The Hidden Cost of AI Agents No One Talks About

23:31Darin Patterson:It's a standard way of an AI client talking to a tool, most often a software application. We could definitely talk about MCP for a while. Make operates in a unique place where this is an example where we're using MCP as a client and just to kind of show off that you can connect to any of these MCP servers. Yeah. It makes it interesting because it's also a MCP server itself. Right. Yeah. In the sense that you're connecting to different other platforms like via API. So you are sort of like the ultimate MCP server in a way because you can connect everything. That's exactly it. Yeah. And you actually get a little bit more control over that.

24:13Darin Patterson:Again, it's how much agency do I give AI versus the kind of precision that MAKE enables you to kind of manage and orchestrate it at a higher level. I had not considered that essentially you're an MCP. Yeah. That makes so much sense. All of the integrations. Yeah. and it's and it's good because it you know oftentimes i hear the criticism of mcp is that uh it sends they can bloat out the context window right like really bad like because you're connecting all these different functions that the ai can use at any given times and then if you include instructions with it that's very complicated this make is a lot better mcp in some ways i mean you can tell me all the ways um uh because it it's very deterministic and very token efficient.

25:00Oh, nice. By contrast. I didn't know that. Right.

25:03Darin Patterson:You nailed it. You couldn't have said it better. It's something we spend a lot of time thinking about and kind of positioning how do you, you know, MCP is new to a lot of people, so they're learning the best way to use it. And then how do we think about and understand the value you make ads on top of that? That's exactly right. Those context windows. And ironically, this is a great irony. Agents and AI need good context to be successful. And ironically, the more you throw at it, the less successful it is. It is about precision. We can look at an example of that exact concept as well as we wrap this up this particular demo.

25:43Yeah. And I had two more questions about this at the end. I don't know if you can address them. Yeah, please. The two questions are, number one, the trigger is great. I'd love to know a little bit more about how that works because when I'm building my own automations, oftentimes coming up with the perfect trigger for something is a lot of times my sticking point, ironically. Yeah, that's interesting. Yeah.

26:07Darin Patterson:I actually spent a lot of time with some people earlier this morning and they were having those exact discussions. So very often people think when they think of AI, they think the trigger is I send a message to it and then it talks. and we've kind of been trained that way right from the chat interface it's like okay i need to do something now i ask for it yeah yep i always wanted to be something really simple like like i built uh an automation in here two and a half years ago where what i wanted essentially was to when i found an article that was important when i was just reading the news on my phone I wanted to be able to hit the share button and have it go somewhere that triggered it.

26:51And I wound up running it through. I can't remember if it was Feedly or Interree or one of those. And it goes through there and then it grabs it, sends it to a Google Sheet, and then the AI would grab it and the AI would run it through the process I'd set up there and deposit the answers back. And I wanted to be able to do that from my phone at a red light, if so be it, if I chose. And I wanted to be able to hit share and be done and just know it'll be there in the morning. I love it. And it was really effective. That's awesome.

27:26Darin Patterson:Yeah, I mean, there are three ways I typically think about kicking off work. And so one of them, which is not the example you described, Corey, but one of them is like on a regular basis. And that regular basis might be every one minute or it might be every 10 minutes or every day I'm going to do something. So that's a pretty straightforward way of kicking off work on a routine schedule. So depending upon the software applications you're working with, working with obviously Google Docs here, it requires that. So every five minutes, this is kind of checking. And of course, I can kind of fine tune that as much as I want.

28:04Darin Patterson:but it's checking Google Docs to say, hey, are there any files that I have not yet processed? That's kind of a key phrase that actually kind of people might not always understand the nuance, but that I haven't yet processed that's in the ready for planning folder. So then I'm basically constantly pulling this to see if it's ready to go. So that's one way to kick off work. The second other way is slightly technical, but it's a webhook, but it's an instant. It basically means that other application has the ability to send information my way. And I might have wired this up to my e-commerce front end, and it's immediately going to send information to this scenario and kick off that agent and interact with it.

