Simon Scrapes — Creating Your Own Agentic OS is Easy (Insanely Powerful)
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Ever wondered why AI tools like Claude Code give you generic output and forget context? It’s not about better prompts; it’s about the system underneath. In this video, I break down exactly how to build your own Agentic Operating System step-by-step. You’ll learn how to give your AI a memory, teach it repeatable skills, and create a structure that delivers consistent, high-quality results 90% of the time.
00:00 - The Problem & The Goal 03:10 - Part 1: Static context (you & your business) 06:56 - Part 2: Improved memory 11:10 - Part 3: Creating repeatable processes 14:06 - Part 4: Multi-step workflows on a schedule 16:14 - Part 5: Planning that matches the project 18:43 - Part 6: Managing projects & clients 20:44 - Part 7: Output consolidation 22:19 - Part 8: Access it anywhere
Key Insights
So, some people use tools like Claude code, and it looks a little bit like this. It’s forgetting context. You’ve got generic outputs, and you’re wasting time, quite frankly. [music] Whilst other people use the exact same tools, and it feels a little more like this. They’re shipping faster, they’re getting better results, and they’re actually saving time. So, the funny thing is, they’re both using the same tools, the same models underneath, but have completely different outcomes. And it’s not because they’re better at prompting, it’s because one group built something underneath the tool, and the other group didn’t. And that is an agentic operating system. So, it’s a system that tells the AI who you are, what you’ve done, what matters to you, how you [music] work, and how to execute on complex briefs. And with the right system in place, you too can get these consistent high-quality outputs 90% of the time that you use it. So, in this video, I’m going to break down exactly how to build your own agentic OS step-by-step. So, whatever AI tool you’re using right now, it will actually work the way you expect it to. So, let’s get in to the goal of the system. So, we effectively want to build with the agentic OS something that overcomes the limitations of LLMs out the box. So, we want it to know who you are and how you work, and that extends all the way to actually your business, your clients, what projects you’re working on. And we want that to be understood and injected at the right time in granular detail. We want it to be able to remember exactly what we worked on last week, last month, recall those decisions, recall the sessions, and actually inject that context without us having to re-prompt it. We want to overcome the limitations that our LLMs are generalists, and make them specialists in our processes, make those processes repeatable with consistent high-quality outputs. And of course, the dream is to go away from the laptop and have these operate as multi-step workflows that execute autonomously without your supervision on a schedule. We want to be able to plan for different types of projects. So, if we’re building out a complex SAS, we want to make sure that the planning is in granular detail to match that project. Many of you will be working across multiple clients, multiple businesses, multiple projects. So, you want to ensure that architecture maintains that clean separation between clients. One of the most painful things I’ve seen is outputs being put everywhere in different file structures, etc. So, we’re going to show you how to put those in predictable places, and of course, access it from anywhere. So, you don’t need to be sat at your laptop to actually run these systems. You’ve got the power of the system, but the ability to actually access it from anywhere in the world. So, each one of these is actually a limitation of the LLMs and the tools we’re using out of the box. And each one is a section inside this video. So, tick all of these nine off, and you’ve got an agentic operating system that’s going to produce you high-quality outputs on a consistent basis. So, we’ll walk you through one at a time, and as we go, we’ll build up a full architecture diagram. So, by the end of the video, you’ve got a complete picture that you can take away, and you’ll know exactly where to start when building out your own agentic OS. And the simplest way to think about all of this is that an agentic OS is just clever context management. So, it’s all about folders, files, any structure that tells your AI tool exactly what it needs to know, exactly when it needs to know it. And by the way, none of this is code. If you can organize a Notion workspace, then you can actually build out this for yourself, too. So, out of the box, your AI tool is going to start every session from zero, and [snorts] you’re going to re-explain your role, your communication style, and all of those non-negotiables. So, we need to build a system that effectively tell
Transcript continues…
Chapters
- 00:00 — The Problem & The Goal
- 03:10 — Part 1: Static context (you & your business)
- 06:56 — Part 2: Improved memory
- 11:10 — Part 3: Creating repeatable processes
- 14:06 — Part 4: Multi-step workflows on a schedule
- 16:14 — Part 5: Planning that matches the project
- 18:43 — Part 6: Managing projects & clients
- 20:44 — Part 7: Output consolidation
- 22:19 — Part 8: Access it anywhere