WEBVTT 00:00:00.000 --> 00:00:02.339 Imagine that feeling of just being overwhelmed, 00:00:03.180 --> 00:00:06.839 drowning in this endless flow of articles, research 00:00:06.839 --> 00:00:09.480 papers, data. You're always trying to keep up. 00:00:09.759 --> 00:00:13.099 Yeah, it's a lot. But what if you had like a 00:00:13.099 --> 00:00:16.219 tireless digital assistant delivering synthesized 00:00:16.219 --> 00:00:19.039 reports, you know, around the clock in minutes? 00:00:19.559 --> 00:00:22.280 And here's the kicker. without writing a single 00:00:22.280 --> 00:00:25.199 line of code. No, seriously. Welcome to the Deep 00:00:25.199 --> 00:00:27.780 Dive. Today we're really unpacking the world 00:00:27.780 --> 00:00:30.160 of autonomous AI research agents. This is where 00:00:30.160 --> 00:00:33.520 the future of information work gets truly interesting. 00:00:33.880 --> 00:00:35.420 And we've got a fantastic guide for you showing 00:00:35.420 --> 00:00:38.240 exactly how to build one using these accessible... 00:00:38.920 --> 00:00:42.679 no code tools will explore its fundamental architecture, 00:00:43.100 --> 00:00:45.079 walk you through setting it up, show you how 00:00:45.079 --> 00:00:47.500 to automate it for serious work, and even how 00:00:47.500 --> 00:00:49.100 to scale it up into something really powerful. 00:00:49.380 --> 00:00:51.759 Get ready to transform raw data into a genuine 00:00:51.759 --> 00:00:53.659 strategic advantage. This could fundamentally 00:00:53.659 --> 00:00:55.920 reshape how you work. Absolutely. It's a total 00:00:55.920 --> 00:00:58.329 game changer. So, OK, let's unpack this core 00:00:58.329 --> 00:01:01.289 problem. It's no longer about finding data, right? 00:01:01.390 --> 00:01:04.390 We're swimming in it. The real challenge is distilling 00:01:04.390 --> 00:01:09.069 wisdom from this endless ocean of information. 00:01:09.609 --> 00:01:11.950 Professionals spend countless hours just sifting, 00:01:12.069 --> 00:01:15.170 trying to synthesize something meaningful. Precisely. 00:01:15.450 --> 00:01:17.810 And the solution isn't just searching faster. 00:01:18.049 --> 00:01:20.650 It's these eponymous AI agents. This isn't some 00:01:20.650 --> 00:01:23.069 far -off concept anymore. It's an accessible 00:01:23.069 --> 00:01:25.829 reality right now. Think of it as a huge short 00:01:25.799 --> 00:01:28.579 cut to being truly well -informed. Let's get 00:01:28.579 --> 00:01:30.719 straight to the insights. Right. And this deep 00:01:30.719 --> 00:01:33.219 dive really focuses on three specific tools, 00:01:33.719 --> 00:01:36.640 N8n for the workflow automation bit, OpenRouter 00:01:36.640 --> 00:01:39.200 for accessing lots of different AI models, and 00:01:39.200 --> 00:01:42.040 Perplexity AI for the specialized research power. 00:01:42.269 --> 00:01:44.670 The goal here is to build an intelligent system 00:01:44.670 --> 00:01:46.950 that performs deep, nuanced research on pretty 00:01:46.950 --> 00:01:49.469 much any topic, competitor analysis, market trends, 00:01:49.650 --> 00:01:58.849 even complex scientific literature. everyone, 00:01:59.290 --> 00:02:01.629 from entrepreneurs and marketers to seasoned 00:02:01.629 --> 00:02:04.430 researchers. By the end, you won't just have 00:02:04.430 --> 00:02:06.909 a working AI agent. You'll have this mental framework 00:02:06.909 --> 00:02:09.789 to customize its capabilities, making it uniquely 00:02:09.789 --> 00:02:12.270 valuable for whatever you need. It's incredibly 00:02:12.270 --> 00:02:15.270 empowering. So when we talk about moving beyond 00:02:15.270 --> 00:02:18.909 just finding data to getting actual wisdom, what's 00:02:18.909 --> 00:02:20.930 the fundamental shift you're talking about there? 