The First Lesson of Agent OS: Only Through Division of Labor Can You Use Agents

Agent OS is not about building more agents, nor about letting a single omnipotent AI take over everything. It’s more like a personal work control layer: starting from a real goal, breaking tasks into workstations, assigning context and acceptance criteria to each station, having a verifier independent from the producer, pausing high-risk actions at “pending confirmation,” and finally consolidating experience into reusable assets.

Let me start with a judgment.

Current AI tools — Codex, Claude, ChatGPT — won’t remain stuck in a chat box or terminal where you ask questions one by one forever. That’s just a transitional form. Their real endgame is to become an Agent OS: an operating system where you can assign work, let a team of agents divide tasks, and have the results come back to you for review and consolidation.

This isn’t a distant vision. I’m already using one myself, managing my family of agents and over seventy Skills.

But today I won’t just talk abstractly about this concept. I want to use a small problem you probably encounter every day to show you the most fundamental principle of this system.

It comes down to two words: division of labor.

Here’s that small problem: Is your browser cluttered with dozens of tabs, with hundreds of articles sitting in your “read it later” list? You saved them thinking “I’ll definitely get back to them,” but you never did.

I used to be the same. But later, using a division of labor approach, I turned that backlog into a stack of truly usable notes in one afternoon. The key wasn’t how powerful the model was — it was that I didn’t dump the whole task on one AI to do from start to finish.

I broke it into several workstations, each assigned an agent: who does the fetching, who does the extraction, who does the verification, and who makes the final call.

This is the first lesson in building your own Agent OS, and the only thing this article aims to make clear.

First Arrange Workstations, Then Assign Agents

Most people get the order wrong.

They start by asking: Which AI is the strongest? Which model is the smartest? Which agent is the most versatile? Then they dump the entire task on it.

You be the researcher, you be the writer, you be the fact-checker, you be the publishing assistant, and you also manage the knowledge base. And now, please do this whole thing from start to finish.

This sounds efficient, but in practice it’s dangerous.

Because you’re mixing three things together:

• Contexts mixed together
• Responsibilities mixed together
• Risks mixed together

If the agent writing notes also handles verification, it will naturally smooth over its own mistakes. If the agent doing extraction also handles deletion, it will directly act on a plausible-sounding wrong judgment. If the agent generating content also has the ability to auto-publish, one mistake turns a draft into public content.

This isn’t because AI is dumb.

It’s because the organizational structure is wrong.

My current habit is the reverse: Don’t act first; arrange workstations first.

Take the “digesting a bunch of tabs” task as an example. I break it into four steps:

First, the fetch workstation. Read in the batch of saved webpages along with their full text, record titles, URLs, and text status, then do a rough classification: which ones are really worth learning from, which have writing styles worth referencing, which could supplement current topic ideas, and which can be archived directly.

Second, the extract workstation. Distill each page’s body into several key points, telling me whether this page is worth keeping.

Third, the verification workstation. Cross-reference the extracted notes against the original text item by item, checking whether the AI invented anything — especially numbers, conclusions, and judgments.

Fourth, the confirmation workstation. Compile the results from the first three workstations into a to-do list, with a suggestion for each item: delete, archive, keep, or read in depth. But it only gives suggestions — the final call is mine, one by one.

As you can see, there’s no magic here. The core is one to fetch, one to extract, one to check, and finally a summary presented to a human for confirmation.

But once you break it into these four workstations, the nature of the whole system changes.

Division of labor isn’t to make things look sophisticated; it’s to isolate contexts, responsibilities, and risks.

The extractor cannot verify itself. The verifier cannot delete things on your behalf. The confirmer only makes proposals, not automatic actions.

This is the flavor of Agent OS.

How to Define a Single Workstation

I prefer the term “workstation” instead of jumping straight to agent, runtime, or orchestration.

Because “workstation” inherently carries a responsibility boundary.

A workstation is not a model. A workstation is a set of clear agreements:

• What role does it play?
• What task is it responsible for?
• What context does it need?
• How is it deemed qualified?
• What is it not allowed to do?
• What output does it pass to the next workstation?

