WEBVTT 00:00:00.000 --> 00:00:02.259 Imagine a world where building really complex 00:00:02.259 --> 00:00:04.240 business automations, well, it only takes a single 00:00:04.240 --> 00:00:06.339 sentence. You just type what you want, and minutes 00:00:06.339 --> 00:00:08.779 later, a robust system simply, well, it appears, 00:00:08.980 --> 00:00:12.160 ready to go. No deep coding, no endless debugging. 00:00:12.380 --> 00:00:14.259 Sounds almost too good to be true, doesn't it? 00:00:14.500 --> 00:00:16.719 Let's unpack this a bit. Welcome back to the 00:00:16.719 --> 00:00:18.780 Deep Dive. Today, we're plunging into something 00:00:18.780 --> 00:00:22.879 truly fascinating. a revolutionary new method 00:00:22.879 --> 00:00:25.899 that lets AI instantly build powerful multi -step 00:00:25.899 --> 00:00:27.539 automations for you. We're talking about combining 00:00:27.539 --> 00:00:29.859 advanced AI, specifically tools like Claude, 00:00:29.899 --> 00:00:31.800 with something really cutting edge called the 00:00:31.800 --> 00:00:36.009 Model Context Protocol or MCP. you know, another 00:00:36.009 --> 00:00:38.170 update. It feels like a seismic shift, a real 00:00:38.170 --> 00:00:40.969 game changer in the automation space. We're going 00:00:40.969 --> 00:00:43.350 to kick things off by exploring why current automation 00:00:43.350 --> 00:00:46.350 tools, powerful as they are, can often feel like 00:00:46.350 --> 00:00:49.109 a bit of a paradox, like incredibly capable yet 00:00:49.109 --> 00:00:52.539 strangely exhausting to really master. Then we'll 00:00:52.539 --> 00:00:54.539 peel back the layers, look at how this MCP method 00:00:54.539 --> 00:00:56.659 actually works. We'll peek under the hood at 00:00:56.659 --> 00:00:59.399 its impressive three -repository power system. 00:00:59.640 --> 00:01:01.340 We'll even walk you through a really compelling 00:01:01.340 --> 00:01:04.140 real -world example, show you its practical magic. 00:01:04.439 --> 00:01:06.579 Later, we'll sketch out how you could conceptually 00:01:06.579 --> 00:01:09.379 set this up yourself, explore its huge business 00:01:09.379 --> 00:01:11.519 applications, and then, importantly, we'll get 00:01:11.519 --> 00:01:14.760 real about its current limitations and the exciting 00:01:14.760 --> 00:01:17.469 possibilities ahead. Our mission, like always, 00:01:17.750 --> 00:01:20.310 is to give you that shortcut, help you get truly 00:01:20.310 --> 00:01:22.849 well -informed fast. Okay, let's start by digging 00:01:22.849 --> 00:01:24.829 into the problem this whole new approach is trying 00:01:24.829 --> 00:01:27.730 to solve. Our source material, it kicks off describing 00:01:27.730 --> 00:01:31.069 NEN as incredibly powerful. They even call it 00:01:31.069 --> 00:01:34.709 Zapier on steroids, which highlights its immense 00:01:34.709 --> 00:01:37.069 flexibility, hundreds of integrations, advanced 00:01:37.069 --> 00:01:39.689 logic. It really sounds like an automation dream 00:01:39.689 --> 00:01:42.420 tool on paper. And it absolutely is. I mean, 00:01:42.439 --> 00:01:45.159 it's a beast for power users, genuinely deserving 00:01:45.159 --> 00:01:47.459 all that hype. But here's the, well, the stark 00:01:47.459 --> 00:01:50.819 reality, the paradox, right? It's greatest strength, 00:01:51.000 --> 00:01:52.959 that incredible flexibility, the sheer number 00:01:52.959 --> 00:01:55.420 of options that also creates its biggest challenge. 00:01:55.939 --> 00:01:58.299 Navigating hundreds of different nodes, you know, 00:01:58.319 --> 00:02:00.299 those little building blocks, configuring them 00:02:00.299 --> 00:02:02.680 perfectly, getting them to work together, especially 00:02:02.680 --> 00:02:05.879 in a complicated workflow. It can be genuinely 00:02:05.879 --> 00:02:08.080 exhausting. You find yourself thinking, maybe 00:02:08.080 --> 00:02:09.979 just writing the code myself would actually be 00:02:09.979 --> 00:02:12.199 quicker. It's kind of like being given every 00:02:12.199 --> 00:02:15.000 single Lego brick imaginable, but zero instructions 00:02:15.000 --> 00:02:17.719 and then being told, go build a skyscraper. So 00:02:17.719 --> 00:02:20.580 it sounds like for all that potential, the real 00:02:20.580 --> 00:02:23.520 core struggle for users is just mastering that 00:02:23.520 --> 00:02:26.800 overwhelming complexity. It's a lot. Precisely. 