00:02:21.270 --> 00:02:24.750 It's moving from basic search to truly understanding 00:02:24.750 --> 00:02:27.259 and synthesizing the information. big difference. 00:02:27.659 --> 00:02:29.159 Okay, here's where it gets really practical. 00:02:29.319 --> 00:02:31.919 Before we dive into the actual setup steps, it's 00:02:31.919 --> 00:02:33.699 crucial to understand conceptually what we're 00:02:33.699 --> 00:02:36.259 creating. An AI agent is, while it's much more 00:02:36.259 --> 00:02:38.560 than just a chatbot, it perceives, it reasons, 00:02:38.919 --> 00:02:40.840 and then it acts to achieve a specific goal. 00:02:41.000 --> 00:02:43.139 It's a whole system. That's right. Our system 00:02:43.139 --> 00:02:45.659 has three main pillars, basically, all designed 00:02:45.659 --> 00:02:47.960 to work together smoothly. You can think of it 00:02:47.960 --> 00:02:50.780 like a body, yeah. Complete with a brain, sensory 00:02:50.780 --> 00:02:52.860 organs, and a nervous system. each part plays 00:02:52.860 --> 00:02:55.680 a really distinct vital role. Okay, so OpenRouter 00:02:55.680 --> 00:02:58.740 in this analogy is the brain, the command center. 00:02:59.099 --> 00:03:02.000 It gives you direct access to a wide range of 00:03:02.000 --> 00:03:04.259 advanced LLMs, that's large language models, 00:03:04.840 --> 00:03:07.759 which are basically sophisticated AI brains that 00:03:07.759 --> 00:03:10.240 understand and generate human -like text models, 00:03:10.740 --> 00:03:14.020 like OpenAI's GPT series or Anthropics Claude, 00:03:14.319 --> 00:03:17.159 all through one single API. An MPI, just quickly, 00:03:17.479 --> 00:03:19.259 is simply a standard way for different software 00:03:19.259 --> 00:03:21.800 programs to talk to each other. Easy peasy. Using 00:03:21.800 --> 00:03:24.159 a model router like OpenRouter offers some real 00:03:24.159 --> 00:03:26.259 strategic advantages. Flexibility, so you're 00:03:26.259 --> 00:03:29.680 not locked into one AI provider. Cost optimization, 00:03:29.819 --> 00:03:31.719 letting you choose the best performance to cost 00:03:31.719 --> 00:03:34.620 model for each specific task. And really important, 00:03:34.900 --> 00:03:37.580 future proofing. So you can adapt as new, better 00:03:37.580 --> 00:03:39.620 models come out. It's a very smart way to do 00:03:39.620 --> 00:03:42.419 it. Right. So this brain is responsible for understanding 00:03:42.419 --> 00:03:44.560 your requests, figuring out a plan to fulfill 00:03:44.560 --> 00:03:46.460 them, and then synthesizing all the information 00:03:46.460 --> 00:03:48.259 it gathers. It's really the conductor of the 00:03:48.259 --> 00:03:52.810 whole operation. Then, Perplexity AI acts as 00:03:52.810 --> 00:03:54.789 the agent's senses. It gives you the awareness 00:03:54.789 --> 00:03:57.250 of the vast digital world out there. It goes 00:03:57.250 --> 00:03:59.909 way beyond a standard search engine. Perplexity 00:03:59.909 --> 00:04:02.969 can actually read and understand content, synthesize 00:04:02.969 --> 00:04:05.789 info from multiple sources, and then give you 00:04:05.789 --> 00:04:08.770 structured answers, crucially with accurate citations. 