---
title: "How I Built an AI Agent Operating System in 90 Days"
episode: 75
podcast: "The LeanScale Podcast"
publisher: "LeanScale"
guest: "Yasin"
guest_title: "Head of AI (former), LeanScale"
date_published: 2026-05-15
date_modified: 2026-07-22
duration: 00:20:32
word_count: 4927
topics: ["ai-in-gtm", "revenue-operations", "gtm-strategy", "sales-enablement"]
canonical_url: https://leanscale-knowledge-hub.netlify.app/podcast/ai-agent-operating-system-90-days/
source: "LeanScale Podcast Knowledge Hub — https://leanscale-knowledge-hub.netlify.app"
license: "Free to quote and cite with attribution to The LeanScale Podcast."
---

# How I Built an AI Agent Operating System in 90 Days — Full Transcript

> Episode 75 of The LeanScale Podcast, with Yasin.
> Published May 15, 2026 · 00:20:32 · 4,927 words.
> Machine-transcribed and **not diarized** — speaker attribution is inferred, so verify
> attribution against the audio before quoting a specific person.
> Structured breakdown: https://leanscale-knowledge-hub.netlify.app/podcast/ai-agent-operating-system-90-days/

## 00:00 — The pitch: an agentic operating system built in 90 days

**[0:00]** This is a 15-page scoping document on how to implement sales territory design for one of our customers. And this is a diagnostic on a startup's entire go-to-market. And this is a 50-page playbook that our entire team runs on. I didn't write it, and AI agent did. So 12 weeks ago, we were using chat GPT like everyone else. You open it up, you ask a question, you copy the answer out, and you close it. And now we have an entire agentic operating system. A customer warehouse that enriches itself, agents that read our transcripts, update our files, and prep for our meetings. And here's the thing, we didn't build any of this.

**[0:32]** We used AI agents in order to build it for other AI agents. How crazy is that? And look, 12 weeks ago, I couldn't tell you how we were going to do this. And there's a lot of noise out there right now about, "Hey, train your AI agent in order to do that." And it's this amorphous understanding. And what we want to do today is I'm really going to break it down for you, step by step, on how we were able to go ahead and set up this entire LeanScale AI operating system. So the biggest thing that you have to know is that all of this is really just based off of folders and files.

## 00:59 — Agents and repos are just folders and instruction files

**[0:59]** And so when you hear about, "Oh, it's an agent," all you got to think about is a folder that has a set of instructions listed as a file. And I'll show you an example inside of this video. And then we have an AI platform that all of this runs through. So everything lives inside of repositories. And this is one of those words that if you hear about, it's like, "Oh, it's a developer talk." No, no, a repository is really just a folder. And so in a second, when you hear me talk about the idea of a transcript warehouse repo and your head just convert that into a transcript warehouse folder, that's really all it is, same thing.

## 01:30 — The four folders that form the backbone

**[1:30]** So we have four folders that really make up the backbone of this entire operating system. The first one is going to be our transcript warehouse folder. The second one is our customer warehouse folder where all of our customer intel is. The third one is going to be what we call our GTM library. It's pretty much all of the playbooks that we run in-house. And then the fourth one is our lean scale context folder that houses all of the context particular to us. Now, instead of actually just walking you through the folders, because that could be a little bit dry, what I want to do is I want to walk you through how the agents actually move through them

## 02:01 — Transcript warehouse: agents that read every call

**[2:01]** and how each of the agents is doing a handoff to the next agent in order to make the entire system work. So the first one is our transcript warehouse repo. And so what we have is that every single call, every single meeting, the transcript are actually automatically landing inside of this folder. And so we use fireflies, but it doesn't matter what you use. It could be gong, chorus, fathom, whatever it is, this is the raw input that is feeding the entire system. Now, you might be thinking, "Hey, doesn't all of this information already live inside of the CRM?" Now, there's a very important reason why we don't actually house this in a CRM

**[2:35]** and why we actually house it in repos instead. And there's an even bigger reason as to why you wouldn't want to be siloed inside of a CRM. And we'll get to that in just a second later on in the video. And so what we have is that we have two agents that are actually watching this transcript folder. And what it's doing is that every single transcript that lands inside of that folder, this agent is reading it, and then it goes ahead and actually annotates that transcript based off of what happened on the call. And then it's routing that to the right customer folder that is inside of the customer warehouse. Now, this isn't just a simple automation, okay?

