are performed by Perplexity is fascinating.

The modern Agentic AI era is altering the work culture as we know it. Employees will transform from workers to supervisors of AI agents who will do the heavy lifting. There will also be a rise in the number of generalists who can use AI to perform a multitude of tasks instead of highly skilled specialists who focus on a single specialized task.

From Maker to AI Agent Manager

To succeed in the allocation economy, the following key skills will be essential for AI agent managers.

• Strategic Insight

Managers should possess the ability to articulate difficult tasks clearly and efficiently. Similarly, the agentic managers who would manage the models with efficiency will achieve better results. The success of the AI managers relies upon their ability to create effective prompts, refine the outputs, and direct the AI agents to deliver high-quality output. Clear and meaningful instruction generates better results. It's also very essential to communicate the overall strategic vision as well as the specific task so AI agents would understand what the end goal would look like.

• Evaluating the intelligence of AI Agents

The great managers are equipped with the ability to pick the best resources to get the task done. In the similar way, Agentic AI managers should be able to choose the most suitable AI agent for their specific needs. Eventually, There will be millions of vertical AI agents available to accomplish specific tasks so the ability to know which AI agent would work out of the pool of AI agents will take more importance.

• Knowing when to delve into details

Good AI managers know when they have to delve into the details and when they have to leave the work to AI agents. Putting efforts in the wrong place may lead to a waste of effort. Once they have done enough due diligence and selected a specific agent, they should trust that the agent will get the job done. The managers who attain the required equilibrium between AI usage and human intervention will achieve success.

• Distinct Taste

Now-a-days knowledge is not just limited to the creation of content, coding, or analyzing the data. The evolving AI technology has the ability to perform all of these tasks but the AI agent managers should be capable of curating effective prompts so that the task is clear to the agent and the refine the outputs to meet the end goal.

The Road Ahead

The renowned economist Tyler Cowen stated:

"If you and your skills are a complement to the computer, your wage and labor market prospects are likely to be cheery. If your skills do not complement the computer, you may want to address that mismatch."

He originally said this about early automation, but his statement holds relevance in this modern AI era as well.

The allocation economy will widen the gap between those who can understand and integrate agentic AI systems in their workforce and those who cannot. This revolutionary change presents an opportunity, and the workforce should embrace it. By utilizing AI agents, humans can extend their potential and work at an unprecedented pace of problem-solving. It's going to be a net positive change in society.

The question that arises here is: "Are we prepared to become good managers who can manage AI agents?"