How to Build Your Own Agent Operating System
The presenter demonstrates how to build a custom 'Agent Operating System' (agent OS) that serves as a visual mission control for managing multiple AI agents, without requiring coding skills. Using Claude as a coding assistant, the presenter shows how open-source agents like Open Jarvis can be integrated into a unified dashboard in minutes rather than weeks. The system is designed to continuously evolve as new AI models and agents are released.
Summary
The video introduces the concept of an 'Agent Operating System' — a custom, locally-hosted visual dashboard (mission control) that consolidates multiple AI agents into one manageable interface. The presenter argues this is far superior to using a terminal or interacting with AI agents directly, as it provides a Kanban board layout, better organization, and a more enjoyable user experience. The system is described as non-technical and accessible to anyone willing to use Claude as a coding collaborator.
The presenter walks through a live example of integrating Open Jarvis, an open-source AI agent project with around 1,600 GitHub stars, into their existing agent OS. The process involves taking the GitHub repository information, feeding it to Claude, and asking Claude to build the integration. Claude then installs the CLI, sets up local models via Ollama, and fits the new agent into the existing mission control UI — all within approximately 6 minutes. The presenter emphasizes that Claude handles all the coding, including matching the visual branding and UI style of the existing system.
A key practical point is raised: AI agents don't always complete tasks perfectly, so the presenter stresses the importance of verifying results manually. In the demo, Claude claims Open Jarvis is visible in the mission control panel when it actually isn't, requiring a screenshot-based troubleshooting loop. The presenter also shows how changes can be rolled back easily if something goes wrong.
The presenter introduces the 'LOOP method' — a four-step repeatable framework: (1) Find a cool agent or model, (2) Ask Claude if and how it can be integrated, (3) Order Claude to build it, and (4) Verify it actually works. This loop is described as never-ending, since the agent OS continuously improves as new tools emerge. The system supports shared memory (via Obsidian), shared skills across agents, local model support, and VPS deployment.
The video concludes with a promotional segment for the 'AI Profit Bot Room' community, where members can download a pre-built version of the agent OS, access step-by-step tutorials, join weekly coaching calls, and connect with other members.
Key Insights
- The presenter argues that using Claude as a coding assistant can compress weeks of developer work into minutes, claiming the full Open Jarvis integration — including local model installation — was completed in approximately 6 minutes.
- The presenter warns that AI agents cannot be trusted to self-report completion accurately — in the demo, Claude claimed Open Jarvis was visible in the mission control panel when it was not, requiring manual verification and screenshot-based troubleshooting.
- The presenter claims the agent OS model is immune to AI obsolescence because the system architecture stays constant while new agents and models are simply plugged in, meaning users 'never fall behind' regardless of how fast the AI landscape moves.
- The presenter describes maintaining a pinned Claude conversation with full context of the agent OS project, allowing Claude to pick up exactly where previous sessions left off without re-explanation — functioning like a persistent team member.
- The presenter notes that individual agents within the system can share skills and memory across a common layer — for example, Hermes agent has 150 skills and Open Claude has thousands, and these can be shared when agents coexist in the same agent OS environment.
Topics
Transcript
[0:00] Today, I'm going to show you how to build your own agent operating system, like you see right here. So, we've plugged in all of our AI agents. And the cool thing about this like you don't need to be able to code. It's non-technical. Anyone can do this. I literally created this and it's evolved into this crazy machine with all our workflows for TO, video agents, or even for example, generating videos and text-to-speech and images using my favorite AI agents. And then I'm going to show you the process that I used at my startup or creating a whole system like this. So, you can take like any project or idea that you have and give…
Full transcript available for MurmurCast members
Sign Up to AccessMore from Julian Goldie SEO
NEW Nvidia Autonomous AI is WILD!! 🤯
Nvidia announced Nemo Clo, a new autonomous AI agent system that operates independently without continuous prompting. Powered by Nemotron 3 Ultra (a 550 billion parameter model), the system is five times faster and cheaper than previous versions, with OpenShell providing secure sandboxed execution.
Laguna XS 2.1: New FREE + Opensource Local AI!
Julian reviews Laguna XS 2.1, a new free open-source local AI coding model from Poolside that performs comparably to Qwen 3.6 and outperforms Claude Haiku on benchmarks. He demonstrates its practical capabilities by building landing pages and functional apps, highlighting its speed, offline functionality, and multiple deployment options through local setup, Claude Code, or OpenRouter's free API.
How to Run Hermes FREE Forever!
The video demonstrates how to run the Hermes AI agent for free using Gemma 4, a local open-source model from Google, with significant speed improvements through MLX optimization. The setup works on Apple Silicon Macs or via free APIs on Open Router, enabling autonomous agents to work offline and privately without subscription costs.
This NEW Chinese AI is INSANE! (FREE + Open Source!)
Long Cap 2.0 is a new open-source Chinese AI model from a food delivery app company that offers 1 million tokens of free context memory, beats GPT-4.5 on SWE bench pro benchmarks, and uses efficient parameter activation to reduce computational overhead while maintaining high performance.
Claude Code is now FREE: Here’s how…
Google's new Gemma 4 model running on Ollama is 90% faster on Apple Silicon, enabling free Claude Code usage locally without token costs. The setup requires three simple steps: downloading Ollama, Gemma 4, and installing into Claude Code, with alternatives available via OpenRouter API for non-Mac users.