00:02:26.800 --> 00:02:28.939 It's the sheer weight of learning. You know, 00:02:28.979 --> 00:02:31.639 that steep climb to really get it. And this brings 00:02:31.639 --> 00:02:34.539 us to where things get really interesting. Our 00:02:34.539 --> 00:02:36.780 source introduces the Model Context Protocol, 00:02:37.099 --> 00:02:40.740 MCP, as the key. The answer to bypassing that 00:02:40.740 --> 00:02:43.159 steep learning curve entirely. It promises to 00:02:43.159 --> 00:02:45.300 sort of transform the whole experience. That's 00:02:45.300 --> 00:02:47.699 the beauty of it, exactly. Yeah. Think of MCP 00:02:47.699 --> 00:02:49.560 not just as another feature, but as a really 00:02:49.560 --> 00:02:52.719 sophisticated system. It gives an AI like Cod 00:02:52.719 --> 00:02:55.939 this deep, structured, almost native understanding 00:02:55.939 --> 00:02:59.990 of a complex tool like any N. The old way, AI 00:02:59.990 --> 00:03:01.870 would often give you, well, inconsistent results, 00:03:01.969 --> 00:03:03.870 sometimes illogical ones, like it was just guessing 00:03:03.870 --> 00:03:06.669 or making vague connections. But this new MCP 00:03:06.669 --> 00:03:08.789 method, it completely changes that. It equips 00:03:08.789 --> 00:03:10.930 the AI with these highly specialized tools and 00:03:10.930 --> 00:03:13.750 gives it access to massive, meticulously structured 00:03:13.750 --> 00:03:16.569 libraries of information. So the AI doesn't guess 00:03:16.569 --> 00:03:18.289 anymore. It builds, builds with the confidence 00:03:18.289 --> 00:03:20.479 and the precision of a seasoned expert. OK, that 00:03:20.479 --> 00:03:23.180 level of precision, that expert like knowledge 00:03:23.180 --> 00:03:26.400 sounds incredible. And you said it comes from 00:03:26.400 --> 00:03:28.539 what the source calls the three repository power 00:03:28.539 --> 00:03:31.180 system. Can you walk us through those? What are 00:03:31.180 --> 00:03:32.759 they? How do they work together? Absolutely. 00:03:33.060 --> 00:03:35.460 Imagine giving the A .I. not just one brain, 00:03:35.560 --> 00:03:39.490 but like three distinct, powerful yet. perfectly 00:03:39.490 --> 00:03:42.110 interconnected intelligence centers. First, you've 00:03:42.110 --> 00:03:45.129 got the NAN MCP server repository. This is like 00:03:45.129 --> 00:03:47.509 the AI's core blueprint library. It contains 00:03:47.509 --> 00:03:50.469 complete structure documentation for over 525 00:03:50.469 --> 00:03:53.909 different NAN nodes. So the AI knows how each 00:03:53.909 --> 00:03:55.710 building block should be set up, how it's used 00:03:55.710 --> 00:03:57.849 correctly, down to every little parameter. It's 00:03:57.849 --> 00:04:00.090 the ultimate instruction manual, basically. A 00:04:00.090 --> 00:04:02.409 complete guide for every single component. Wow. 00:04:02.629 --> 00:04:05.729 Exactly. Then second, there's the Context 7 repository. 00:04:05.930 --> 00:04:08.389 This acts like the AI's live fact checker. It's 00:04:08.389 --> 00:04:10.949 dynamic memory, if you will. Software documentation 00:04:10.949 --> 00:04:13.610 changes all the time, right? This repository 00:04:13.610 --> 00:04:15.349 makes sure the AI always has the most up -to 00:04:15.349 --> 00:04:17.889 -date version -specific documentation. And this 00:04:17.889 --> 00:04:19.910 is crucial because it actively prevents errors 00:04:19.910 --> 00:04:22.610 you might get from using outdated info. Keeps 00:04:22.610 --> 00:04:25.959 the AI's knowledge fresh, relevant. So it's constantly 00:04:25.959 --> 00:04:28.540 checking its understanding against the latest 00:04:28.540 --> 00:04:30.639 information out there. Precisely. And third, 00:04:30.860 --> 00:04:33.079 and this is maybe the most powerful, most fascinating 00:04:33.079 --> 00:04:35.879 part, is the workflow reference repository. This 00:04:35.879 --> 00:04:39.319 is like a massive case study archive, a treasure 00:04:39.319 --> 00:04:42.639 trove of practical wisdom. It holds over 2000 00:04:42.639 --> 00:04:47.000 proven real world MEN workflows. So when you 00:04:47.000 --> 00:04:49.699 ask for an automation, the AI