Humans Will Always Be the Foreman

Humans are not built to work on one task at a time.

We move between projects, responsibilities, and commitments throughout the day. We might be building a product, responding to a customer, planning a meeting, and thinking about something in our personal life at the same time.

That is why I do not think the future of AI will be one meta-agent that does "everything" for us.

The problem with “everything”

This is not mainly a problem with the capability of the meta-agent. Agents will keep getting smarter. That is our bet.

The problem is the definition of “everything”.

What does everything mean? Everything in my personal life? Everything involving customer relationships? Everything in engineering? Everything related to writing?

How would one agent work across five different parts of my life at the same time, especially when each part has different context, tools, priorities, and standards?

A human is not going to wait for an engineering task to finish before dealing with a personal commitment. We are not going to sit and stare at a loading screen while one agent completes a job.

We will start another job.

That is why we need multiple agents to work with.

The market is making different bets

You can already see several answers forming in the market.

Meta Muse and Instinct are betting on a personal assistant that a person can message and ask to handle many parts of life.

Town starts closer to email and calendar, where much of a busy person's work already happens.

Grok Bot is moving toward persistent bots with names, jobs, and context that compounds over time.

Each approach is useful. But they also expose the same unresolved question: does the future collapse into one universal agent, or does one person manage several persistent agents with different roles?

Our bet is on the second.

The human is the foreman

Right now, we already use multiple coding agents to ship a feature, fix a bug, and run tests at the same time.

While editing a video, we might have one agent working on the frames and positioning, another building animations, and another finding background music.

The human decides what each agent should do, checks the results, and moves between the different workstreams.

The human is the foreman.

As models become more capable, many of the agents we work with directly will get consolidated. The coding agents that currently handle separate parts of a feature may become one coding meta-agent. The agents that currently handle different parts of a video may become one video-editing meta-agent.

We will no longer need to coordinate every small step inside the coding project or the video project. The meta-agent will become the foreman of that particular job.

But what does the human do next?

The human starts another project.

I may have one meta-agent for coding and another for video editing. I may have another agent doing deep customer research, another planning a family member's birthday, and another helping with writing.

The individual work gets consolidated below me, but my own life still contains multiple goals that are happening at the same time.

The hierarchy keeps moving upward

This is the part of the meta-agent conversation that is frequently misunderstood.

People imagine that once one agent can coordinate ten other agents, the human will have nothing left to supervise. That assumes the human has one task.

Humans do not have one task. We have several projects, changing priorities, competing ambitions, and new ideas arriving every day.

This is how humans have always organized work. A company has individual contributors, team leads, managers, and executives. A military has soldiers, officers, and commanders. A government has teams, departments, ministers, and a leader.

Every person in each hierarchy is a foreman.

But a hierarchy is not merely a group of people stacked on top of one another. Everyone in it operates on top of the same base.

A military shares a mission, a command structure, and a set of plans. A company shares its goals, standards, and accumulated knowledge. This common foundation allows people at every layer to act without having every detail explained from scratch.

It also allows the person above them to manage outcomes instead of individual actions.

That is how humans move up layers of abstraction. As the people below become more capable and better aligned with the organization, the person above can step further away from the details.

Their responsibility does not disappear. Their scope expands.

Shared context holds the organization together

The same must be true for AI agents.

It is not enough to give one person ten powerful agents if every agent is an isolated system. Without shared memory, ten agents are just ten strangers.

Every new task begins with the human explaining who they are, what they are trying to accomplish, and what has already happened. The human does not become the foreman.

The human becomes the communication layer.

For the human to truly act as the foreman, every agent beneath them needs to operate on top of the same foundation.

The agents can still have different roles, personalities, and responsibilities, just as different teams inside a company do. I do not want a customer-research agent to approach a family commitment in exactly the same way, and I do not want a writing agent to sound like an engineering agent.

But they must understand the same person, remember the same decisions, and remain aligned around the same underlying context.

This is the idea behind Turnstone. One person works with multiple persistent Agents, all of which live on top of a shared Brain.

I can ask one Agent to work on the product while another helps me write and another researches a customer. They can work at the same time without each relationship beginning from zero. Every Agent already knows me and understands the context that came before it.

That shared Brain is what makes the Agents feel truly intelligent.

Intelligence is not only the ability to complete a difficult task. It is also the ability to understand why the task matters, how it connects to everything else, and how the person directing it prefers to work.

What the first users are showing us

We are already seeing early evidence of this behavior.

In the first two weeks after launching Turnstone, we have manually onboarded close to 100 users with many using Turnstone as their daily driver now.

The most engaged users were not people new to AI. They were already power users of Codex, Claude, and other agent tools.

Their problem was not access to intelligence.

Their problem was that their work, context, and automations had become fragmented across too many places.

Three observations stand out.

1. People switch for a workflow, not for intelligence

People do not switch because an agent is slightly smarter. They switch when it solves one recurring workflow well enough to overcome the friction of changing a habit.

The first wedge cannot be “move your entire working life here.” It has to be one job where the value is obvious.

2. Power users feel fragmentation first

The people who feel this problem most clearly are often power users.

They already know how to work with AI, but they are bottlenecked by scattered context, disconnected threads, and the maintenance required to hold everything together.

3. Multiple agents need a common foundation

Multiple agents only become useful when they share context and have clear roles.

Without that foundation, a multi-agent system quickly becomes another mess for the human to manage.

What cannot be delegated

Could one sufficiently capable agent eventually coordinate every other agent in a person's life?

Technically, perhaps. A single interface may eventually route work across dozens of specialized systems.

But it still has to decide whether a customer issue matters more than an engineering problem, whether a product launch matters more than a family commitment, and which new idea deserves attention.

These are choices about what the human is trying to do with their life. They should not be delegated to another singular entity.

What comes next

Three predictions matter for the second half of 2026.

1. Agent products will begin with one obvious workflow

The strongest products will start with one job where the value is immediately clear, then expand into the rest of a person's work.

People may eventually manage their entire working life through agents, but they will not adopt that future all at once.

2. Persistent context will matter more than marginal intelligence

Models will continue improving, and the gap between them will keep changing.

The durable advantage will come from understanding which task matters, why it matters, and how the person wants it done.

3. Agent products will become organizational

The interface will move beyond one chat window.

People will manage groups of agents with different responsibilities, all operating from shared context. The structure will look less like a chatbot and more like an organization.

For builders, this means the question is not only whether your agent can do more.

The harder question is whether a person can understand where it fits, trust it with a meaningful part of their life, and see value before the cost of changing their habits becomes too high.

The winning products will give people a clear first workflow, then let the rest of the organization form around it.

As models improve and the shared memory beneath them becomes richer, each Agent will manage more of the detail below it, and the layers of abstraction will continue moving upward.

Agents will get smarter. The jobs beneath us will get consolidated. The shared context will hold the organization together.

But humans will always be the foreman.