00:04:08.990 --> 00:04:11.669 This is the tool that gives our agent the ability 00:04:11.669 --> 00:04:14.449 to conduct real, verifiable research. It lets 00:04:14.449 --> 00:04:16.910 the agent see and interpret. Yeah, and it offers 00:04:16.910 --> 00:04:19.269 real -time search for the very latest information, 00:04:19.629 --> 00:04:21.769 multi -source synthesis, which means it can process 00:04:21.769 --> 00:04:24.350 dozens of sources at once. And those reliable 00:04:24.350 --> 00:04:27.009 citations are absolutely key for integrity, right, 00:04:27.110 --> 00:04:28.970 for trusting the output. That's how you get truly 00:04:28.970 --> 00:04:31.540 trustworthy results. Definitely. And finally, 00:04:31.759 --> 00:04:34.319 N8A1 is the agent's nervous system and skeleton. 00:04:34.319 --> 00:04:37.379 It connects all these pieces together. As a powerful 00:04:37.379 --> 00:04:40.379 workflow automation platform, N8A1 lets us define 00:04:40.379 --> 00:04:43.339 the entire logic and flow of operations for our 00:04:43.339 --> 00:04:45.759 agent, all without writing any code. It's the 00:04:45.759 --> 00:04:48.379 essential glue. It also offers a flexible interface. 00:04:48.979 --> 00:04:51.579 So, while we'll start with a chat interface for 00:04:51.579 --> 00:04:54.779 testing, your agent can actually be triggered 00:04:54.779 --> 00:04:58.100 from, say, a new row in a Google Sheet or an 00:04:58.100 --> 00:05:00.560 incoming email, or just run on a schedule, maybe 00:05:00.560 --> 00:05:03.180 a webhook from your CRM. You can build these 00:05:03.180 --> 00:05:06.759 complex chains of actions, research this, then 00:05:06.759 --> 00:05:08.720 translate it, and then post the results straight 00:05:08.720 --> 00:05:11.420 to Slack. The versatility of the system is really 00:05:11.420 --> 00:05:13.939 impressive. Yeah, and scalability is key here. 00:05:14.220 --> 00:05:16.899 You can start simple. with a really focused agent 00:05:16.899 --> 00:05:19.339 and then gradually build more complex systems 00:05:19.339 --> 00:05:22.240 as your needs grow. The beauty of this modular 00:05:22.240 --> 00:05:24.300 architecture is its, well, pretty much infinite 00:05:24.300 --> 00:05:27.180 flexibility. Adapting it for new tasks is as 00:05:27.180 --> 00:05:29.199 simple as changing the prompt to your instructions 00:05:29.199 --> 00:05:31.720 and maybe the input data source. It's almost 00:05:31.720 --> 00:05:33.920 like stacking Lego blocks of data, you know, 00:05:34.040 --> 00:05:36.639 building exactly what you need. That modularity 00:05:36.639 --> 00:05:39.199 is fascinating. How exactly does that allow for 00:05:39.199 --> 00:05:42.759 such infinite flexibility? Well, by easily swapping 00:05:42.759 --> 00:05:44.620 out tools or just changing the instructions, 00:05:44.819 --> 00:05:47.000 it adapts super quickly. Okay, this is the practical 00:05:47.000 --> 00:05:49.120 bit, the nuts and bolts. How do we actually build 00:05:49.120 --> 00:05:51.279 this thing? The good news is it's probably simpler 00:05:51.279 --> 00:05:53.439 than you might think. First, you'll need to create 00:05:53.439 --> 00:05:56.720 an N8n account. The cloud version is definitely 00:05:56.720 --> 00:05:59.000 the quickest way to get started. Once you're 00:05:59.000 --> 00:06:01.459 logged in, you just create a new workflow, which 00:06:01.459 --> 00:06:03.899 gives you this blank canvas. This is where your 00:06:03.899 --> 00:06:05.759 agent will come to life. Then you add a trigger, 00:06:06.519 --> 00:06:09.100 click Add First Step, search for On Chat Message, 00:06:09.199 --> 00:06:11.959 and select it. This sets up a simple chat interface, 00:06:12.139 --> 00:06:14.480 which is perfect for our initial testing. It's 00:06:14.480 --> 00:06:16.399 how you'll actually start talking to your agent. 