can actually look 00:04:49.699 --> 00:04:52.319 through these and find similar battle tested 00:04:52.319 --> 00:04:54.629 solutions to use as a starting point. Moment 00:04:54.629 --> 00:04:57.029 of wonder, just whoa. Pause for a second and 00:04:57.029 --> 00:04:59.889 think about that. Over 2 ,000 successful automations, 00:04:59.889 --> 00:05:02.209 all documented, all available. It's like having 00:05:02.209 --> 00:05:04.189 the collective wisdom, the direct experience 00:05:04.189 --> 00:05:06.889 of thousands of expert builders right at your 00:05:06.889 --> 00:05:09.649 fingertips. ready to instantly apply it's truly 00:05:09.649 --> 00:05:11.370 astounding the kind of capability that gives 00:05:11.370 --> 00:05:13.189 it it sounds like having yeah a whole team of 00:05:13.189 --> 00:05:15.509 master builders on call this huge amount of knowledge 00:05:15.509 --> 00:05:17.870 fundamentally changes how the ai approaches a 00:05:17.870 --> 00:05:20.269 task it moves it way beyond just guesswork it 00:05:20.269 --> 00:05:22.970 absolutely does the ai now builds with real deep 00:05:22.970 --> 00:05:25.029 expert knowledge not just you know speculation 00:05:25.029 --> 00:05:28.069 that's a really powerful foundation so okay with 00:05:28.069 --> 00:05:31.860 this mcp system in place How does a simple request, 00:05:32.160 --> 00:05:34.699 like in natural language from a user, actually 00:05:34.699 --> 00:05:37.600 turn into a fully functional, production -ready 00:05:37.600 --> 00:05:40.199 workflow? What are the steps? It's actually a 00:05:40.199 --> 00:05:43.399 pretty elegant four -step dance, almost seamless 00:05:43.399 --> 00:05:46.420 in how quickly it happens. First, you provide 00:05:46.420 --> 00:05:48.620 a simple description. It all starts with you. 00:05:48.819 --> 00:05:51.560 You give the AI, maybe in Cloud or a code editor 00:05:51.560 --> 00:05:53.699 like Cursor, just a natural language description 00:05:53.699 --> 00:05:55.519 of what you need the automation to do. Clear, 00:05:55.639 --> 00:05:58.769 direct. Second. The AI performs intelligent research. 00:05:59.129 --> 00:06:01.170 Now this is where the magic really starts humming. 00:06:01.389 --> 00:06:03.990 The AI doesn't just start building blindly, it 00:06:03.990 --> 00:06:06.910 does its homework. Real homework. It intelligently 00:06:06.910 --> 00:06:09.129 accesses those three repositories we just talked 00:06:09.129 --> 00:06:11.810 about, grabs the relevant up -to -date docs from 00:06:11.810 --> 00:06:14.509 the Blueprint and the Fact Checker, and crucially, 00:06:14.589 --> 00:06:17.490 searches that massive library of 2 ,000 tested 00:06:17.490 --> 00:06:20.769 workflows to find good examples. This deep research 00:06:20.769 --> 00:06:23.230 helps it find the perfect nodes, the most efficient 00:06:23.230 --> 00:06:25.509 structure for what you asked for. It's like it's 00:06:25.509 --> 00:06:27.430 reviewing every relevant textbook and case study 00:06:27.430 --> 00:06:30.600 before drawing the first line. Third. The AI 00:06:30.600 --> 00:06:33.480 builds and validates the workflow. Armed with 00:06:33.480 --> 00:06:35.720 all this expert knowledge, it then constructs 00:06:35.720 --> 00:06:39.800 the workflow's JSON structure. Beat. JSON structure, 00:06:39.980 --> 00:06:41.620 for everyone not familiar, that's just a standard 00:06:41.620 --> 00:06:44.879 text -based format for representing data. Super 00:06:44.879 --> 00:06:47.420 common online. And every decision it makes is 00:06:47.420 --> 00:06:50.319 based on that validated documentation. It uses 00:06:50.319 --> 00:06:52.519 built -in tools to pre -validate configurations, 00:06:53.040 --> 00:06:54.959 catching potential errors before deployment, 00:06:55.079 --> 00:06:58.279 like a self -correcting blueprint. Fourth. The 00:06:58.279 --> 00:07:01.500 AI deploys and manages the workflow. Once it's 00:07:01.500 --> 00:07:04.300 built and validated, the AI uses its tools to 00:07:04.300 --> 00:07:06.339 upload it directly into your AnnieNan workspace. 00:07:06.759 --> 00:07:08.620 It does a final check to make sure everything's 00:07:08.620 --> 00:07:11.240 integrated correctly, ready to run. That automated 00:07:11.240 --> 00:07:13.699 validation part sounds like a huge differentiator. 