## 03:06 — Inside an agent's instruction file (the SOP)

**[3:06]** Because what's really happening is that the agent is tagging it, and it's looking at the context in order to figure out. Is this a discovery call? Is this a renewal conversation? Did the customer mention a competitor? Did they push back on pricing? Did they have urgency on the call? And so all of that is being annotated inside of that transcript based off of the agent. Now, I know, again, that sounds super amorphous. So let me actually open up the agent's instruction file and show you exactly what that looks like. Okay, so what you're looking at is the actual transcript processing agent.

**[3:34]** And this is the really cool part about the way that AI and agentic AI is moving right now, is because you can literally code an entire agent in natural language and then feed it that SOP, and it can literally follow that instruction. It's not like you got to learn how to code or any of the other things from the traditional paradigm. And so you can see this transcript processing agent, it has the overview, and then it breaks down into the phases and the tasks of what it's doing. And so every single time a transcript is going ahead and landing inside of that folder, we do have an automation that is kick-starting that agent,

## 04:01 — Token windows and why agents pre-process the data

**[4:01]** but then that agent has reasoning compute in order to actually go through and figure things out based off of this SOP. And so when we say programming the agent, when we talk about training the agent, this is what we're talking about. It's literally just creating an instruction file for the agent to follow, and then you can go ahead and kick-start it off of an automation. So that's what that agent is doing. But now here's the key part, because remember, in a previous video, I've talked about this, we'll probably touch on it a little bit more in this video, is that in LLM world and in AI world, every single agent is going to have a 200,000 to

## 04:30 — Customer warehouse: enriching account context

**[4:30]** a million token window. That's about 400 to 500 pages effectively of what it can focus on. That means that you can't just dump 100 transcripts and expect you to parse through it. And so what happens is that this agent is now actually going ahead and setting up things for the next agent inside of the customer warehouse. And in the customer warehouse, we have another agent, which is now reading through the transcript and the notes that the previous agent left it and enriching each of the actual customer warehouse folders and the files within that. So for us, we look at the tech stack of our customers,

**[5:00]** we look at the context, the stakeholder map, all of the additional detail that is being teed up. And so that customer warehouse agent is taking that and looking at all of the notes that the transcript warehouse agent went ahead and left it. So that way it can do its job. Are you starting to see how this is kind of like replacing team members? It's literally a handoff that you're having from one agent to the next in order to go ahead and do their job. And so in our system, the next folder that we then have is this customer warehouse folder. And I'll show you what that looks like right over here. So you'll see we have our customer warehouse folder.

**[5:28]** Obviously, this is an anonymized demo version of it. But you'll see in here, we have all of our customer information. And I'll show you one ahead and put this example file together. So we have overall context. I'll show you this example enrichment on here. It'll go over, you know, Flowforge is a demo company we went ahead and put together for this video. But it's like company snapshot, stakeholders, current priorities, pain points, tech stack, verticals, and ICP. You'll see this is a very long document based off of the context of this customer. Now, here's the important part that you need to keep in mind because you might be thinking, okay, well, what?

**[5:57]** A human's going to go out and read this entire document? No, what we're doing is we're creating these context files by extracting the nuggets from the transcripts. And then these context files live in order for the next agent to be able to get that handoff. So that way, it can actually work on what it needs to work on. And so really, what this entire ecosystem that is being built is a context memory layer for the agent platform. So that way, every single agent can have memory as to what is the context for each customer? What is the context for you as a company? What is the context of what's going on overall with the playbooks?

## 06:28 — The context memory layer, made for agents to read

**[6:28]** What's going on with the sales reps? And this is the ecosystem. This is the platform that enables that memory layer. And so these documents are not read by humans. They're read by other agents. And so it's really an ecosystem made by agents, for agents, used by agents. How crazy is that? And so what we have is we have this agent right over here. And this agent, again, just like the last agent, really whenever you're here training an agent, it's just a file that gives it instructions. That's literally it. And as long as it's living within an agent platform, you're good to go and you just feed it that instruction set.

**[6:56]** And so you'll see, we have this instruction set right over here, which is the customer context enrichment SOP. And so what happens is that that agent is using this SOP in order to process these transcripts right over here. And what it's doing is it's figuring out what are the nuggets? What are the important pieces, the important things that it needs to go ahead and enrich all of the customer context. And that is what you're seeing right over here. And so what really that agent is doing is it's taking the hand off from the previous transcript agent, which went ahead and already left for it a bunch of notes.

