01What Is an Agent OS and How Does It Work?

An agent OS is a system that manages your AI agents the same way a computer operating system manages apps. It gives your agents shared memory, a common workspace, and a way to talk to each other. Instead of switching between ChatGPT, Claude, and a terminal, you have one dashboard where everything works together.

Think about how your laptop works. The OS handles resources and lets apps share data. An agent OS does the same for your AI tools.

Every agent pulls from the same memory, saves outputs to the same place, and gets smarter over time. This turns a pile of tools into a real system that runs without you.

02The 5 Mistakes That Waste Your Time

Before the blueprint, here are the mistakes Julian made. Avoid them to save hours of trial and error.

  • Treating AI as a tool, not a system – Using each agent separately makes nothing fit together. The breakthrough is designing a system around your models.
  • Paying before checking free options – Most free tools like Hermes and Obsidian can do the job. Prove the workflow first, then pay if needed.
  • Relying on built-in chat memory – In-app memory is too vague. You need an external memory layer every agent can pull from.
  • Building automations in NAN – NAN works but breaks easily. An agent OS runs those workflows with no code.
  • Letting outputs land in random folders – If you can't find your work, you can't build on it. Every output needs a home.

These mistakes made Julian waste 20% of his time re-explaining context.

03Layer 1: Foundation and Layer 2: Memory

Layer 1 is your hardware. A modern laptop works. Everything runs locally, so no cloud subscription is needed.

Layer 2 is the memory layer. Most people skip this, but it changes everything. Julian uses Obsidian with OMI.

Obsidian stores everything as plain markdown files on your machine. It has no proprietary formats, so no vendor lock-in. Since early 2026, Obsidian has an official CLI with over 100 commands, so your agents can control your notes from the terminal.

OMI records your conversations and screen context, then syncs to Obsidian. Every agent pulls this context before responding.

For example, when Julian asks Hermes for keywords for the AI Profit Boardroom, it reads the vault and knows the audience, niche, and past content. The output is instantly useful because it starts with full context.

04Layers 3-5: Brain, Agents, Command Center

Layer 3 is the brain – the models you route tasks to. Models change often, so what matters is the routing layer. Start free on OpenRouter.

When a better model comes out, you swap it in without rebuilding anything.

Layer 4 is the agents. These wrap the models and give them tools and memory. Julian uses Hermes for long autonomous tasks, Claude Code for code, and OpenClaw for images and voice.

You don't need all agents at once. Start with Hermes and add others as needed.

Layer 5 is the command center. It is a single interface where every agent appears in the same sidebar. Without it, you have a collection of tools.

With it, you have an OS. Julian builds it with Next.js, but you can make your own or grab his setup.

05Layers 6-7: Production and the Loop

Layer 6 is production. This is where the real work gets done. Julian's system has goals mode, SEO content, a studio for images and video, a notebook for research, and a workspace showing everything he has built.

When he needs SEO content, he goes straight to that section. It is already set up and contextualized, so he doesn't have to explain anything again.

Layer 7 is the loop. This is the most skipped layer, but it makes the whole system compound. Every output your agents create gets written back into the memory layer as structured notes.

Each new chat starts smarter than the last. Without the loop, your system is the same on day 100 as day one. With it, you get compounding value over time.

06How to Build Your Own Agent OS Step by Step

Follow these steps to build your own agent OS this weekend. Start simple and expand as you go.

  1. Get Obsidian set up – Create a vault on your machine. Use the CLI so agents can read and write notes.
  2. Set up OMI – Let it capture your conversations and screen, then sync to Obsidian.
  3. Install Hermes – Run it locally. It will build persistent memory and write reusable skill documents.
  4. Get an OpenRouter key – Use it to route to models for free. Test which models fit your tasks.
  5. Build a simple command center – Use Next.js to create a dashboard where agents appear. Or start with Hermes' interface and add more later.
  6. Automate the loop – Set up a script that saves every agent output to Obsidian as a markdown note with date and type.

07Results and Comparisons from the Episode

Julian says he spent 100 hours of trial and error and had nine complete failures before this system worked. He found five lessons the hard way. One major result: he stopped re-explaining context, which used to take 20% of his time.

Now he runs content production for the AI Profit Boardroom end to end without manual work.

He compares the old way to the new way. Old: switching between Claude, keyword tools, Google Docs, and publishing platforms. None of them talked to each other.

New: one dashboard where every agent lives together, and every output flows back to memory.

He also compares free vs paid tools. He paid for memory tools and dashboards before testing free stacks. Hermes is free and MIT licensed.

Obsidian is free. OpenRouter starts free. The rule: prove the workflow first, then upgrade only when the free option can't keep up.

08Tips, Limits, and Mistakes to Avoid

Here are practical tips from the episode. They will save you time and frustration.

  • Don't build all seven layers at once – Start with memory and agents. Setting up Obsidian and Hermes alone will change your workflow within a week.
  • Don't skip the loop – It feels like extra work, but it is not. Set agents to write notes back to the vault from day one.
  • Build for the architecture, not the tool – Models change and agents get replaced. The seven-layer structure stays. Build it now and every new tool plugs in.
  • Watch for brittle automations – NAN breaks when things update. An agent OS uses a shared workspace so agents talk to each other.
  • Keep your outputs organized – Give every artifact a home that auto-collects by type and is previewable.

09Who This Is For and Why It Matters

This system is for anyone who uses AI daily and feels slowed down by switching tools. If you are a content creator, marketer, or solo builder, an agent OS helps you do real work without babysitting every step. The people who get 10 times more done are not using better tools.

They are using a better operating system around the tools.

Building your own agent OS matters because it makes your work compound. Every task your agents finish becomes part of your memory, so the next task is faster and smarter. You stop starting from zero.

You build a system that runs and improves even when you are not in it. That is leverage, not friction.