00:07:13.740 --> 00:07:15.779 What's the real advantage there for someone using 00:07:15.779 --> 00:07:18.220 this, knowing that Step is built in? The real 00:07:18.220 --> 00:07:20.439 advantage is catching errors proactively. It 00:07:20.439 --> 00:07:22.660 means you deploy with way less risk, ensuring 00:07:22.660 --> 00:07:25.060 flawless operation right from the get -go, less 00:07:25.060 --> 00:07:27.790 debugging later. Right. To really make this concrete, 00:07:28.050 --> 00:07:30.529 the source gives a great real world example, 00:07:30.790 --> 00:07:33.670 a digital marketing agency wanting to automate 00:07:33.670 --> 00:07:36.170 its client onboarding. Could you walk us through 00:07:36.170 --> 00:07:39.250 that, the prompt and what the AI actually built? 00:07:39.550 --> 00:07:41.509 Yeah, absolutely. It's a good one. The prompt 00:07:41.509 --> 00:07:42.949 they gave was pretty straightforward. Something 00:07:42.949 --> 00:07:46.009 like automate our client onboarding process using 00:07:46.009 --> 00:07:49.430 HubSpot, Asana, Google Workspace and Slack. And 00:07:49.430 --> 00:07:53.189 the AI using this MCP system took that one sentence. 00:07:53.579 --> 00:07:55.639 and did an astonishing amount of work automatically. 00:07:56.300 --> 00:07:59.120 It used all three documentation sources to understand 00:07:59.120 --> 00:08:01.620 not just the individual HubSpot, Asana, Google 00:08:01.620 --> 00:08:04.100 Docs, Slack nodes, but also how they typically 00:08:04.100 --> 00:08:06.959 interact in these kinds of flows. Then it meticulously 00:08:06.959 --> 00:08:09.439 mapped out this logical multi -step workflow, 00:08:09.720 --> 00:08:11.980 triggered exactly when a HubSpot deal was marked 00:08:11.980 --> 00:08:14.560 closed one. It found the specific nodes needed, 00:08:14.720 --> 00:08:16.600 checked they were compatible, and then created 00:08:16.600 --> 00:08:19.079 the whole complex multi -step workflow. Okay, 00:08:19.100 --> 00:08:20.939 and the result? The outcome was pretty incredible, 00:08:21.060 --> 00:08:23.819 actually. A fully functional multi -agent workflow 00:08:23.819 --> 00:08:26.639 literally just appeared in the user's Anet in 00:08:26.639 --> 00:08:29.079 account. This thing automatically created an 00:08:29.079 --> 00:08:31.720 Asana project from a template, generated a Google 00:08:31.720 --> 00:08:34.019 Drive client folder and a personalized welcome 00:08:34.019 --> 00:08:36.700 packet, set up the initial tasks in Asana for 00:08:36.700 --> 00:08:39.220 the team, notified the right Slack channels about 00:08:39.220 --> 00:08:41.639 the new client, and even updated the HubSpot 00:08:41.639 --> 00:08:44.139 client record with the new project details. The 00:08:44.139 --> 00:08:47.019 only manual part. the human touch, was connecting 00:08:47.019 --> 00:08:49.840 the API credentials securely. An API credential, 00:08:50.080 --> 00:08:52.440 just simply put, it's like a secure digital key. 00:08:52.679 --> 00:08:54.879 Let's one program talk to another safely. So 00:08:54.879 --> 00:08:57.820 this really complex multi -platform setup just 00:08:57.820 --> 00:09:00.100 materialized in there in an account almost instantly. 00:09:00.240 --> 00:09:02.100 That's quite a leap forward. Nearly instantly, 00:09:02.240 --> 00:09:04.840 yeah. With astonishingly minimal human effort. 00:09:05.129 --> 00:09:06.970 beyond that first prompt and plugging in the 00:09:06.970 --> 00:09:09.450 keys. It really redefines plug and play for these 00:09:09.450 --> 00:09:12.230 kinds of complex systems. Midroll sponsor Reed 00:09:12.230 --> 00:09:14.570 provided separately. This sounds genuinely transformative, 00:09:14.870 --> 00:09:17.870 really powerful. For listeners who might be curious 00:09:17.870 --> 00:09:20.830 now, thinking about the next step, how does someone 00:09:20.830 --> 00:09:23.990 even begin to set up a system like this conceptually? 00:09:24.029 --> 00:09:25.929 We're not doing a full tutorial here, obviously, 00:09:25.950 --> 00:09:28.470 but what are the key phases involved? Good question. 