00:06:16.660 --> 00:06:18.980 Next step. After that chat message node, you 00:06:18.980 --> 00:06:21.139 click the little plus button and search for AI 00:06:21.139 --> 00:06:24.079 agent. Select the AI agent node from the list. 00:06:24.439 --> 00:06:28.019 This node is like the real heart of the whole 00:06:28.019 --> 00:06:30.480 operation. Okay, once you're inside the NAN AI 00:06:30.480 --> 00:06:33.319 agent node, you'll find chat model and select 00:06:33.319 --> 00:06:35.579 open router chat model. It'll ask you to create 00:06:35.579 --> 00:06:37.879 new credentials. So you'll hop over to openrouter 00:06:37.879 --> 00:06:40.699 .ai, sign up, add a small amount of credit, just 00:06:40.699 --> 00:06:43.079 a bit to start, and generate an API key from 00:06:43.079 --> 00:06:45.620 your profile section. Now copy that key immediately. 00:06:45.879 --> 00:06:47.839 They won't show it to you again. It's like your 00:06:47.839 --> 00:06:50.300 secret password to the AI brain. And don't worry, 00:06:50.620 --> 00:06:52.500 we'll have detailed step -by -step instructions 00:06:52.500 --> 00:06:54.600 for all this key stuff in our show notes. Right. 00:06:54.920 --> 00:06:57.220 Then back in AN8N, you'll simply paste that API 00:06:57.220 --> 00:06:59.540 key into the credential field and save it. Easy 00:06:59.540 --> 00:07:02.860 enough. Then you choose your AI model from the 00:07:02.860 --> 00:07:06.000 list OpenRouter provides. For good balance of 00:07:06.000 --> 00:07:07.839 performance and low cost, especially when you're 00:07:07.839 --> 00:07:10.879 starting out, GPT -40 Mini or Claude III Haiku 00:07:10.879 --> 00:07:12.879 are really excellent choices. They'll get the 00:07:12.879 --> 00:07:15.319 job done efficiently without costing a fortune. 00:07:15.639 --> 00:07:19.160 Perfect. Now for the agent's eyes and ears, perplexity. 00:07:19.540 --> 00:07:22.939 In that same AI agent node, find the tool section, 00:07:23.079 --> 00:07:26.740 click plus add tool, search for perplexity and 00:07:26.740 --> 00:07:29.850 select perplexity tool. Again, you'll need to 00:07:29.850 --> 00:07:31.329 create new credentials just like you did for 00:07:31.329 --> 00:07:34.189 OpenRouter. So you go to perplexity .ai, sign 00:07:34.189 --> 00:07:37.509 up, navigate to settings, find API keys, create 00:07:37.509 --> 00:07:39.790 a new key, and copy it. Like OpenRouter, you 00:07:39.790 --> 00:07:41.449 might need to add a little bit of credit to your 00:07:41.449 --> 00:07:44.029 account first, then paste your perplexity API 00:07:44.029 --> 00:07:46.689 key into NAN and save it. Okay, now within the 00:07:46.689 --> 00:07:48.930 perplexity tool settings back in ANN, you have 00:07:48.930 --> 00:07:51.250 some important options. For the model, make sure 00:07:51.250 --> 00:07:53.709 you select sonar online. That one gives you comprehensive, 00:07:53.769 --> 00:07:56.069 deep answers. It's critical for doing robust 00:07:56.069 --> 00:07:58.470 research. And crucially, for the user message 00:07:58.470 --> 00:08:00.670 field, you need to set this to let the model 00:08:00.670 --> 00:08:03.290 define this parameter. This is a really critical 00:08:03.290 --> 00:08:06.389 setting. It allows the AI brain open router to 00:08:06.389 --> 00:08:08.930 automatically generate optimized search queries 00:08:08.930 --> 00:08:11.709 for the perplexity tool based on your initial 00:08:11.709 --> 00:08:14.790 high -level request. It basically gives the AI 00:08:14.790 --> 00:08:16.870 the intelligence to figure out the best way to 00:08:16.870 --> 00:08:19.110 ask its questions, making it much more effective. 