Once you write these things clearly, a workstation is established. Whether the backend is a ChatGPT dialog, a Claude Code subagent, a Codex agent, or a future truly systemic Agent OS — that’s just the execution backend.

Let me give you the most crucial example: the verification workstation.

I define it like this:

Role: You are a note verifier. Responsibility: Compare every note, every number, every conclusion written by the extraction workstation against the original text, item by item. Context: I give you two things: the notes to be verified and the actual original text of each webpage. You cannot judge based on your own memory — only based on the original text I provide. Acceptance Criteria: For items that match the original text, mark “Verified + source location.” For items that don’t match, or are simply not found in the original text, mark “Suspicious / no basis in original text.” Finally, give me a table.

The difficulty is never the grammar — it’s thinking through these things clearly.

There’s also an iron rule for the verification workstation: The workstation that wrote the notes cannot verify itself.

If it verifies itself, the blind spots are the same. It can’t see what it hallucinated a moment ago — it’s like letting someone grade their own exam. So the verifier must be a different agent, with its own independent context, unable to see how the extraction step filled in gaps, making it impossible to cover for itself.

And it must be given the real original text.

You’ll see how this saved me later.

Using a Stack of Browser Tabs as an Example

To avoid exposing my real tab library, I used seven neutral, publicly available web pages for the demonstration: a few tech blog posts, a YouTube talk, and a Twitter thread. The volume is smaller than what I actually process, but the division of labor and judgment logic are identical.

First: fetching.

There’s a nuance here: I don’t let the AI fetch the webpage text on the fly.

If you ask it to fetch a webpage directly, nine times out of ten it will be incomplete, and often it fails outright. For this kind of dirty, unreliable task, I use mature tools — like Readwise Reader, Obsidian web clipper, or even manual copy-paste.

When I browse and find something worth reading, I just save it to my read-later list. The messy task of fetching the full text is already handled cleanly by the read-later tool. What the fetch workstation needs to do is not to fetch the webpage, but to read in the title, URL, text status, and initial classification from the already saved content.

This is itself a layer of division of labor: hand the fetching task to the most reliable tools, and let the AI do only what it’s good at — extraction and judgment.

Among these seven samples, six had their full text saved. One was a YouTube talk with only a title and no text. Notice how the fetch workstation handled it: it didn’t force a classification — it honestly marked it as “No full text.”

That’s a rule I deliberately set: if there’s no text, say so. Don’t make it up from the title.

But there’s a more subtle situation: some pages have complete text, but their title and summary are way more aggressive than the body. If the next extraction workstation gets lazy and only looks at titles and summaries without reading the body, it will still go wrong.

So for the second step, I deliberately created a counterexample.

I simulated a lazy approach that many people take: feed only the title and summary of each page to the extraction workstation, without giving it the full text.

Most pages passed. But one page immediately caused a problem.

It was an annual report from the database industry, with a title like “Adoption Rate Hits New High.” The extraction workstation, only looking at the title and summary without reading the body, wrote with great confidence: “Adoption rate exploded, doubling year-over-year.”

It also casually downgraded this page.

The fetch workstation had initially classified it as “Learn,” but the extraction workstation, without even glancing at the body, reclassified it as “Archive,” meaning there’s nothing new here — just move along.

Make a mental note of these two: “doubling year-over-year” and “archive.”

They read smoothly and sound credible, but both are pure hallucinations based on the title and summary. The original text didn’t mention any doubling, and this page was far from an old piece that could be archived.

If I had taken the easy route and let this division of labor pipeline run unchecked to the end, what would have happened?

This page — the only one in the batch containing real industry data — would have been treated as useless old news and archived, never to be revisited. Meanwhile, I would have planted a completely false “doubling” impression in my mind, potentially using it as a data point when writing, making judgments, or teaching.

A real piece of knowledge discarded, a fake number internalized as truth — and I would never have found out.

What saved me were the next two workstations.

The Real Lifesaver: Confirmation Boundaries

The third workstation: verification.

I handed the extracted notes, along with the original text of each webpage, to the verification workstation. Its job was exactly what I defined earlier: compare every key point, every number, every conclusion against the original text, item by item.