**[7:27]** And then it's further going ahead and processing that. And then enriching the customer's information. So that way when the third agent comes and says, OK, we now need to do a sales territory design. We've got to do a lead routing project. We've got to go ahead and really map out the quotas for the sales reps. When that third agent comes in, it's not having to parse through hundreds of transcripts. All of this processing has already been done by other agents. And so that third agent can just get to work working on lead routing for the startup because it has all the context and it has that memory layer built in. OK, so that's our customer warehouse.

## 07:56 — The GTM playbook library

**[7:56]** We've talked about the transcript warehouse. We've talked about the customer warehouse. The third one is what we call our GTM library. This is really our playbook repo, our playbook folder. And I'll show you what that looks like in here. And so for us, what we have is we have all of the projects, obviously for us because we service startups in B2B tech, we have every single process in all of GoToMarket mapped out with individual playbooks. And here's the key. You're going to notice the theme here, OK? All of the playbooks on this are not like a couple page documents. We're talking about super in-depth documentation.

**[8:28]** This is just a sales territory design playbook, right? The methodology. I can open up the implementation process. Look at how long these documents are, OK? The reason is because when I say that for XYZ startup, we're going to do a sales territory design, that agent has all of the context it needs to understand what sales territory design best practices look like from our IP, from having done this dozens of times. We've trained the agent on that. How have we trained the agent? By putting these files together. How do we put these files together? By using a different agent in order to actually create the files in the first place.

## 08:58 — Live demo: generating a territory-design scoping doc

**[8:58]** Now, I know a lot of this sounds like really amorphous. It sounds really vague. Let's actually do a practical example so you can see how the agent platform is working with all of these context files and how the backbone of this entire operating system works. And so let's say, for example, we go over to our example customer. So we have flowforge over here. And what we have is we have their context file, which is right over here. And so what I'm going to do is I'm going to go ahead and copy the path of that context file. And then I'm going to go ahead and shoot up a different AI platform called Whisper Flow. So that way I can talk to my agent.

**[9:30]** And I'm just going to go ahead and say, I'm looking to go ahead and do a sales territory design for this customer called flowforge. So I'm going to go ahead and give you the context on this customer, which is a file I've gone ahead and given you. The next thing I'm going to do is I'm going to go ahead and give you the playbook for sales territory design. And then the third thing I'm going to go ahead and give you is I'm going to give you the kickoff call that we did with the customer flowforge on what the customer was saying that they needed for sales territory design. And then what I want you to do is I

**[9:56]** want you to actually create an entire end-to-end scoping document that is customized for this customer and go ahead and put it inside of that customer's project folder. And then what I want you to do is I want you to go ahead and save that scoping document inside of that customer's sales territory folder. OK. And then from there, what I'm going to do is I'm just going to go ahead and feed it. So I have the transcript right over here for the kickoff call. And then I'm going to go over to our warehouse right over here, which is our Playbooks warehouse. And I'm going to go ahead and feed it all of the sales territory design playbook.

**[10:27]** And you'll see that this is like a massive document. We've got overview, methodology, implementation, post-project support. How do you do a diagnostic report on it? How do we actually load that up inside of our project management software? All of that context, with the click of a button, I was able to go ahead and copy the entire folder's path right there. And then you know what? Just for fun, let's actually go ahead, and I haven't talked to you about it just yet, but we're going to talk about it in just a second, which is our LeanScale context repo. This repo actually has all of the context about us as a company, all of our brand guidelines,

**[10:54]** our customer avatars, our pain points for the customer avatars, et cetera. So I'm going to go ahead and go over to our brand guidelines, and I'm going to feed it our brand tone. And I'm going to go ahead and give it one more instruction. Oh, yeah, and just for fun, I actually want you to go ahead and use the LeanScale brand tone and make sure that that scoping document is inside of the wording and the language for that brand tone as well. And then just like that, I go ahead and send the agent on its way, and it's going to go ahead and read all of those files. It's going to understand the prompt.