00:09:28.509 --> 00:09:31.090 It's a natural next thought, right? The source 00:09:31.090 --> 00:09:33.490 breaks it down pretty cleanly into two distinct 00:09:33.490 --> 00:09:36.149 phases. First, there's phase one, the basic MCP 00:09:36.149 --> 00:09:39.409 setup. This basically gives your AI, like Claude, 00:09:39.450 --> 00:09:42.549 read -only access to all that knowledge. You 00:09:42.549 --> 00:09:44.929 essentially configure Claude by feeding it a 00:09:44.929 --> 00:09:48.470 single unified JSON file. Beat a JSON file. Again, 00:09:48.529 --> 00:09:50.950 just a common organized text format for data. 00:09:51.259 --> 00:09:53.899 like a structured recipe. This file is the instruction 00:09:53.899 --> 00:09:56.279 set. It tells Claude exactly where to find those 00:09:56.279 --> 00:09:58.860 three repositories we talked about, the N18 node 00:09:58.860 --> 00:10:01.019 docs, the live fact checker, and that treasure 00:10:01.019 --> 00:10:03.600 trove of real -world examples. Once that's done, 00:10:03.700 --> 00:10:05.419 Claude gets incredibly accurate at designing 00:10:05.419 --> 00:10:07.740 workflows. It'll give you the complete JSON code, 00:10:07.899 --> 00:10:10.059 and you just manually import that into your N18 00:10:10.059 --> 00:10:12.059 setup. Okay, so it gives you the perfect blueprint, 00:10:12.120 --> 00:10:13.940 and you just drop it in yourself. Makes sense. 00:10:14.159 --> 00:10:16.879 Exactly. Then you move to phase two, advanced 00:10:16.879 --> 00:10:19.549 integration. This is where you unlock the direct 00:10:19.549 --> 00:10:21.809 NA10 deployment, meaning the AI can actually 00:10:21.809 --> 00:10:23.789 push the workflows straight into your system 00:10:23.789 --> 00:10:27.129 for you. This phase involves using Docker. Docker 00:10:27.129 --> 00:10:29.769 is a fantastic tool. packages applications in 00:10:29.769 --> 00:10:31.990 these isolated environments, like little self 00:10:31.990 --> 00:10:34.169 -contained computers. You use Docker to run a 00:10:34.169 --> 00:10:36.149 local server that acts as a secure bridge between 00:10:36.149 --> 00:10:38.730 Cloud and your NNN account. You pull a specific 00:10:38.730 --> 00:10:41.409 Docker image, run it, then update Cloud's config 00:10:41.409 --> 00:10:44.429 with your NNN URL and a unique API key that you 00:10:44.429 --> 00:10:47.330 generate inside your NNN settings. This gives 00:10:47.330 --> 00:10:50.750 the AI the necessary secure permission to connect 00:10:50.750 --> 00:10:53.649 and actually modify your NNN workspace. Beat. 00:10:54.209 --> 00:10:56.190 I'll admit, I still occasionally wrestle with 00:10:56.190 --> 00:10:58.149 making sure all the correct API keys and permissions 00:10:58.149 --> 00:11:00.289 are perfectly in place myself. It's, you know, 00:11:00.309 --> 00:11:02.490 a critical security step, obviously, but sometimes 00:11:02.490 --> 00:11:04.610 it can trip you up. A misplaced character, an 00:11:04.610 --> 00:11:07.450 expired token. It happens. Yeah, it sounds like 00:11:07.450 --> 00:11:09.309 for anyone looking to actually get this running, 00:11:09.470 --> 00:11:11.149 aligning all those security credentials correctly 00:11:11.149 --> 00:11:14.269 is maybe the main hurdle. It absolutely is. Getting 00:11:14.269 --> 00:11:16.049 all the security credentials lined up perfectly 00:11:16.049 --> 00:11:17.909 is often the trickiest part of the whole setup. 00:11:18.240 --> 00:11:20.659 Okay, now that we have a clearer picture of the 00:11:20.659 --> 00:11:23.960 how, let's pivot back to the what. Where can 00:11:23.960 --> 00:11:26.139 this incredibly powerful automation building 00:11:26.139 --> 00:11:28.299 be applied in the real business world? Seems 00:11:28.299 --> 00:11:30.399 like the possibilities are pretty broad. They 00:11:30.399 --> 00:11:33.419 truly are. This tech isn't just for one industry. 00:11:33.500 --> 00:11:36.940 Its power is in automating any structured, repeatable 00:11:36.940 --> 00:11:40.559 process. Think about a law firm, right? Imagine 00:11:40.559 --> 00:11:44.159 AI -driven client intake. New client data triggers 00:11:44.159 --> 00:11:46.559 drafting engagement letters, sets up tasks in 00:11:46.559 --> 00:11:49.220 Asana or Clio, starts automated communication. 