00:08:19.230 --> 00:08:21.790 Right. This is the moment of truth. Save your 00:08:21.790 --> 00:08:24.230 workflow and activate it. then open up the chat 00:08:24.230 --> 00:08:26.810 interface. Usually there's a chat tab or button 00:08:26.810 --> 00:08:29.410 right there in N or APN. It's pretty cool seeing 00:08:29.410 --> 00:08:31.569 it actually come to life. Now ask it a research 00:08:31.569 --> 00:08:33.470 question that really needs synthesis, not just 00:08:33.470 --> 00:08:36.429 a simple fact lookup. Try something like analyze 00:08:36.429 --> 00:08:38.830 the key marketing strategies used by emerging 00:08:38.830 --> 00:08:40.830 electric vehicle companies to compete against 00:08:40.830 --> 00:08:44.759 Tesla. Or maybe summarize recent advancements 00:08:44.759 --> 00:08:47.080 in solid -state battery technology, focusing 00:08:47.080 --> 00:08:50.100 specifically on manufacturing challenges. Give 00:08:50.100 --> 00:08:52.559 it something substantial to work with. So what 00:08:52.559 --> 00:08:54.259 happens behind the scenes when you ask that? 00:08:54.620 --> 00:08:56.700 Your question goes straight to the AI agent node. 00:08:57.259 --> 00:09:00.679 The open router model, our brain, analyzes your 00:09:00.679 --> 00:09:04.019 request, figures out it needs outside info, decides, 00:09:04.200 --> 00:09:07.000 okay, use the perplexity tool, and then it formulates 00:09:07.000 --> 00:09:10.700 these detailed, precise search queries. Perplexity 00:09:10.700 --> 00:09:14.019 gets those queries, scours the web, reads dozens 00:09:14.019 --> 00:09:17.080 of sources, and sends back a structured summary 00:09:17.080 --> 00:09:19.740 to the brain. Finally, the brain synthesizes 00:09:19.740 --> 00:09:23.039 all that information into a coherent, well -organized 00:09:23.039 --> 00:09:26.230 final answer. It feels like magic, but it's all 00:09:26.230 --> 00:09:28.509 just clever code and smart design working together. 00:09:28.690 --> 00:09:31.169 And what you get back is a mini report, well 00:09:31.169 --> 00:09:33.769 researched, full of insights and citations, all 00:09:33.769 --> 00:09:35.909 in just a minute or two. That's just a massive 00:09:35.909 --> 00:09:37.990 leap from how we traditionally do research. I 00:09:37.990 --> 00:09:39.610 can already see how that changes the game for 00:09:39.610 --> 00:09:42.009 so many professionals. In that setup process, 00:09:42.090 --> 00:09:43.710 what would you say is the most critical setting 00:09:43.710 --> 00:09:46.190 in perplexity for getting better research results? 00:09:46.649 --> 00:09:49.110 Definitely letting the AI model define the user 00:09:49.110 --> 00:09:52.840 message. That's key. Sponsor. Okay, so while 00:09:52.840 --> 00:09:55.559 that chat interface is really fantastic for testing 00:09:55.559 --> 00:09:58.360 and quick queries, the real power of these agents, 00:09:58.379 --> 00:10:01.259 as you said, lies in automation. Let's explore 00:10:01.259 --> 00:10:04.360 how to turn this agent into a truly autonomous 00:10:04.360 --> 00:10:07.259 business asset that works for you. Right. Imagine 00:10:07.259 --> 00:10:09.580 waking up every single morning to a fresh intelligence 00:10:09.580 --> 00:10:12.259 briefing on your key competitors. Here's how 00:10:12.259 --> 00:10:14.830 you'd set that up. First, you change the trigger. 