The result was straightforward.

The statement “adoption rate doubled year-over-year, explosive growth” was flagged as “suspicious.” The reason was also clear: there was no mention of “doubling” in the original text. The original said “steady growth, about 8%.”

The extraction workstation’s claim was fabricated from the title and summary.

For judgments that directly determine whether I keep or delete a page of material, I also run a second verification with a different “pair of eyes.” I ran the same verification workstation definition inside Claude again.

Note: I didn’t write the verifier from scratch in each tool. They share the same definition — if I change one place, both update. So running the check in a different tool uses the same setup without reconfiguration.

Both independent runs flagged “doubling” as suspicious: the original text said steady growth, not doubling, not explosive growth.

This is the value of having two different AIs verify independently: each looks at it fresh, they even present disagreements to me, and I make the final call.

But the real key today isn’t catching one fake number.

The real key is the fourth workstation: confirmation.

I didn’t have the confirmation workstation verify again — that was already done. I gave it a different task: compile the results of the first three workstations into a to-do list.

For each item, it gives me a suggestion: delete, archive, keep, or read in depth. But all suggestions stay in the “pending confirmation” column.

It did reverse the report page from “archive” back to “read in depth,” and it specifically singled out the fake “doubling” for my cross-check. But it didn’t take any action on its own.

Whether to execute — I click confirm one item at a time.

This is the single most important point I want you to remember today.

When AI organizes things, it can confidently make wrong judgments. It will mark resources you need as disposable, and it will invent conclusions that don’t exist in the original text. If you trust it and let it execute automatically, your knowledge base gets silently corrupted, and you won’t even know what changed.

So this final step must be a human decision.

This layer is the true confirmation gate of your Agent OS. No matter how capable the front workstations are, they only complete the work and lay out suggestions; pressing the confirm button is entirely your choice.

The most important thing about Agent OS is not automation, but controllable automation.

The Industry Is Moving in This Direction Too

This isn’t just my personal preference.

Different companies use different terminology. OpenAI talks about Agents SDK, Anthropic about effective agents, Microsoft about Agent Framework, Google about ADK, AWS about AgentCore. Names differ, but if you look under the hood, they’re all filling in the same set of underlying capabilities: giving agents clear roles, context, state, tool permissions, approval mechanisms, and observability.

More simply: just giving a model a prompt is no longer enough. You need to give it an operational structure.

When should you split into specialists? Not because multi-agent looks advanced, but because different roles require different instructions, context, tool permissions, and acceptance criteria.

When must you have human confirmation? Not because we don’t trust AI, but because actions like deletion, archiving, publishing, sending emails, and changing long-term memory alter the real world or long-term assets. They cannot be executed automatically based solely on a smooth-sounding response.

That’s why I’m skeptical of the idea that “just give an agent a bunch of tools and it will take over your work.”

Taking over is not a capability — it’s a risk.

What you really need is a clear work structure: goal entry, workstation division, context isolation, permission boundaries, execution logs, human confirmation, and memory consolidation.

This is not an outdated approach. Quite the opposite — it’s the direction agent usage must take to become practical.

How an Individual Can Start Building Their Own Agent OS

If you want to build your own Agent OS now, don’t start by building a big system.

First, find a repetitive, real task.

For example: organizing tabs, writing newsletters, digesting Reader feeds, creating video scripts, processing meeting notes, compiling competitive intelligence. Pick one task you face every week, find annoying, but whose results matter.

Then follow six steps.

Step one: write down the goal clearly. Not “help me organize materials.” Write: “Turn these 20 saved webpages into a pending confirmation proposal, and finally I’ll decide which to read deeply, which to archive, and which to delete.”

Step two: map out workstations. Don’t ask which tool to use first. Ask how this task naturally breaks into segments. Input, extraction, verification, confirmation — usually the most basic four.

Step three: for each workstation, write down four things. Role, task, context, acceptance criteria. If these four things aren’t clear, you haven’t truly defined that workstation.