28:49Where some external action is essentially the trigger. That's exactly right. In the e-commerce scenario, it would be, you know, someone makes a purchase, for example. Makes a purchase, signs up for something. Precisely. Yeah. Okay. Okay. That's exactly it.

29:04Darin Patterson:And then the last one, and the super simple way to do what you were working on, Corey, is we actually have a concept of mail hooks. Basically, I can have a setup of magic email address that's like, hey, to-do list later. I just give it that contact in my phone, and then I would just send it to to-do list later every time it receives an email. This is kind of old school technology. We don't have the fax option, but we do have the email option. So if it kicks off based upon a new email to a particular address. That's all. I wouldn't be surprised with robotics if fax comes back in some way. I don't know how, but I could just see, like, you know, maybe the anti-AI people.

29:39I recently heard that's how you communicate with Dolly Parton. Yeah.

29:43Darin Patterson:What? That you fax Dolly Parton. Or that you receive faxes from her. Wow. That's cool. I don't remember where I heard that, but I remember being just floored that, wow, there is someone still using it. Well, we got some investment advice, I think, there from Grant. That was excellent. The people who got that. worth out of this podcast for sure. That's right. Facts, it's the next big thing. Yeah. Facts is the next big thing. My other question, and perhaps you can address this because you're going to show us the scenario of dealing with MCPs and the precision, is let's say I'm intimidated by all of this.

30:20Could I ask an AI agent to set one of these up for me, whether that's Chat2PT or maybe perhaps something inside make.

30:27Darin Patterson:Yeah. So you read our mind. So as we intro the show today, we talked a little bit about some of the things that we've released and we're really excited about over the last year. We recently had our customer conference in Munich, Germany and got to spend some time with just fantastic, amazing people from all over the world. And we unveiled one of our most exciting things that is in cooking as we speak. I am not ready to show it off today, but we launched Maya by Make. We're working on some select kind of interviews with some key customers to make sure that we nail this experience. And essentially, that's exactly as you describe it, is an in-product experience that allows me to chat with AI in order to build out these automations.

31:14Darin Patterson:And the one thing that I really want to emphasize about that, that I'm particularly excited, Sorry, I don't have the most amazing image on the screen. But Corey and Grant, I suspect this is true, but I don't know. Have you guys vibe coded before? Oh, yeah. Weekend vibe coded. We give it our best. Definitely. Definitely, definitely. Perfect. Some people get offended if I use the word vibe coding. So I hope that's not how you're doing. I am nowhere near close enough to a legitimate engineer to be offended by vibe coding. So you're all good. I resemble that remark as well. Well, the beauty of it, like for me is sort of it's the difference between like a one shot.

31:53Darin Patterson:And that's what you'll see a lot in products like this in other places. You'll see like I give it one prompt and let's let's just hope and pray it comes up with a fantastic scenario. That's amazing. Like that's not the real world that we live in. I've been doing this a while. I've never seen two businesses, even of the same shape or size that operate the same way. There's so much like universal creativity on how people operate a business. And so Maya by Make is actually highly interactive and is designed to very similar to that vibe coding experience where it's not just one shot. It's actually a continual interaction where it's going to ask you clarifying questions and build alongside you.

32:33Darin Patterson:So I'm particularly excited about that release coming soon. Ooh. That said, I will say, you know, today people, obviously, they'll go to Claude or they'll go to ChatGPT. They'll even, you know, create a workflow diagram on a napkin and upload it to Claude and say, create a make scenario for me and have pretty solid success. Yeah. That's not quite the experience that we totally aim for. And so we're working heavily on bringing that to market. Well, you know, something I feel like is really valuable here is like, like I would say Grant and I really probably started doing this in like an N8N kind of tool that's a little more technical, a little less user friendly.

33:13And as time has gone on, you know, we're getting more and more to a time where it's just going to be expected that employees are capable of building, deploying and maintaining their own agents. And that's just going to be a fact of life. And what I like about this is it's not intimidating. It's not scary. I love the idea of being able to build these with natural language. So I'm really excited to see Maya when it's ready. And I think that that kind of approach really can enable a bunch of people who right now might be scared to death of the idea of building automations. Or being automated. Or being automated.