00:11:49.659 --> 00:11:52.340 Or for a marketing agency, beyond that onboarding 00:11:52.340 --> 00:11:54.259 example, it could automatically create dedicated 00:11:54.259 --> 00:11:56.960 Slack channels for new clients, send out detailed 00:11:56.960 --> 00:11:59.399 onboarding questionnaires, or even pull daily 00:11:59.399 --> 00:12:02.240 PPC performance data from Google Ads, format 00:12:02.240 --> 00:12:04.379 it into a neat summary, and post it to a team 00:12:04.379 --> 00:12:06.759 dashboard or Slack. All before anyone's had their 00:12:06.759 --> 00:12:09.200 first coffee. It's really about leveraging this 00:12:09.200 --> 00:12:11.240 intelligence wherever there's a predictable flow 00:12:11.240 --> 00:12:14.240 of info or tasks. So it's about identifying those 00:12:14.240 --> 00:12:16.600 routine high -volume tasks and letting the AI 00:12:16.600 --> 00:12:19.519 handle the heavy lifting. Makes sense. And to 00:12:19.519 --> 00:12:21.539 get the absolute best out of it, what are some 00:12:21.539 --> 00:12:24.379 of the pro tips the source suggests? Yeah, the 00:12:24.379 --> 00:12:26.399 source offers some really good practical advice 00:12:26.399 --> 00:12:29.840 for getting maximum results. First, be extremely 00:12:29.840 --> 00:12:32.679 specific in your prompts. The AI is brilliant, 00:12:32.820 --> 00:12:34.940 but it's very literal. So instead of something 00:12:34.940 --> 00:12:37.440 vague like build me an automation for new clients, 00:12:37.559 --> 00:12:39.799 you need crystal clear instructions. Try something 00:12:39.799 --> 00:12:42.559 like create a workflow. Trigger. HubSpot deal 00:12:42.559 --> 00:12:45.460 stage changes to close one. Action one. Create 00:12:45.460 --> 00:12:48.259 new Asana project using new client onboarding 00:12:48.259 --> 00:12:51.320 template. Action two. Generate Google Doc welcome 00:12:51.320 --> 00:12:53.740 packet from template. Merge client name. Action 00:12:53.740 --> 00:12:56.080 three. Send Slack notification to hashtag new 00:12:56.080 --> 00:12:58.490 clients with deal details. See, more details 00:12:58.490 --> 00:13:00.889 mean less guessing for the AI. Much better results 00:13:00.889 --> 00:13:03.389 for you. Much clearer. Second, use a project 00:13:03.389 --> 00:13:05.690 template in Claude. Don't start from a blank 00:13:05.690 --> 00:13:07.990 chat every time. Create a persistent project 00:13:07.990 --> 00:13:09.769 with some permanent instructions for the AI. 00:13:09.990 --> 00:13:13.269 Like, you are an expert in ADAN automation architect. 00:13:13.690 --> 00:13:15.889 Always start by analyzing the trigger. Gives 00:13:15.889 --> 00:13:18.769 it a consistent starting point. Also, monitor 00:13:18.769 --> 00:13:22.169 usage carefully. This kind of powerful AI generation 00:13:22.169 --> 00:13:24.490 can eat up credits on platforms like Claude. 00:13:24.590 --> 00:13:27.659 So plan your query strategically. Maybe bundle 00:13:27.659 --> 00:13:30.139 smaller requests into one big one. Another crucial 00:13:30.139 --> 00:13:33.340 tip, plan your automations. Sketch it out on 00:13:33.340 --> 00:13:35.340 paper or in your head before you start writing 00:13:35.340 --> 00:13:37.779 the prompt. Clarifies your thinking. Good advice. 00:13:38.159 --> 00:13:40.639 And related to that, capitalize on each query. 00:13:41.039 --> 00:13:43.480 Try to get as much done as possible in one detailed 00:13:43.480 --> 00:13:45.700 prompt instead of lots of back and forth for 00:13:45.700 --> 00:13:48.120 every little step. Finally, and this is key, 00:13:48.279 --> 00:13:51.539 start simple, then scale up. Don't try building 00:13:51.539 --> 00:13:54.139 a massive 20 -step system first time out. Start 00:13:54.139 --> 00:13:56.759 basic. Two, three steps. New Google Sheet row. 00:13:56.919 --> 00:14:00.019 Send Slack message. Get comfy. Then build complexity. 00:14:00.379 --> 00:14:02.679 That's excellent practical advice. If you had 00:14:02.679 --> 00:14:04.639 to pick just one, what's the single most important 00:14:04.639 --> 00:14:07.080 tip for success with this? Oh, without a doubt, 00:14:07.179 --> 00:14:10.360 specificity in your prompts. Every single time. 00:14:10.809 --> 00:14:13.450 Clarity is king. Right. It sounds almost magical, 00:14:13.450 --> 00:14:15.490 honestly, but let's ground this with a reality 00:14:15.490 --> 00:14:18.149 check. What parts does this system handle incredibly 00:14:18.149 --> 00:14:21.250 well and what still absolutely needs that human 00:14:21.250 --> 00:14:22.669 touch? Yeah, that's an important distinction. 