Lone Wolf Unleashed is an ICN production. The Indie Creator Network, powered by Podnose, is our service bridging the gap between DIY podcasting and full-service production. It's perfect for serious creators who want expert polish without the agency price tag. Find out more by messaging us via the contact page at podnose.co.uk. Today, I'm going to be walking through the different layers of AI capability for your business and you'll use this as a path to building your first agent. G'day, my name is Mike from Lone Wolf Unleashed and welcome to another week. So last week, we went through how to build your first agent. This is a layer on top of that. So if you haven't listened to that one, please go back and listen to that one first. This one, I'm going to be walking through a little bit more about the different layers that go into building AI agents that will give you the maximum benefit and trying to work towards making things faster in your business. And I have fancy little slides here that I, yes, I did use an AI agent to build these. So you can see those today if you are online. This will also be published to YouTube, so you can check it over there. Make sure to go and follow my channel, but let's kick right in. There's three different layers that I'm going to be talking about today when it comes to designing an agent and they are going to be in some way, they're going to be able to be embedded into whatever tool you're using on whatever plan you're using. I've tried to be a little bit general here because obviously different people are using different types of tools, which is fine. I personally, I'm on Claude. I'm paying for Claude Max. I'm getting excellent efficiency out of that and I hope to hopefully soon be able to put out some content about how specifically I'm using that connected with Obsidian and connected to my other different now MCP servers, GoHighLevel and Asana, which is pretty cool. Three layers. First one is connections. This is an AI that can reach into your other tools. They're the task managers, your calendars, your documents. They connect to other tools. The second one is the playbook. So these are your workflows, your instructions for how the AI agent is to operate. And then thirdly is identity. So who is the AI? How are they supposed to think? When are they supposed to push back on instructions? I, in my custom instructions, have that, you know, I want to be challenged on some of my ideas so I can get really good outcomes. I don't want it to be a yes man oh yes Mike that's an excellent idea no not all my ideas that I put into an AI engine are good ideas and you know because I'm using a lot for brainstorming and understanding client situations I really do like having that little bit of pushback when I'm trying to brainstorm about how to deal with certain situations so that's layer three identity there's different spectrums of what to do for using ai a lot of you have used it as a chat bot okay you've sat in chat gpt and you have put in queries maybe it's a sentence prompt maybe you've started doing your searches in there that you used to put into google so that's a fairly rudimentary type use for it okay it's capable of a lot more the second one is assistant so an assistant is something that helps you do your work it's not just search it's not just a chatbot it's something that produces outputs for you to use the example there is to draft a meeting agenda so you have an agenda it's templated and then you're going to draft the meeting agenda for the quarterly review right so you have a template which is standard and then the quarterly review content is the the custom stuff that you you getting it to generate into there then you have the agent so this does the work and it follows your methodology that you defined It checks your task manager for overdue items drafts the agenda adds action items and then asks should I send this. Okay so it's more autonomous in terms of it has a broader scope of the content it's putting in but it now has more context about what to put into that quarterly review and what to do afterwards. So it's got an expanded scope. So the agent has three things that a chatbot doesn't. It has hands, so it can reach into your other tools. It has the playbooks, which follows your specific workflow step-by-steps. And it has a point of view. Okay, what type of specialist is it? What is it supposed to be doing? What perspective is it supposed to have when it's looking at your different prompts that you're feeding it, the different inputs that you're feeding it? When we're looking at connections, what we're trying to do is we're trying to give the AI hands. An agent is going to be a little bit like an octopus. It's going to be taking information from different places and it's going to be feeding that in and then it's going to be pushing it out again. Without the connections, the AI can only talk about your work. It can't actually do any of the work. With connections, it can do your work. In different AI tools, I'm going to work through a little bit here. So ChatGPT, we're looking at different plugins, we're looking at GPT actions. Okay, so we're going to be connecting to external sources and that's going to be through your GPT builder. So if you're paying for the premium subscription of GPT, of ChatGPT, then you'll be able to have access to those functions. With Clawed, it's the MCP, it's the model context protocol. So you're basically installing the connections through the plugin marketplace or you're configuring that manually. You can literally ask it how to do that. So today I was doing this with Go High Level and it produced me an instruction list about where to find it in the settings to set that up so I can have Claude just attached to the MCP. What does that mean? It means that I can have a workflow where I can go, I'm going to add a task and assign it for someone to do something and I'm also going to add in the opportunity into Go High Level so we can track our pipeline. you know now we can execute multiple different tasks just from one prompt because it able is able to reach into those different tools to do that with microsoft you're looking at extensions and plugins and then with gemini you have the extension so it's built in for workplace for the workspace and you can have your third party via other extensions your common high value connections you know i've already mentioned asana you might have your other project management type stuff in there might be communications it might be hey once this is done and you can see that this is happening can you send a message off to so and so in this via this channel you might have it update documents i have that doing that right now i have it connected into my obsidian workspace i don't draft notes anymore claude does that for me i review them once i'm happy with them they stay there if i need to go back and reference them claude can do that it can provide me summary of what those notes are and what the connections to those notes are and things like that. It is epic, honestly. It really does change the game in terms of how you put all that information together, especially in a space like mine where you're having to deal with a lot of information. Then we have permission. So we need to be thinking about what is the model allowed to do? So this is just like any other user that you would put into your ecosystem. We have to be thinking about that for an AI agent as well. So what are they allowed to do? Are they allowed to read are they allowed to create an update or are they allowed to delete so there's going to be specific instructions here about what you're allowed to do and when and you need to define what those are don't miss that because otherwise files will go missing and you wonder why so here's an exercise you can do you can map