33:59Because I think if you can build these types of automations, that makes you valuable because then you can scale how much you can do as an individual. Yeah, you're not going to automate yourself out of a job. You're going to automate yourself into one. Into more work.

34:12Darin Patterson:No doubt. I mean, I often theorize that, look, what I would describe as orchestrating AI, orchestrating agents. It's the new middle management. It's no longer the case that, hey, you're doing great. I'm going to promote you and you can hire some people, but you're doing great. I'm going to promote you and you can 10x your output by creating and managing an army of agents. Yes. I think that's 100 % true. Whether or not people like that remains to be seen. But I mean, certainly knowing the skills and knowing the tools will empower you in whatever role you're in. Yeah. So I agree 100%. I kept joking.

34:51Sam Altman told me I would have a fleet, but we would all have a fleet by the end of this year. But the truth is I have six and I use them religiously. But definitely by the end of next year, I can't imagine a scenario where just about everyone isn't dealing with a lot of these.

35:09Darin Patterson:Absolutely. It's a baseline expectation when you come to work now. Yeah. Yeah. This is a great demo, by the way. I think this is going to be incredibly valuable for people who have never used this before. Yeah. Yeah, we didn't even hit run, but yeah, I think it's good. I keep thinking of the people on our team who really love the colorful workflows in our project management tools that we use and stuff. I always struggle with all of the colors for whatever reason and often use a like grayscale thing on my screen to help. But so many people think better and are less intimidated with that approach.

35:49And I think what you've got here is really cool. That's a big compliment.

35:54Darin Patterson:I can tell you it was very intentional. I'm lucky enough to spend some time with our co-founders. Actually, here's an interesting insight. Our co-founder is based in Prague in the Czech Republic. and part of the inspiration for make is literally coming from the metro maps that you come from if you think about like a business process and you think about taking complexity of how to get from one part of a city to another the most effectively like literally what you're seeing here has a little bit of inspiration from those those metro maps that you might see i totally see it yeah that absolutely makes sense i'm thinking of like yeah if you're in london and you're on the tube you see these little lit up uh screens all over yeah absolutely and the lines are different colors like the purple line the pink line yeah i mean there's so much like subtle happening here like like the way that that's lighting up is very intentionally directional and yeah like you feel and that's exactly the word i use all the time you you feel powerful you feel connected um and it seems strange for software to be emotive but it is it is it is i agree Well, you talk about building an AI stack with four layers.

37:06That's data, models, interfaces, and speaking of orchestration, orchestration. Where do you find that most companies get stuck when they try to build this type of stack? Is it the data layer, model layer? Yeah. Yeah.

37:21Darin Patterson:So the one thing that I find when we think about that kind of very often people in the no-code world, they refer to the trifecta of no-code. So they think about data and very commonly tools like Airtable and even Monday, they store data in very flexible sorts of ways. And they think about interfaces and building on interfaces on top of that. And then they think about how do you automate and integrate between all those things. And what are the places where, so people come up with insane ideas on how to create incredible value for their organizations, both internally and externally, by combining the power of these different stacks.

38:02Darin Patterson:An incredibly flexible data source, incredibly flexible interfaces, and of course, automating everything in between. And the piece that we actually saw, and we started to see this several years ago, is when you start to, the great irony of got a fantastic business, you start to automate and no code every part of your business. It becomes difficult to rein in and understand what's happening and connects to all the various pieces. That was really the inspiration behind a concept called MakeGrid. And we're actually going to ignore the, well, these errors are intentional, so I should highlight that.

38:43Darin Patterson:And we actually might, actually, let's get rid of those for a moment. So, MakeGrid represents how this entire stack connects to everything. And so, what we found with Make, we were looking at a very specific process, like one process. But your business is way more than just one process. And so often you were looking at the tree, but missing the forest in the broader scheme of things. And so what MakeGrid is a unique, one-of-a-kind, automatically generated view of your entire automation landscape. Not only does it figure out how processes are connected to each other, but it actually identifies how key assets in your company are connected to different processes.

39:30The direction of data flow, too. Yeah, I love that.