00:14:22.929 --> 00:14:25.549 It's amazing at generating the complete workflow 00:14:25.549 --> 00:14:28.730 JSON, selecting the right nodes, building complex 00:14:28.730 --> 00:14:32.230 logic, validating it all. That's huge. But what 00:14:32.230 --> 00:14:35.190 still needs you? First, API credential setup. 00:14:35.350 --> 00:14:38.279 You will always... Always manually add your API 00:14:38.279 --> 00:14:41.059 keys for security. The AI won't handle those 00:14:41.059 --> 00:14:43.899 sensitive keys, nor should it. Second, custom 00:14:43.899 --> 00:14:46.259 business logic. If you have really specific, 00:14:46.399 --> 00:14:49.580 intricate formulas, unique decision trees, very 00:14:49.580 --> 00:14:52.279 nuanced if -then stuff, you might still need 00:14:52.279 --> 00:14:55.179 to manually tweak the code or IF nodes after 00:14:55.179 --> 00:14:58.019 the AI generates the main structure. And always, 00:14:58.100 --> 00:15:00.519 final testing. You should always, without fail, 00:15:00.779 --> 00:15:03.120 do an end -to -end test before activating any 00:15:03.120 --> 00:15:05.629 workflow for real use. Think of the AI as your 00:15:05.629 --> 00:15:07.370 master architect, but you're still the final 00:15:07.370 --> 00:15:10.330 inspector, the QA engineer. Cost is also, you 00:15:10.330 --> 00:15:12.889 know, a factor to keep in mind. Generating really 00:15:12.889 --> 00:15:15.389 complex workflows can be credit intensive on 00:15:15.389 --> 00:15:18.029 Claude, and its usage credits often reset every 00:15:18.029 --> 00:15:21.250 four, eight hours, even on pro plans. So planning 00:15:21.250 --> 00:15:23.250 your big queries around that cycle can be smart. 00:15:23.600 --> 00:15:26.080 And, you know, while we focus on Claude here, 00:15:26.220 --> 00:15:28.700 the MCP method isn't exclusive to it. You can 00:15:28.700 --> 00:15:30.779 adapt it, use other AI dev environments like 00:15:30.779 --> 00:15:33.440 Cursor, Windsurf, even LM Studio if you want 00:15:33.440 --> 00:15:35.820 to run things locally for privacy. So it's incredibly 00:15:35.820 --> 00:15:38.620 powerful, automates so much, but it's not quite 00:15:38.620 --> 00:15:41.159 set it and forget it just yet. Human oversight 00:15:41.159 --> 00:15:43.159 is still critical. Precisely. Human oversight 00:15:43.159 --> 00:15:45.980 remains key for security, for that final fine 00:15:45.980 --> 00:15:48.809 tuning and for ultimate peace of mind. OK, beyond 00:15:48.809 --> 00:15:51.210 just making existing things easier, what new 00:15:51.210 --> 00:15:53.070 business opportunities does this tech really 00:15:53.070 --> 00:15:55.210 create? How does it reshape the landscape for 00:15:55.210 --> 00:15:57.490 entrepreneurs or existing businesses? Yeah, this 00:15:57.490 --> 00:15:59.970 isn't just about small efficiency gains. It's 00:15:59.970 --> 00:16:02.830 about unlocking entirely new competitive edges, 00:16:02.970 --> 00:16:05.870 new business models even. Imagine automation 00:16:05.870 --> 00:16:08.990 consultancy. You can now offer rapid prototyping, 00:16:09.070 --> 00:16:11.789 build custom workflows at a speed and cost that 00:16:11.789 --> 00:16:14.830 was just impossible before. Delivering high quality 00:16:14.830 --> 00:16:17.909 validated automations in a fraction. of the traditional 00:16:17.909 --> 00:16:20.970 dev time. That's a massive differentiator. Huge. 00:16:21.250 --> 00:16:23.450 Or think about a workflow template marketplace. 00:16:23.830 --> 00:16:26.549 You could build and sell industry -specific templates 00:16:26.549 --> 00:16:29.389 like a new real estate client onboarding package 00:16:29.389 --> 00:16:33.330 or a healthcare patient intake workflow, all 00:16:33.330 --> 00:16:35.590 pre -built with AI help ready to plug in. And 00:16:35.590 --> 00:16:37.570 of course, training and education. As this gets 00:16:37.570 --> 00:16:39.450 more mainstream, there's going to be massive 00:16:39.450 --> 00:16:41.250 demand for people who can actually teach others 00:16:41.250 --> 00:16:43.210 how to use it effectively. Makes sense. This 00:16:43.210 --> 00:16:45.570 really is a fundamental shift in how