00:10:15.389 --> 00:10:17.370 Get rid of the chat message node and replace 00:10:17.370 --> 00:10:19.649 it with the schedule node. Set it to run daily 00:10:19.649 --> 00:10:22.690 at a specific time, say 5 a .m., so it's working 00:10:22.690 --> 00:10:25.210 for you while you sleep. Pretty cool, huh? That 00:10:25.210 --> 00:10:27.429 is cool. Okay, next, you add a Google Sheets 00:10:27.429 --> 00:10:29.710 node to get your competitor list. You configure 00:10:29.710 --> 00:10:32.070 it to read a specific column from a spreadsheet 00:10:32.070 --> 00:10:33.649 where you've just listed all your competitors. 00:10:34.350 --> 00:10:36.090 The workflow will then automatically process 00:10:36.090 --> 00:10:38.559 each one, just one by one, down the list. Now, 00:10:38.659 --> 00:10:41.000 the most critical part here is really refining 00:10:41.000 --> 00:10:44.659 the system prompt inside the AI agent node. You'll 00:10:44.659 --> 00:10:46.580 need to craft a detailed prompt that defines 00:10:46.580 --> 00:10:49.340 its role, like, you are a senior competitive 00:10:49.340 --> 00:10:52.740 intelligence analyst. Clear role, its mission. 00:10:52.919 --> 00:10:55.899 To investigate five specific areas over the last 00:10:55.899 --> 00:10:58.820 two weeks, new product launches, major marketing 00:10:58.820 --> 00:11:01.379 campaigns, strategic partnerships, changes in 00:11:01.379 --> 00:11:03.940 senior leadership, and any commentary and financial 00:11:03.940 --> 00:11:06.279 reports, being super specific in that prompt 00:11:06.279 --> 00:11:09.159 is absolutely key. And you'll also specify the 00:11:09.159 --> 00:11:11.600 output format you want, like a clearly structured 00:11:11.600 --> 00:11:14.399 markdown report. Maybe it starts with a concise 00:11:14.399 --> 00:11:16.840 three -sentence executive summary, then detailed 00:11:16.840 --> 00:11:19.379 bullet points for each area, always citing sources. 00:11:19.779 --> 00:11:22.000 That clarity is crucial for making the intelligence 00:11:22.000 --> 00:11:25.200 action. Exactly. And finally, after the AI agent 00:11:25.200 --> 00:11:27.480 node does its work, you add another Google Sheets 00:11:27.480 --> 00:11:30.159 node, this time set to append update mode. You 00:11:30.159 --> 00:11:31.840 configure it to write the generated report into 00:11:31.840 --> 00:11:34.100 a new column right next to the competitor's name 00:11:34.100 --> 00:11:36.779 in your original spreadsheet. Boom. You now have 00:11:36.779 --> 00:11:39.379 a fully automated system delivering fresh competitive 00:11:39.379 --> 00:11:42.299 intelligence right to you every single day. That's 00:11:42.299 --> 00:11:45.210 a true game changer for strategy. Wow. And the 00:11:45.210 --> 00:11:47.710 applications really do extend far beyond just 00:11:47.710 --> 00:11:49.870 competitive analysis, don't they? Oh, absolutely. 00:11:50.149 --> 00:11:52.570 Think about lid research. You could trigger the 00:11:52.570 --> 00:11:54.970 agent whenever a new lead pops up in your CRM, 00:11:55.309 --> 00:11:59.070 like Salesforce or HubSpot. The agent then researches 00:11:59.070 --> 00:12:01.850 the lead's company, their specific role, any 00:12:01.850 --> 00:12:04.970 recent news, providing your sales team with incredibly 00:12:04.970 --> 00:12:07.429 personalized talking points. This could totally 00:12:07.429 --> 00:12:10.789 transform sales outreach. That's powerful. Or 00:12:10.789 --> 00:12:13.620 