39:34Darin Patterson:It's a beautiful thing. I noticed they're not all the same. That's what caught my eyes. That's exactly it. Not to put you on the spot, but is this real time? Because that would be really cool. So this is showing the direction of data. We've actually continually pushing on more and more layers that will put you to that kind of let you look at different, the volume of pieces over time. So there's not a lot of volume there, but if I look at actually, I could see the volume here. This is a very highly active workload. Fun fact, the process we're looking at right now, that's the conference registration system.

40:08Darin Patterson:Let's see. Ah, this is amazing. So we have a business process. So I can travel to Prague regularly. We're super lucky at Prague. We serve lunch to everybody there on Wednesdays and it's fantastic catered lunch. Like it's good stuff. But if people don't come, then we waste food. And if we don't make enough, then that's a problem as well. So a completely automated process every week on Slack, you get a notification and you respond with an emoji, like a meat sign or a vegetarian sign or like what you want. And then you get a QR code. It's a wholly automated process. And we're looking at that process.

40:46Darin Patterson:Like that process has lots of different steps. It has lots of different pieces. I can see the Slack channels that we're using to manage that and connect all those pieces together. But what's important here is I see these processes, how they all work together, but I also see dependencies. And so in this case, Airtable and this particular base in Airtable turns out to be really important for at least three of these business processes. And if I change something about that Airtable, and in fact, I can even drive in a little bit deeper here to understand all the different connections. These are the different attributes in that particular table and Airtable that they're dependent upon.

41:25Darin Patterson:Lake has a lot of insight into not just how your automations are working, but actually your whole tech stack and how it's all connected together. And that works for no-code things like Airtable, but it also works for enterprise things like Workday and NetSuite. So you can understand that these are critical assets that connect multiple processes together. That's awesome. Right. Now, is this connected to the idea of the precision that we had mentioned earlier, like in how this works? This particular piece is really about zooming out. I think of precision as zooming in a little bit where you're getting more precise with the scalpel.

42:01Yeah, yeah, yeah. And this is great because I don't know of any other tool that shows you at this higher level that you can visualize all your automations. Like I could imagine a CEO coming in and scrutinizing this. What are we doing? Like how can we do it more efficient? Like looking at the data flows, you know, how can we get rid of expensive subscriptions that we don't need anymore? Could one of these other services replace it?

42:26Darin Patterson:Yeah, you're absolutely right about that. If you're connecting core software through low-code automation, it makes it less reliant on that core software and you're able to not be vendor locked in for sure. Yeah, because you could say, oh, we can swap this vendor right here and replace all the automations still work if we switch it up. Amazing. Yeah, this is a one-of-a-kind experience. I can really kind of go deep on any one of these pieces, understand how the relationships work, and it's incredibly visual, and all updated in real time. Is there an easy way to know, like, here's everywhere I need to go make a change, or at least look up all of my automations connected to ClickUp, for example?

43:13Darin Patterson:Yeah, like, that is a little bit of what this is getting at. So I can see here, and so many people use a spreadsheet as a database. Yeah. Whether we agree that's a good idea or not, it happens all the time. It does. So this is, that spreadsheet is an asset. It's become an important asset for my business. And I can see exactly that spreadsheet. Of course, if I click on open spreadsheet, I can go directly to that real spreadsheet. I can see very clearly it's Google, it's spreadsheet, and I can identify all the different links that are connected to that particular spreadsheet. But we also have literally all the attributes that are in place.

43:50Darin Patterson:So literally there's a word, there's a column, column F, in that spreadsheet called classifications. And it's being referenced by these three scenarios. And so I do know not necessarily if somebody adds one, because it may not be relevant for these automations, but I do know if somebody needs to change this or modify it or delete it, or if we decide that we're moving from Google to Microsoft, it's going to be critical that I know where all this is connected and how to read a line-up. That makes sense. Yeah, that's exactly what I was looking for. I'm just curious. My theory is that over time, as we have more and more and more of these, we're also going to have more and more and more ways to break them if we're not careful.

44:32And I think a system like this is a really important part of building out agents, having that overhead sky down view of your automation situation, for lack of a better phrase.

