Face Off with AI Agents – Humanity’s Question of Relevance

Humanity is staring down a new challenge: staying relevant in the age of agentic systems. For decades our basic workflow looked like this: we set a goal, power up a computer, connect to the internet, open software, search, analyse, decide, and finally act. We switched the machine on, launched programs, combed through websites, weighed options, and arrived at an outcome.

Then came large language models (LLMs). They read and write text nearly as well as we do. Soon they became multimodal able to interpret pictures, videos, and documents—so they could “see” and “listen” as well. They don’t truly reason in a human sense; they predict the next best token. Yet the results are good enough that, for many tasks, that distinction hardly matters.

Then Agents Arrived

Early LLM use felt like a Q&A: prompt in, answer out. Humans, however, usually work in flows. We discover a problem, research it, consult people, refer to documents, loop back, find tools, run programs, and read books. We pick our own path. The missing piece was an intelligent system that could plan and re‑plan the path just as we do.

Agents are that piece. An agent can juggle multiple subtasks, invoke the right tool at the right moment, and adjust the plan on the fly exactly the way a human would.

The Real‑Estate Analogy

Consider buying a house. You could hunt alone browse listings, call sellers, visit properties, negotiate, and finally close the deal. Or you could hire a real‑estate agent. The agent searches their database, scrapes the web, calls other agents, meets the seller, negotiates, and then hands you a ready‑to‑sign contract. You pay a commission and move in.

LLM Agents Work the Same Way

Give an LLM agent a goal, and it chooses the right tools search, a knowledge base, a custom script—autonomously. In a full agentic flow, multiple agents (each powered by an LLM and its own toolset) run in parallel or sequence until your objective is met.

Browser‑Use Agents

One headline capability is browser control. An agent can open a browser, type queries, click links, fill forms, book tickets, draft slides, or check email mirroring human actions step by step.

Where Do We Fit In?

If an agent can replicate the digital labor we perform, what remains for us? OpenAI’s public agentic workflows put immense power in many hands, widening both capability and economic divides. Fewer people may be needed to achieve the same output. A single person armed with agents could replicate what once took a company. We already see “one‑dollar‑person, million‑dollar companies” emerging; billion dollar and even speculative trillion‑dollar one‑person firms may follow.

Agents won’t just sit beside employees they could be employees. One day an agent might be the CEO. Such a reshuffle would overturn industries and labor markets as we know them.

History shows that new job categories appear whenever old ones vanish ice factories fell to refrigerators; horse‑drawn carts to motorcars; bull‑powered plows to tractors. In every shift, humans replaced humans with machines.

This time, humans will be replaced by humans with AI. The open question is: how many humans will still be part of the equation? Our task now is to identify the roles that remain uniquely, irreplaceably human and to master the agents who will handle the rest.