## 11:20 — Why an agent platform beats Notion or a wiki

**[11:20]** You'll see it's going ahead and reading all the documentation, and it's going to create that scoping document for me. Now, obviously, this is just an example for us and what we're able to do for the customers that we work with. But you can start to get your gears turning of how this might actually be useful for your customers. And if you have agent platforms like this running inside of your organization in marketing, in sales, in CS, across your entire company, how powerful this would actually be. Now, when you see us talk about these repos and these folders, some of the questions I'm going to try to answer as they're coming up in your head is, hey, how

**[11:48]** is this, let's say, playbook library different from a wiki or saving something inside of Notion or saving something inside of a Google Drive folder? And the answer for that is going to be a key theme that I'm going to go ahead and come back to again and again, which is that the agent platform is tool agnostic. Notion isn't going to connect to your CRM. And so in the previous video, we did talk about the difference between consumer apps and agent platforms. So make sure you go ahead and refer to that for a deep dive. But that is the difference when we're talking about all of this living inside of an agent platform,

## 12:15 — MCP: mixing and matching across your tech stack

**[12:15]** is you're able to mix and match from different places. And so imagine what's happening here with the agent platform. We're able to go ahead and mix and match files from each of our different repos, which is all the context in the memory layer that it has. And then via MCP's, model context protocol, we can actually pull the data from HubSpot, from Salesforce, from Google Drive, from Snowflake, from Intercom. And so really, all of that becomes the central repository. Everything then becomes housed inside of what we call the new workspace for the future, which is the agent platform. So that way, Cloud Code, Cloud Cowork,

**[12:45]** whatever agent platform you decide to go ahead and use, is able to go ahead and mix and match and do work with that. And then here's the other big benefit of an agent platform, is because an agent platform is now able to actually write into the files. It means it can edit the files, right? And so now, every single time we do a sales territory design implementation with this massive document, let's say we learn something new when we're doing an implementation for a customer. All we have to do is we tell the agent, go ahead and look at that file, go into the actual playbook, and update based off of what you learn from implementing

## 13:15 — Agents write to files, so the system compounds

**[13:15]** this inside of this customer. So what you're noticing is that then the entire system becomes something that compounds on top of itself. And so now, the agents are actually making the system better. So that way, every single instance, every single prospect, every single customer that you talk to is actually making the system better. This is the difference between having your knowledge siloed in all of these different tools, as opposed to having it a part of this ecosystem that is what we call the agent platform. And last but not least, we have this repo, which is what we call our lean scale context. For you, it would be called your company's context.

## 13:47 — The LeanScale company context repo

**[13:47]** And inside of this repo is pretty much all of the context that is pertinent to you. So it's going to have your brand guidelines. It's going to have your customer avatar information, their pain points, everything that your team can come back to again and again that is going to be relevant information for them to know about you. So that way, whenever you're launching a new agent and you want to go ahead and tell it, hey, here's our customer avatar, I want you to go ahead and reference, how does this transcript with this prospect, if I'm a sales rep, if I'm an AE or an SDR, how does this actually compare to our typical avatar?

## 14:14 — Reviewing the agent's finished scoping document

**[14:14]** Boom, the agent can go ahead and give you that answer very quickly because you can just mix and match those files, transcript file mixed with the context file around the customer avatar. Now let's go ahead and look back at how our agent is doing. So remember, we went ahead and launched this agent for sales territory design. And it was going ahead and customizing everything based off of this demo customer that we have. And so remember, we had all of this in rich context. And we went ahead and fed it the kickoff call that we had with the customer in order to then design the scoping document for us. And so if we open that scoping document up,

**[14:46]** you'll see what we have here is we have the actual territory design, scoping doc with the executive summary, the current state, the data problem, the business objective. And so what you're seeing is this is a extensive document with diagrams that this agent has gone ahead and put together. And again, the reason why this is so powerful is because it had all of the context about what does it mean sales territory design for us at LeanScale with the entire playbook. What does it mean to be able to customize this for this customer because it had all of that enriched information? What does it mean to be able to kick off

**[15:16]** this project for this customer because they actually had the kickoff call transcript? And so that's the reason why the output quality is contingent upon the input and the context that you give it. This is the reason why I make such a big deal about what I was saying earlier is that the agents are actually prepping the data and making sure that the data quality is there. And so it's an evergreen process that is being upheld by the agents in every step. I know we went ahead and addressed it just a little bit earlier, but I want to really go ahead and emphasize this point. Because the question that the market's going to have

## 15:45 — Why you still need this on top of your CRM

**[15:45]** is, well, all of this is living inside of our current tech stack. We have our CRM. We have our sales engagement tool. We have Gong. We have Kors for recording my calls. We have all these enrichment tools. All of that information is living in the tech stack. Why do we actually need an operating system like this? Because here's the shift that you're going to have to make and understand about where the market is moving, is that the agent platform is becoming the new workspace. Why? Because the agent platform is tool agnostic via MCP connectors. It's able to go ahead and pull in the information that you need based off of whatever else

**[16:17]** is living inside of your tech stack. And so whenever you need to go ahead and update a deal inside of Salesforce, you can do it through your agent platform. Whenever you need to go ahead and pull in call transcripts and analyze those based off of information that's in your playbooks, you can actually go ahead and use the Gong MCP in order to cross-reference that with your pipeline information that's living inside of your CRM. And so your agent platform becomes the interface through which you're actually looking through all of the information throughout your entire organization. This has been what is missing in AI all along.