44:51Darin Patterson:That's exactly where we're, you know, again, either lucky or prescient, I don't know. But if you think about what's necessary for AI agents to be successful in real production situations, you're going to need to know the relationship between AI agents and what scenarios, what tools they access, and be able to drill into that to understand exactly what they did at any point in time. And that's very much our focus there. Awesome. Here's a feature idea for you if you haven't done this already so if you have i'll be really impressed nice um how do you export all of this information to give it to an agent so that it understands how your company works is it possible to do it um i yeah i'll definitely categorize that in the bold ideas from from grant it's interesting fax machines agent agent maps yeah i gotta write these down that i don't have these days you're going to make literally some of the dollars.

45:46Yeah. I mean, there's an interesting relationship

45:52Darin Patterson:here. I don't know if you guys are aware, but so Make is actually, so we're very lucky because Make is managed as its own distinct brand, its own distinct customers, but we actually have a parent company called Salonis, which is a data mining company. It really helps companies map out and understand their actual business process based upon data that they collect. And of course, knowingly so. And yeah, so there's an interesting tie in a relationship there to help you understand what your actual business process is like. You think you have an accounts receivable process, but then there's the reality of the data and how it gets processed and it's different.

46:26Could we go look at the workflow again? I have a question. Yeah, you got it. Suppose I, so I know we're talking low code, no code, but suppose I have something that is just complex and it's a pain in the neck. And my only solution is I need to shove in a code node somewhere. Is there such a thing? Yeah.

46:51Darin Patterson:Why would you want to do that? No, I'm just kidding. I don't. I promise. But in the event that that was my best choice. So some of your listeners will perk right up. They'll be excited to see exactly this. and of course, I don't code, but Gemini does. So I'm actually incredibly proficient with including a make code module when you need it. And of course, again, code, one of the things that's great about arbitrary code, it can do anything. But of course, that means doing it in a secure way. And so we've invested heavily on enabling you at any point in your scenario to be able to execute, of course, the standard JavaScript or Python code.

47:33Darin Patterson:and to be able to dynamically send data to that as well. Yeah. Oh, that's awesome. That's awesome. That's awesome. It was just a thing that crossed my mind. I was like, you know, the goal is not to, but, you know, goals are what they are, and sometimes they don't happen. So if you needed to. But, you know, if you were dealing in snippets and stuff, the average person could absolutely go in and find their way through that using a chat GPT or a Gemini, Claude, whatever, and stumble through some small, like, I can't connect this to this. I need something there. And probably it'll tell you, you need to use this code I'm going to write you.

48:14That's exactly it. Yep. Okay, so we have a couple lightning round questions for you. Is there anything else you want to show us before we get to those?

48:21Darin Patterson:Man, I could do this all day. Maybe very briefly, we touched on MCP. so let's very briefly do a demo of what that looks like. That'd be great. Perfect. Yeah. Tell me. So this is totally made up and I can't spell neuron either. I don't even know how. Oh, it's fine. It'll know. The AI is smart. They're good at typos. Right. But I'm going to say, tell me about my customer. I'm going to pretend you're my customer. This is not true. But we all have imagination. So So I have Claude up here. We'll see if the demo gods have been properly satiated today. But I'm within Anthropic, and you'll notice that this is, I've already set up and kind of configured Make as my MCP server.

49:14Darin Patterson:So it's looking at various tools that it can utilize in order to effectively support my query and my request. Yeah. So from a technical perspective, I've actually set up a scenario that's designed in that way. And what's important to recognize is my scenario could be, you know, grabbing information from Salesforce and Freshdesk and the news for all that matters. Like it's everything. And it's going to call that one tool. I can actually see the inputs that it provided. I can see actually, you know, if I really want to geek out, I can see actually what it did do. But now I can talk about my customer in sort of a natural way.