your connection opportunities right so list three systems you use every day it might be a signer outlook it could be your google docs it could be Google Calendar and then for each one what do you do the most often in there so do you check what overdue do you set calendar meetings do you do XYZ and then which one would save you the most time if an ai could do it for you so i give you an example i have previously set up a telegram bot that will go to an nain workflow to punch meetings and blackout calendars in certain calendars i do that because i have to manage multiple calendars across different clients to update my availability and that makes it easy for me i can just do it from one message prompt so you might try to do that you might say i want to be able to set in meetings or things into my calendar i do that a lot i'm going to connect into whatever calendar system i'm using and i'm going to have the ai agent start to interact with that so have a go at mapping out your connections like that into the common tools that you use if you've gone through the process work that i've done before in a previous episode and you've got some process models there, you'll have already listed your systems about which tasks are dealing with what systems and you'll be able to basically go to there for inspiration about how an AI might be able to help you do stuff there. Then we have playbooks. So teaching your AI your workflows, describe to it, feed it one of your procedures. I'm not going to dwell on this anymore. Give it some of your knowledge about how that task is specifically supposed to be done. The worked example is the daily stand-up playbook. Okay, so we have a stand-up that we do with the team. Give me my incomplete tasks that are due today or overdue. Give me some tasks I completed yesterday and then the tasks I'm currently blocked on. I can share those immediately and I can punch them through a channel. This basically automates a stand-up between, say, a manager and their team or with a scrum master and a development team, things like that it streamlines it incredibly quickly because all that information is already available in the tools okay we're not having to have someone stand there and scroll through their tasks and just basically feedback what is already there those people can now focus more on that and i know there are many developers out there who are would be happy about doing that i know they don't like the stand-ups normally so what makes a good playbook so we need to be specific about what the output looks like, right? So if we're filling a document, what's the document template, what information goes under what area, for example. Okay, you might show a table with columns for a task overdue due date, what data are we using? Then we have the safety rails. So, you know, before deleting anything, you need to tell me, ask me to confirm, tell it what to ask, not guess. Okay, so if there's a particular framework you're going through, you might have it ask you specific questions so you can get good output and then you define the step order so previously i in the last episode i went through an example of what it was to build an agent that has a workflow okay this one was a communications disk agent that would go out do some research come back draft up a communication based on a template okay so that's a multi-step workflow with multiple steps so you need to determine those. And remember, start simple here. We're not trying to conquer the world. We are just trying to make some of the boring, laborious things that we do more efficient, more streamlined. Websites made simple. The place where website designers finally stop winging it and start running a business that actually works. If you're a web designer who's constantly busy, but still wondering why your business feels harder than it should, you're not alone. 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So we're going to turn the AI into a specialist now. So you've probably, from the early days of ChatGPT, I remember a lot of the things was around context setting. You know, I want you to take on this role so you can give me context back. That still remains true today. And we're going to still give the AI an identity so they know the context of which to respond to you. It's kind of like when you're at a barbecue, you don't ask the gardener for finance tips. You ask the guy who's in finance finance tips. It's the same as here is we're giving the AI agent and identity that is appropriate for the response that you're wanting to receive. So then we have the four components. Okay, we have the role, we have the directives, we have the knowledge domain and the routing. The role defines who the AI is. Okay, the directives give the rules the AI must follow and will never break. Make sure that these rules, the really important stuff stays at the top of the prompt. And then domain knowledge, what are the keys, concepts, terminology, you might feed it a framework that you're trying to work to you know feed it some of my transcripts if you want to have a systemization agent head over to my website grab some transcripts down and see if it can start populating in some procedures and knowledge from you based on the information that i share and then you have your routing so when someone asks x use y okay so you can start to dictate about when it uses certain other parts of your ecosystem based on keywords or actions so here's the full stack we have identity playbooks connections and permissions. So who am I? What are my rules? What workflows do I follow? What systems do I need to reach into? And what can I do without asking? So you might go away and build your own agent now, which could be a communications assistant. so you know you can build on working in a system for your business right so we have a social post client email content plan review response elevator pitch you might choose to build an agent around trying to do some of those things you know and then we're going to write those knowledge files that the agent can refer to as their knowledge base to go when i've been asked for this i can reach out into this file and get what i need to do for that okay it saves having to put so many words in just one really large instruction list because you'll consume all your tokens just trying to feed it the context that you need. If you refer out to other files, it will only refer to those files. This is building up in say an ecosystem like Claude Skills where it will only consume the context for the tasks that's been given to do. And then you'll write your system instructions. So I have an example there which I'll share on my website. You can find lonewolfunleashed.com. there is a resources page there you'll be able to head over to and then you can set it up you test it and you refine it so the first version is not going to be perfect it's going to have some bugs in it it might behave some ways or it might have terminology that you don't like you can always go back in and you can refine what those instructions are you can even just get it to update its own instructions okay that's what i've been doing with claude is i will get it to edit its own file based on the feedback that I have for it as we go. There's some common pitfalls there. I'll also share them on my website. Guys, I want to thank you so much for joining me today, learning how to better configure the different layers of what goes into an AI agent. Thank you so much for joining me today. You could have been doing so many other things, but you decided to hang out with me and learn about that today. Head over to my website, subscribe to my newsletter. I'm going to be pumping some stuff out on there soon to do with more of this stuff in more detail. and as always, I hope you have a great week and I'll see you next Tuesday.