## 16:47 — MCP explained (Anthropic, 2024)

**[16:47]** It's one central workspace that connects everything together. And real quick, if you're not familiar, all of this is really made possible not only with the repos and the folders that I showed you earlier, but also through this thing called MCP model context protocol. This is something that Entropic came up with in 2024. It's since become the standard in the LLM and AI world. And it's really kind of like an API endpoint. You can kind of think about it like a plug-in that allows the agent, the AI, the LLM, to actually connect to all of these other third-party sources and really start to pull in all of that information.

## 17:17 — Agentforce vs. tool-agnostic agent platforms

**[17:17]** And so a lot of times when I'm explaining this, sometimes we get the question. It's like, OK, well, what about Salesforce's Agent Force? What about all of the AI tools that are living inside of each of the different platforms? The difference is that Agent Force is not going to be platform agnostic. Unless you're using some sort of agent platform, like OpenAI's codecs, you're using Google's anti-gravity, you're using Cloud's code or Cloud's co-work, the capability of the agents is not something that is built into Agent Force as of right now. But even if Agent Force was able to do some of this, it's not going to be tool agnostic

## 17:48 — GTM use cases: morning prep and stakeholder maps

**[17:48]** to be able to pull from all of the different sources that you need it to. And so the agent platform is that layer that is sitting on top of everything in order for you to be able to mix and match. That is the difference. And so then when you have this agent platform all set up, well, what can you actually do? Well, if you watched the last video, we talked a lot about those use cases. So let's say reps are struggling in order to find patterns as to why deals aren't closing. Being able to check the forecast and the reality of that. Being able to pull up a stakeholder map whenever a champion leaves. Those are just the tip of the iceberg use cases

**[18:16]** that you can have inside of GTM. Let's say, for example, you want to do morning prep. You can actually have an agent that checks the recent activity inside of Salesforce. It'll go ahead and read the last few transcripts of the calls that you had with the customers. It can then go ahead and research online if there's been any public activity about those companies in the last week or since the last time you talked to them. And they can go ahead and send you a one pager inside of Slack for all of the calls that you have on that day. Imagine how powerful that is. And that's just a small example of what you can do when you mix and match and create these agents.

## 18:45 — Scaling playbooks across every customer

**[18:45]** Now for us at LeanScale, because we service investor backed B2B tech startups, let me go ahead and show you a cool use case as to how we are using it, right? So we went ahead and showed you how we were able to go ahead and create the Sales territory scoping document based off of the playbook, as well as the transcripts for that. But the cool thing is that it doesn't just stop with Sales territory design. Inside of our playbook library, which I'm gonna go ahead and pull up right over here, you'll see that we have all of these playbooks built for every function in Go to Market that I can literally just right click, copy path,

**[19:17]** give it to the agent, pull in the context for every single customer, whatever the customer, whoever that might be, and then the agent can get to work actually going ahead and figuring out the implementation plan for every single one of those customers based off of the stakeholder map and then actually create who are the stakeholders from each of those companies that we work with in our customer base that needs to be on the calls and create the entire roadmap for that project and that implementation all done because you've gone ahead and trained the agent on our entire business. And so that's the shift that's happening here.

## 19:46 — Early days, compounding value, and what's next

**[19:46]** It's not just about how do you actually make the agents and the AI gets better. It's how are we contributing to making sure that the data layer and all the context that we're able to go ahead and give it across our entire organization is already organized. And the cool thing is you can just use agents in order to organize that context for the other agents to do work. This is still super early days. Like I said, just 12 weeks ago, we're really going ahead and experimenting with this. And so the compounding is real, the value is real. We wanna go ahead and show you every single step of the way that we're building this.

**[20:15]** And so if you wanna see how we actually set all of this up from scratch as well as the use cases in GoToMarket, that's actually gonna be in the last video. We talked about how to set up the platform, the repos, the automations, all of that. That's gonna be in the last video. You can go ahead and use the link below and follow along. And so with that being said, this is Yasin from Lean Scale. Check it out. Catch you in the next video.