49:50Darin Patterson:Like, I don't know any salespeople that want to log into their CRM. I can imagine that there's a world in which that doesn't exist anymore. Yeah. So can I. So it's a happy world. It's a happy world. A world where everything is nice. So, of course, pulling out information, that's not too difficult. And it's probably okay for it to be a sledgehammer. But when I get into more distinct business processes, I have a new customer to my CRM. And I'm going to say Starbucks. I just landed them. It's happy days. There you go. Oh, congrats. So that's a joke. Oh, I'm sorry. I was in on that. I don't know what you're talking about, Darren.

50:37Darin Patterson:I'll try to be less literal. I was like, hell yeah, go Darren. Yeah. Commission checks on the way. Um, so I added, I've done this as well. So you might say to yourself, well, why, why not just have like the HubSpot CR, the MCP server or whatever the case is. And so here you can see, I actually, I said, added CR Starbucks to my CRM. It says, okay, the website's that. Is that good? And I'll say, yeah, that's good. So at this point, it's got enough information that it feels confident it's going to go execute that tool. I'm going to show you what that tool looks like inside of Make and why there's power for the Make MCP server that's different from just what I would call the standard one.

51:19Darin Patterson:So by the way, I can click on it now. Great, awesome. So this is that very simple scenario. It's probably oversimplified. But in Make, when I create an MCP tool, basically a tool that's available to any AI, I get to define a few things about it. Actually, I get to define the description of it. So this is how it's going to be described to the AI. So the AI kind of knows how to use it. So I have control over that. I get to decide what inputs this tool expects. So it expects the name of the business. And you'll notice the business domain. And I just wrote a very simple phrase here. I said, always confirm the business domain by the user.

52:03Darin Patterson:So you can kind of think of that as a business rule. Like, so rather than just giving AI, here's all the possible fields in HubSpot and go for it. I've only said there's two. Maybe there's more in reality. I'm going to add a few more. But I've only said there's two here. And I've added in that this one's required. I always need the web address. And then, of course, I can go in here and I can add it to the CRM. So basically, I've got a wrapper around this MCP. Or if you think about it that way, I have total control over the inputs. I have total control of the outputs. You'll notice that the output I provided was the HubSpot URL exactly.

52:41Darin Patterson:So I get that back. What you're likely to get back from HubSpot's actual MCP, and HubSpot's an awesome partner, but you're likely to get back an ID. And then you're like, okay, cool. But here I have complete control to get precision. And then, of course, I can see every time this scenario is executed. So we haven't talked about it a lot, but I can geek out on this all day long. Every time these workflows get executed, knowing what actually happened, what was the inputs, what was the outputs is incredibly valuable, especially as you scale your business to be able to have that insight. Excellent.

53:12Darin Patterson:Totally. Yeah, that's really cool. That's awesome. I've never thought of this use case. So this is amazing. Awesome. Yeah. So we said in the beginning, Make has like 250 ,000 organizations using it, which is massive. What patterns are you seeing in how the most successful teams structure their automation and AI strategy? Like if I'm starting with Makelic, what should I be doing? Yeah. So the patterns that we're seeing and we're looking across all of our customers and understanding what resonates and who's most successful. One of the most distinct patterns that we see emerge is that we see different types of business functions where AI and automation is most likely to be deployed in and most likely to be successful in.

53:54Darin Patterson:So we're seeing incredible uptake in business functions like marketing. Maybe this is not a surprise, but the reason for that is in the world of AI, the things that matter a lot are high content and high context situations. And you really can't think of more high content and context situations than within marketing. And so we see a significant push from there. We see the same push on things like customer service as well, where, again, context is high and content is high. But one of the interesting insights I've had most recently is when I narrow it down and I look at some of our largest customers.

54:34Darin Patterson:So these are customers with many employees, many different departments and divisions that are thinking holistically about what I would call AI transformation strategy. I've been blown away. I didn't expect this at all. But in the data, I see significant uptake and also qualitative and quantitative within finance departments. I would have thought they were the last to really start to adopt these technologies. But I've talked to an amazing customer of ours that's a food delivery service, and they're literally using AI agents to pre-create journal ledger entries. It's been a long time in my career since I've created one of those.