Step four: add forbidden actions. The fetch workstation cannot hallucinate body text. The extraction workstation cannot treat titles as body text. The verification workstation cannot make excuses for notes. The confirmation workstation cannot auto-delete.

Many people only write “what you should do,” rarely “what you should not do.” But in agent workflows, what you must not do is often more important.

Step five: define handoff artifacts. The fetch workstation outputs a fetch list. The extraction workstation outputs extraction notes. The verification workstation outputs a verification table. The confirmation workstation outputs a pending confirmation proposal.

Once every step has a file or table, the system becomes recoverable, auditable, and replaceable. Use ChatGPT today, Claude tomorrow, Codex the day after — the workflow remains.

Step six: add confirmation boundaries. All deletion, archiving, publishing, email sending, knowledge base changes, and long-term memory modifications stay at “pending confirmation.”

You can let AI give suggestions. Don’t let it silently execute on your behalf.

This is the dividing line between “using AI” and “building a system.”

You Don’t Have to Start with Claude Code

In my video this time, I’ll demonstrate Codex and Claude Code because these are my tools of choice. But they’re not a prerequisite.

If you have nothing installed, just using ChatGPT, you can open four dialogs:

• Dialog one: Fetch workstation
• Dialog two: Extract workstation
• Dialog three: Verify workstation
• Dialog four: Confirm workstation

Each dialog does its own thing. Don’t mix them. Don’t let it verify itself. Don’t let it auto-execute high-risk actions.

Once you get it working, solidify these prompts into Claude/Codex agents or skills. Later, consider a real control plane.

This path seems slow, but it’s actually faster.

Because you’re not stacking tools — you’re training your system design ability.

Models Are Rented; Context Is Your Own

After running through the four workstations, did you notice who made all the decisions from start to finish? You.

Which pages to keep, which to archive, whether to believe the fake note — all up to you. The AI divided labor and did the work, but the person pressing the confirm button never changed.

Today I used Claude Code and Codex. But at the end of the day, they are tools, rented. A stronger one comes out next month, and I’ll switch anytime.

But one thing won’t be replaced — my own.

Models are rented; context is my own.

And this context isn’t just the data you’ve stored. It includes how you divide labor, how you feed input, how you verify, and how you decide what can be executed.

Tools keep changing; this framework stays the same.

So stop agonizing over which AI is strongest. Don’t think that building one agent is the end of the road. The strongest thing is never a model or a single agent — it’s your own arrangement.

You don’t have to know how to build an agent — that’s not the barrier. First, open four dialogs, clarify these four roles, have them supervise each other, and finally press the confirm button yourself.

Starting with the tabs you have today, you are the operator of your own Agent OS.

I’ve Prepared the Workstation Pack Too

The companion tool pack for this video is available here:

→ Get the “AI Workstation Prompt Pack: Tab Digestion Edition”

Backup link: https://img.axtonliu.com/downloads/2026/06/Claude-AI-Workstation-Prompt-Pack-Tag-Digestion-v1.0-ascii.zip

Inside, there’s no magical automation script — it’s a set of ready-to-copy workstation definitions:

• Fetch workstation prompt
• Extract workstation prompt
• Verify workstation prompt
• Confirm workstation prompt
• Division of labor decision table
• Confirmation boundary checklist
• Claude/Codex cross-tool shared setup for advanced users

Light users just need to open four ChatGPT dialogs to run it. Advanced users can turn it into their own agents and skills.

You don’t need to have a full Agent OS before you start working like you have one.

You can start with one workstation.

Take one repetitive task and break it apart. Write clear context and acceptance criteria for each step. Let an AI do the work, let another AI check it, and let the final step stop in front of you.

Pick a task you’re struggling with today and try a division of labor experiment right now.

References

• Anthropic: Building Effective Agents
• Anthropic: Effective Context Engineering for AI Agents
• OpenAI Agents SDK: Orchestration
• OpenAI Agents SDK: Guardrails and Approvals
• LangChain: Multi-agent systems
• Microsoft Agent Framework Overview
• Google Agent Development Kit
• Model Context Protocol Specification
• OWASP AI Agent Security Cheat Sheet