complex 00:16:45.570 --> 00:16:49.269 automations get built. The old way. Long, manual, 00:16:49.389 --> 00:16:52.309 often expensive, a real bottleneck. The new MCP 00:16:52.309 --> 00:16:55.460 method. It flits that script. Now, a business 00:16:55.460 --> 00:16:57.440 expert can just describe their desired outcome, 00:16:57.600 --> 00:17:00.639 you know, in plain English. And the AI acts like 00:17:00.639 --> 00:17:02.720 the master developer. It researches, generates, 00:17:03.019 --> 00:17:04.859 validates, deploys a production -ready workflow 00:17:04.859 --> 00:17:08.980 in minutes, not days or weeks. Minutes. That's 00:17:08.980 --> 00:17:11.180 a huge shift. So how does this fundamentally 00:17:11.180 --> 00:17:14.180 change the actual job of an automation developer 00:17:14.180 --> 00:17:17.539 or a solutions architect? It transforms it. Less 00:17:17.539 --> 00:17:20.000 manual building, much more strategic prompting, 00:17:20.019 --> 00:17:22.299 design, and oversight. Less hands -on keyboard 00:17:22.299 --> 00:17:24.519 time clicking nodes, more high -level thinking 00:17:24.519 --> 00:17:26.779 about the best way to instruct the AI. Interesting. 00:17:26.900 --> 00:17:29.200 So to bring it all together then, the model context 00:17:29.200 --> 00:17:31.359 protocol, when you combine it with powerful, 00:17:31.460 --> 00:17:34.240 knowledgeable AIs like Claude, it's radically 00:17:34.240 --> 00:17:36.519 simplifying how we create these really complex 00:17:36.519 --> 00:17:38.900 automations in platforms like NENN. It's truly 00:17:38.900 --> 00:17:41.000 democratizing what used to be a very specialized 00:17:41.000 --> 00:17:43.220 skill. Yeah, that's exactly it. It's all about 00:17:43.220 --> 00:17:45.799 making powerful automation accessible to everyone. 00:17:46.250 --> 00:17:48.890 For beginners, it's a direct shortcut to building 00:17:48.890 --> 00:17:51.210 professional -level automations that were just 00:17:51.210 --> 00:17:54.210 out of reach before. For the experts, it turns 00:17:54.210 --> 00:17:56.970 into this massive productivity multiplier, turns 00:17:56.970 --> 00:18:00.069 hours of tedious work into, well, minutes. And 00:18:00.069 --> 00:18:01.910 for agencies, for businesses, it's a profound 00:18:01.910 --> 00:18:04.089 new competitive advantage, lets them deliver 00:18:04.089 --> 00:18:06.630 higher -quality, more robust results way faster 00:18:06.630 --> 00:18:09.829 than ever before. Okay. Feeling inspired to maybe 00:18:09.829 --> 00:18:11.829 explore this transformative power for yourself, 00:18:12.170 --> 00:18:14.809 our source lays out a pretty clear, actionable 00:18:14.809 --> 00:18:17.930 plan to get started. First, they suggest joining 00:18:17.930 --> 00:18:20.250 an online community focused on AI automation. 00:18:20.730 --> 00:18:23.450 These places are invaluable for learning, support, 00:18:23.710 --> 00:18:27.150 sharing tips. Second, start simple. Get that 00:18:27.150 --> 00:18:30.369 basic read -only MCP configuration set up first. 00:18:30.549 --> 00:18:33.730 Then challenge yourself. Generate a really simple 00:18:33.730 --> 00:18:36.130 one -charger, one -action workflow. Just build 00:18:36.130 --> 00:18:39.339 that initial confidence. Third, scale gradually. 00:18:39.599 --> 00:18:41.140 Once you're comfortable, move to multi -step 00:18:41.140 --> 00:18:43.700 automations. Experiment with more complex logic, 00:18:43.859 --> 00:18:46.299 different nodes. And finally, document and share. 00:18:46.460 --> 00:18:48.740 Keep track of what works, what doesn't. Share 00:18:48.740 --> 00:18:50.740 your successful prompts, your solutions with 00:18:50.740 --> 00:18:53.440 others in those communities. The barrier to building 00:18:53.440 --> 00:18:55.759 really powerful business transforming automations, 00:18:55.779 --> 00:18:57.980 it has just dropped to nearly zero. The question 00:18:57.980 --> 00:19:00.660 isn't if you should learn this method, it's what 00:19:00.660 --> 00:19:02.819 will you build first? Think about the possibilities. 00:19:03.200 --> 00:19:05.559 Really open things up. Thanks for diving deep 00:19:05.559 --> 00:19:07.140 with us today. We'll catch you on the next deep 00:19:07.140 --> 00:19:10.779 dive. Outero music fades in, then fades out.