Hello, hello. Welcome to another episode of Old School New Tech. Thank you for joining me. My name is Rana Russi, and today I want to talk to you about company scale agentic AI and kind of the different things that I've been seeing and I've been experiencing in part of our work with different companies when it comes to uh really agentic scale. So the reason for this is I wrote a book, another one. It's called Company Scale Agentic AI. It's I would say that it's an executive version of my previous book, which is Production Grade Agentic AI. And the previous book was for engineers. It was about 650 pages long, and full code, the whole thing. And it did its job. But the message that stuck with me the most when I wrote it and kind of received feedback was actually from uh the people who signed the engineers' paychecks, which are the executives and all kinds of uh founders, CEOs, CFOs, uh those types of people. And they also admitted that I could have written in more plain English for the non-techie crowd, so that's why I went ahead and created and and wrote this new book, which will be start to be available in the next few weeks, probably first weeks of August, I assume. And it's about how the company from the company perspective should look at agenda corporations and the different things that they need to do. So that this is kind of what I wanted to talk about. The this is not a pitch for my book, just like my previous book, it's gonna be free. Uh, you'll be able to download it and get it for free. So I'm I'm not trying to get one on you. Obviously, if you want to, the printed version is gonna be available on Amazon, but PDF and uh EPUB are gonna be available for free on my website. So sorry. So here it is. The main thing that I've noticed with different companies that I've both worked with and advised with, and kind of came on board to try to analyze what the problem is, is uh the AI is not stupid. Okay, it's blind. And uh that that's kind of the reframe of the whole thing. A lot of the time, a lot of the times executives and even engineers tend to blame the intelligence. They you know they come up with things like, oh, the the model is no good, or it's not strong enough, or the workflow is not smart enough, it's not optimized. And I think that yeah, it could be the case where it's not smart enough or it's not uh optimized enough, but more often than not, it's blind rather than stupid. It means that it doesn't have access to how the company actually operates, and the whoever implements the AI in the organization, it usually happens in the form of training or in the form of some sort of let's teach you how to use cloud code or sorry, not cloud cloud, but cloud desktop or chat GPT. So this is where it usually ends. But the problem is that it's very disconnected. There's no obviously there's no company brain, even if they they think they have one, it's not a company brain, it's an assortment of information that different people have at their favorite chat companion. So it's not really a genic. Most of uh, you know, if if you're seeing this and if you're looking at the listening or seeing this episode, you are probably more belong more to the tech crowd, so you're probably a bit more advanced, or you think that everyone and their mothers are using AI and workflows and loops and graphs and all that cool pipe words that are going on at the moment. But the fact of the matter is that in most organizations and in most large-scale companies, especially companies that are not startups, they're not new companies that have been around like 10, 20, 15 years, not to say 30 or 40. The AI, if it's even being used, it's being used in the scope of firing up Chat GPT, asking a question, and moving on. So don't assume that everybody is using AI just yet. So what I'm noticed happening is that you know, the more advanced people of those companies that are not using just chat or cloud desktop, what they do is they tend to start a project which is already a step in the right direction within those chat applications. They load it with all sorts of information about how the company works and different policies of the company, different types of call transcripts that they had, which is great, but it's not enough. You know, every company has that one person that knows everything and it's already like a second nature, it's not really a well-thought-out process. You know, Jane knows how to do XYZ. And if you ask Jane to kind of lay out how she's actually doing that or what helps her make the decisions, she's gonna write it down. But I bet that in most cases it's not gonna be accurate because some of the things, if not most of the things that she's doing, uh she's not even thinking about them. She's just doing that because she's been doing it for 10 years or 15 years, she's she's been doing that. So some of the things, you know, they they only live in the COO's head, or they're a decision that happened on a WhatsApp thread, or uh a decision that happened on a call, on a phone call, and nobody bothered to log it anywhere. So that's what I mean when I say the AI is blind, it's not stupid. And the road to fix it starts with fitting it all the information in a loop. Yeah, I know it's a hype word right now, and that's not even the type of loop that I mean. What I mean is an ever-present and ever growing company brain. So let me show you an example of what I mean. So this is uh the book, but that's not what I wanted to show you, so let me start sharing the screen. Yeah, so that's the loop that I'm mostly interested in showing you. It's a loop that has five pieces to it. One is the observe part, and this is kind of a snippet from from my book, but let's see if I can make it even larger so everybody can see it. All right, yeah, yeah, that's much better. So this is this is the loop. You start with observe, meaning you have to install a ghost in the machine, you have to install an AI agent that lives on Teams or Slack, obviously, not in private messages unless it was invited. It's BCC'd to email that are going on in the company, or even better, hooks up to the company's email server. It's also a resident on the company's WhatsApp group or iMessage groups. It's there whenever there's a call transfer, sorry, a conference call that's happening, or even a phone call that's happening using tools like Plot or Pocket or these types of uh widgets that clip onto your phone. So it needs to know everything. Obviously, it needs to sit on the company's Google Drive, and it just needs to absorb everything that the company is doing. This loop on a smaller scale needs to happen on a per employee as well level and on a per team level. But right now, let's talk about the company as a whole. So it needs to observe everything and it needs to understand and distill how the company actually works. So if you go to a manager and those companies and you're gonna ask, you know, how where are the bottlenecks? What's the process of doing