Using an AI Agent as a Middle Manager

Why a Solo Worker Might Need Three Layers

While talking with a professor who had started using AI agents through Claude Cowork, I began to consider how I use them myself. I realized that I use an AI agent as a “middle manager.”

Working alone means continuously switching among the roles of planner, manager, and executor. I used to consider it normal to alternate between “the person drawing the big picture” and “the person organizing spreadsheets.” But the switching itself consumes a substantial amount of cognitive energy.

A structure has gradually emerged in my work: I act as the senior manager, setting the overall direction and making key decisions. The AI agent acts as the middle manager, organizing the current status, managing TODOs, documenting work, and managing files. At the working level, the AI and I divide the actual execution between us.

A three-layer organization may sound excessive for one person, but in practice it feels quite natural.

What the Middle Manager Does

The essential role of a middle manager in an organization is translation: turning abstract direction from above into concrete, executable tasks below, while consolidating scattered progress updates from below into a form that can be reported upward and recorded.

As a middle manager, an AI agent can identify and organize the status of each project; propose specific tasks for the day; document work results and organize them into subfolders; and systematically track what the human worker and the AI worker need to do and have done.

The key is that I can simply say, “Tell me the current status,” or “Recommend what I should do today,” and receive an answer immediately.

The Problems This Actually Solves

On the surface, this is about the convenience of understanding project status and deciding what to do next. In reality, it addresses more fundamental problems.

The first is the cost of restoring context. When working on several projects in parallel, considerable time and energy are spent remembering where each project left off whenever I return to it. In my account of using Antigravity, I emphasized the importance of interim reports. If a middle-manager AI keeps those reports updated automatically, the cost of restoring context can approach zero.

The second is decision fatigue. The number of decisions a person can make in a day is finite. If energy is spent on the meta-level decision of “What should I do?”, less remains for important substantive decisions. When the AI presents a structured TODO list, it reduces the cost of these meta-decisions.

The third is the inertia involved in starting work. Starting is always the hardest part. It can be impossible to begin from a vague state, but if there is a concrete list saying, “These are the three things to do today,” I can simply open the first item. This reduces friction around the two major hurdles of deciding what to do and getting started.

I Exist at Two Levels at Once

A distinctive feature of this model is that I am both the senior manager and a frontline worker. As the senior manager, I set direction. As a frontline worker, I read papers, write documents, and log into websites to do the work directly. I delegate only the burden of management in between to the AI.

My current division of labor between myself and the AI is straightforward.

• I handle work that requires judgment: identifying the core of a paper, making strategic decisions, and reviewing quality.
• The AI handles work that requires transformation: changing formats, summarizing, drafting, and organizing data.
• I handle work that requires access permissions: logging into specific systems and operating internal institutional systems.
• This division is not fixed. As tools develop, it continues to change. As tools such as Cowork gain file-system and web access, the range of work that can be delegated to AI is expanding.

The Hidden Value of Documentation

The documentation and file management performed by a middle manager mean more than simple organization. Documentation often does not happen when working alone because its beneficiary is “future me.” Present me bears the cost without receiving an immediate reward.

When AI bears that cost instead, future me always encounters a well-organized project. In effect, this gives individual work institutional memory. Organizations can continue operating even when people change because they have documented processes and records. A person working alone previously could not really have that. An AI middle manager helps close the gap.

I also felt this in my experience with Antigravity: if one only diverges and does not converge, no orderly context is created for an agent to use, making large-scale research difficult. The middle-manager model structurally ensures this process of convergence.

Points of Caution

It is important to distinguish efficiency from effectiveness. Overreliance on a middle manager can cause a loss of direction. If I follow AI-recommended TODOs uncritically, I may miss the senior-management question: “Is this project heading in the right direction?” A middle manager can maximize efficiency—doing things correctly—but effectiveness—doing the right things—remains the human’s responsibility.

In an earlier post, I wrote that it is the human’s role to restrain an agent’s instinct to keep moving forward. The same applies in the middle-manager model. When the manager says, “This is the next task,” the senior manager must still be able to ask, “Is that actually what we should be doing?”

There is also tool dependency. If work status depends entirely on a middle-manager AI, system outages or platform changes create vulnerability. It is safer to retain the context for key decisions separately, in one’s own mind or personal notes.

What Ultimately Changes

What this model changes is the allocation of cognitive resources. It does not provide an entirely new ability. Instead, it lets cognitive resources previously wasted on management work be concentrated on setting direction and carrying out work that requires judgment.

I think what we need now is more experience in learning how to integrate new tools into existing work and how to change established ways of working. The middle-manager model is one answer I found during that learning process. Different people will probably find different approaches that suit them.