55:12Darin Patterson:It's interesting to me. I think a lot of finance people, I would assume, would be stuck. I'm going to create it in the old-fashioned way, but they're happily and ready to take that information from the business and leverage AI to create those. It's been a fascinating trend. Awesome. I have a question that might even be a little corny, but I'm going to ask it anyways. What do you think is the single most impressive automation in Mank? Wow. It's a bit corny. It is very corny. It's very corny. I was just curious. If there's one that you're like, anytime I want to really impress somebody and rock their world, this is the coolest thing you could do.

55:58Darin Patterson:I'm going to lean into your corny. So I have a soft heart. So I'm going to lean in on that way. So I get the chance to work with all sorts of different types of organizations in the Pacific Northwest. I work with an organization that runs a pet adoption center. So this is a pet adoption center. It's not necessarily the big enterprise use case you think of, but it's like how make gets applied in the real world. And there's like one guy who's volunteering his time to help them set up. Because if you think about it, if you work at a pet adoption center, you don't like literally they have Salesforce to track people and track the adoptions process, et cetera.

56:36Darin Patterson:They don't want to spend a lot of time in Salesforce. And so literally simplifying that process, I was blown away that one of the like oldest school problems that make an AI does really well at is actually matching. So what I would call fuzzy matches. So within a pet adoptions, they actually have a do not adopt list. So they have people who, for whatever reasons in the past, have shown themselves not capable of successfully taking care of a pet. So the do not adopt list, it's name a person, maybe an address, phone number. And so if you think about that business process, so to speak, the business process is making sure that a person that comes in is not on that do not adopt list.

57:17Darin Patterson:Fuzzy matching is like a hard software problem to solve normally. Like, is this is Tom Thomas and Thomas Tom? Like, are these the same person? that's cool to solve and AI does a really good job at it. And so I'm leaning in on your fuzzy, warm billing. Those are the types of use cases that get me really excited. I can talk about big ROI ones for big companies all day long, but I love seeing stuff like that actually deployed in the real world. That's really cool. So where do you think MakeGrid and everything that you're building goes next? What becomes possible when everyone in a company can understand their automation landscape?

57:55Darin Patterson:Yeah, this is what I spend most of my time thinking about. So grid is a significant investment into not just fancy things that you saw on the screen before, but it's a significant investment into becoming not just understanding how that's set up, but becoming proactive at the macro level. So identifying where opportunities or challenges exist in your automation landscape and then responding to those. And maybe it gets at sort of the very big picture of make and how we think about it today, which is so automation at its core. Yes, it saves you time. That's absolutely true. But what makes design to do is to support agile businesses, basically businesses that can respond to a very fast technology environment or a competitive environment.

58:45Darin Patterson:And if we can help businesses do that and empower people that are closest to the business functions to be very responsive and innovative in the broader market, that's where all the value comes from. And that's what we're trying to serve with things like MakeGrid and even all the little things we do in the scenario designer to create that visibility so that you can not just automate it or forget about it, but it's actually continue to innovate and iterate on it in order to outlast your competition or do better things. That's really cool. Darren, thanks so much, man. Really appreciate you coming today, joining us and showing us how to build agents in Make.

59:24It's a really cool tool. Where can people go to learn more, especially like, you know, if they want to want to learn beyond what they saw here today?

59:36Darin Patterson:Yeah, fantastic. One of the best kept secrets is certainly our hands on training. That's a academy.make.com. So if you're just getting started, this amazing set of resources that are walking through and at whatever pace you seek and lots of amazing resources. is if you're more of like an executive decision maker and thinking, man, my company needs to really transform and change the way we operate and you want to know where to get started, you're not going to drive in and necessarily be operating the tool. I recommend checking out playbook.make.com. This is how you think about driving AI transformation in your company and where to get started.

1:00:14Great resources. Absolutely. We'll have links to all of that in the description below today's video. I want to thank everyone for watching. And if you haven't yet, please take just a moment to like and subscribe so we can keep bringing you these interviews and telling you about the cool tools and the interesting people in the space where we all live and work today. But that's it for today's episode. Farewell for now, humans. We'll see you next time.

1:00:51and introduce round Eagles social media to respectfully discuss major rivals