X, Y, and Z? How long is something taking? They're gonna answer wrong most of the time because there's a difference between where you think the bottlenecks are and knowing where they really are, where time is being spent the most. So that's the second part of that loop is to understand everything that's going into the company and to build its own company brain and its own knowledge graph of how the company works, its processes, the relationship between people. You know, Jane usually calls upon Mark to do XYZ, and then Mark sends an email to whoever it is, and so on and so forth, and just build that uh knowledge map of information. The next thing that the next part of the loop is to build. So now that we understand everything, that system needs to come up with its own suggestions on what needs to be automated, at the very least, a report of where the bottlenecks are, what needs to be automated, where things usually kind of fall uh through the cracks, you know, stuff like that, and either have someone on the company build that workflow or start building its own skills, and then the next phase obviously is to run it, and then this stays in a loop that the final step is the compound. But if you notice here on the run part, you can see that there's a dial for readiness. So, first off, even though I'm kind of an AI guy and I'm all in, and this is uh the topic of this conversation, I want to kind of make sure that we understand, and this is the I'm looking for a graph here. I'm gonna find it, it's somewhere here. Yeah, all right. I want to first make sure that we all have the distinction between an automation and an agent, and this is another graph that that is important. So too often I see kind of the run to fully autonomous agents, and in most company and business environments, what you need is is an automation, you which means it's fixed path. You know, whenever a support email comes in, the next step is to gather all the information about the client or the past who the account representative is, the path, the past correspondence with that with that client, what products they bought, so on and so forth. So that's you don't need an AI for that. It's just gathering the information. You can go directly to your CRM with an API and build this. And then you need to understand the problem and answer the client. Or if it's a refund, just make decide on whether or not you're making a refund. If it's a very deterministic step, you don't need AI at all. And but in most cases, you do have an AI block within an automation. So writing the email, making a sense of what the customer wrote in the support request. So that those are the types that are more fuzzy, and they can benefit from using AI. So it's it's not like here's a customer request, let's throw it into an LLM and hopefully can figure things out because I've loaded it with a bunch of MCP tools and I'm just gonna pray that it's gonna work. No, there are some deterministic, mostly deterministic steps before the AI gets involved. An AI, on the other hand, is for the fuzzy parts for the things that are non-deterministic. So if an urgent email arrives in that regard, so the first thing that we might want to do is if there's an urgent in the subject, or maybe this entire block can be part of this step, is who should we redirect the email to? We need to understand from the context of the email who is responsible, and do we need to get back to the user with more information, or can we re-resolve it without even escalating it to a human, and so on. So that's a very important distinction. And the reason why I wanted to cover this before going on to the next step is to talk about this style. Most of the operations, especially in the business environment, need to need to start off as gated, meaning the AI and the agentic stuff is going to do, or the agentic or the automation is going to do most of the work, but it's gonna leave you with the final step. So it's gonna leave you with a draft email, for example, or it's going to leave you with an order that's already filled and waiting for you to review it. So this is a very important step because in a business environment, obviously, one you know, wrong move can cost the company from tens of thousands of dollars to millions of dollars, depends on the company size and scale, etc. So that's it that that's a very important thing that you you do not rush to full automation when um during that time where the company, you and other stakeholders in the company need to build trust in the fact that the system can actually perform well on its own without any human in the loop or human in the gate. These are two different things. Here I'm talking specifically about human in the gate. So that's probably one of the more important things is not to rush into the automation because if and the goal, by the way, of the gate is not to get a draft, see that it's not exactly what you wanted, then edit it yourself and then send the email. No, the goal is for you to see that if two drafts in a row are kind of not correct or not in the spirit of the company or not according to the policy or whatever it is, then you go back to either the skill or the process or whatever automation or genetic automation that you've created, then you go back and fix that. You fix it as a root cause and not at the um not at the output level. Okay, so that's that's kind of something that's that's very important. And as the company learns more, or the company's uh brain, this entire loop keeps learning from how you're fixing the drafts and how how many times you had to cancel an operation, it keeps on learning and keeps adjusting. Now, another thing that is very important, and and I won't don't don't want to go into too much technical detail because you'll as I mentioned you're more than welcome to go and uh download the book. You know, in fact, I think I created like a widget here. Here you go. So here's a widget. Just scan that, you can go download the book, and obviously I'll add the link in the show notes. But the the issue of role-based access management. Another problem that is occurring quite often with uh companies when they implement a genetic process or let's just call it the implement AI into the organization, is they are bound to not implement it at the organization level, but they're doing it more on the department level or on a personal level. And the reason is that if I'm connecting to the CRM or the ERP or the accounting software, if I'm connecting to that software with privileges of the CFO, I should be able to see everything. But someone from I don't know from the development team or the HR team should not have access to the company's financials or the company's legal team, and building those gated systems where every person in the company have possibly access to the same tools but in different access levels, that is something that is very hard to implement. And one of the things that I recommend that companies do is, and there's really no way around it, is to build a custom solution, which is something that I've done with uh with Moxie, my own open source tool, is to build a mechanism for a middleware, that's the word I was looking for. So to implement a middleware that automatically removes different tools or adds different tools that are available to the agent based on who's talking to it. So some people will have access to rough numbers. What are the growth or the trend in the company's financial? So they're gonna get it in percentage-wise all only because they need to build their uh sales prediction or sales pipelines or whatever it may be. Some will have access to the full information on a per line item basis, and some will have no access to this tool. So by implementing that middleware that sits between the user and the brain, and kind of filters down the different tools and levels of of what can be done with the system based on who you are, and it's also recommended that that you we know who you are, where whether you're contacting the system using email, using Slack, using uh chat-based interface, or anything like that, that is also an opportunity for the middleware to take to take advantage of. But filter the access level right then and there before the the request even gets to the system. So it's very hard to do. It usually takes you know integration with tools like WorkOS, which in turn needs integration with the company's Active Directory. And yeah, it's it's it's not an easy task, but if you have any chance of implementing an AI system that you can feel comfortable with, and on an organizational level, I'm talking about hundreds of employees, if not thousands of employees, then you have to have that role-based access management ability. Let's see if I can think of another thing. Oh, yeah. One other cool thing that we uh we had an opportunity to implement with one of our clients is to build, they wanted it as a new tab in the browser, just a simple Chrome extension, but the implementation is the same wherever it is. So whenever they open a new tab, it automatically connects to the system which pulls information like a morning brief, but that gets updated every time they open a new tab. Obviously, with some sort of a caching, we don't need to literally fetch new information every second, but every few minutes they're gonna get that information refreshed and they don't have to worry about you know forgetting to get back to Sarah on Slack or that they have an email waiting. It's gonna be right then and there. And unlike a to do app, because that's what basically that that's how it kind of looks like, unlike a to do app, when you click on it, it automatically takes you to the right email or to the right Slack thread. And the only way This can get checked off is by you taking the action on the Slack email, WhatsApp, whatever it is, or delegate it to an agent saying, okay, go back to get back to Sarah and tell her that blah, blah, blah. So that's the only way to take off that task. And it's and it's nice because you don't have to worry about things falling through the cracks because you always have that being that collects information about everything that's on your plate at any given moment, and you tend to be a lot more efficient. And that may, you know, that slightly takes me back to my final point before I close on, is most AI kind of transformations or consultants that I noticed, they tend to force the company to reshape itself or its workflows around how the AI works. Yeah, you need to move this workflow into cloud co-work or you need to start using this tool or that tool. But you know, that 60-year-old HR person, they're not going to change a workflow that they've had and they've been using for the past 20 years. So you can either fire them, which I don't know if it's the right thing to do if they're there for 20 years. I don't know if it's in your best interest. You can force them to adopt new tools, which will probably cost you in time wasted, morale, and not at all sure that they're actually going to stick to it. Or you can shape the AI to work where they work. So if they like to keep working with their outlook, then make sure that they can email the company's AI if that's their preferred way of talking, or if they're already on Slack or Microsoft Teams, that that's where they're gonna have that interaction with AAI. So they wouldn't have to download new software and get used to new things because the time to adoption, yeah, you can with new hires, with the newer crowd, or with the younger crowd in the company, you you can probably say, hey, that's uh a better tool. Let's let's start using Buzz, for example, over Slack. But most of the company is still on Slack, so you'll need to build some sort of a bridge where the cool kids are hanging in in Buzz and using agents to do all sorts of cool stuff, but the rest of the company keeps using whatever it is that they've been using for the past 10, 20, 30 years. So, yeah, that's kind of what I wanted to cover today. And again, it's not a pitch to my book, it's it's completely free. Where is that graph that this is the loop I want to leave you with? And I think that this is the most important part of the whole thing is we need to start with observing, make sure that there's always an observant and observation process that's going on and lives inside the company, and it needs to understand. And when I say design the company, it's I mean everywhere. Because if one decision was made over a phone call and that was an important decision, and nobody followed up on it on email or whatever, the that ghost listens on the the it will take the wrong decisions. So you need to make sure that it knows everything, and that's basically the the process. You need to observe where where did it go? You need to observe, understand, make sure there's uh distillation, uh distillation process, I think I'm saying it right, and then start building around the conclusions that the and the reports that the AI has been giving you, and then it just compounds every time you learn and you make a new decision. The company the company brain and the company's ghost, for lack of a better word, need to adjust, need to adapt, need to update its its own processes, skills, or whatever it's decision making. And this might be a good opportunity to kind of make a mental note to have another episode where we can probably talk about a company-specific LLM that you fine-tune that is built on top of all of that. So decisions are happening on the LLM level rather than on the harness level, or even better, both. But yeah, it's out of scope for what I wanted to talk about in this episode. So, yeah, that's that's pretty much it. And if anyone has any questions or feedback about this thing, you're more than welcome to hit me up on LinkedIn, X, email, you know where I am. And yeah, thank you for spending this time with me. And I'll see you on the next episode. Bye-bye, everyone.