market intelligence and trend tracking? You could 00:12:13.620 --> 00:12:16.039 set up the agent to monitor specific keywords 00:12:16.039 --> 00:12:19.730 or topics, say, AI and healthcare, decentralized 00:12:19.730 --> 00:12:22.769 finance, or maybe sustainable fashion. And it 00:12:22.769 --> 00:12:24.690 sends you a weekly digest of the most important 00:12:24.690 --> 00:12:26.610 developments, helps you stay ahead of the curve 00:12:26.610 --> 00:12:29.169 almost effortlessly. Content creation support 00:12:29.169 --> 00:12:31.690 is another huge one. Give the agent a topic for 00:12:31.690 --> 00:12:34.330 a blog post or maybe a video script. It can generate 00:12:34.330 --> 00:12:36.929 a detailed research brief complete with key statistics, 00:12:37.370 --> 00:12:39.210 expert quotes, even potential counter arguments. 00:12:39.570 --> 00:12:41.710 This forms a solid foundation for your content 00:12:41.710 --> 00:12:43.809 so you're never staring at a blank page again. 00:12:44.000 --> 00:12:46.720 And for investment research too, you could automatically 00:12:46.720 --> 00:12:49.100 analyze potential stocks by asking the agent 00:12:49.100 --> 00:12:52.080 to summarize recent financial reports, analyze 00:12:52.080 --> 00:12:54.779 news sentiment, compare key metrics against industry 00:12:54.779 --> 00:12:57.200 peers, imagine the depth of insights you could 00:12:57.200 --> 00:13:00.500 get automatically. With all these amazing possibilities, 00:13:00.720 --> 00:13:02.779 what's truly the key to making this automation 00:13:02.779 --> 00:13:06.620 really effective and work for you? A clear, detailed 00:13:06.620 --> 00:13:09.529 system prompt for the AI. That's the foundation. 00:13:10.009 --> 00:13:13.110 The quality of your agent's output is just directly 00:13:13.110 --> 00:13:15.110 proportional to the quality of your instructions. 00:13:15.549 --> 00:13:17.669 It's that whole saying, garbage in, garbage out. 00:13:18.210 --> 00:13:20.250 It totally applies here, even with sophisticated 00:13:20.250 --> 00:13:22.549 AI. The system prompt is, well, it's essentially 00:13:22.549 --> 00:13:24.730 your agent's constitution, isn't it? It defines 00:13:24.730 --> 00:13:26.730 its role, its personality, if you will, and the 00:13:26.730 --> 00:13:29.049 precise rules of engagement. It guides everything 00:13:29.049 --> 00:13:31.610 the agent does. Yeah. We like to use this persona 00:13:31.610 --> 00:13:33.610 task format principle. It helps structure it. 00:13:33.850 --> 00:13:37.379 First, persona. clearly define its role. Like, 00:13:37.580 --> 00:13:40.279 you are a seasoned financial analyst with expertise 00:13:40.279 --> 00:13:43.500 in the consumer goods market. Be specific. Then, 00:13:43.620 --> 00:13:46.559 task. Describe exactly what it needs to do with 00:13:46.559 --> 00:13:49.159 all the necessary details. For instance, your 00:13:49.159 --> 00:13:51.600 task is to analyze the latest quarterly earnings 00:13:51.600 --> 00:13:55.000 report for company acts. And finally, format. 00:13:55.559 --> 00:13:58.600 Specify the desired output structure. Like, present 00:13:58.600 --> 00:14:01.720 your analysis as a structured memo using markdown 00:14:01.720 --> 00:14:04.250 headings for clarity. That makes sense. An advanced 00:14:04.250 --> 00:14:06.950 example, maybe for academic research, might specify 00:14:06.950 --> 00:14:09.870 the role as an academic research assistant specializing 00:14:09.870 --> 00:14:12.950 in computer science. The task to find five influential 00:14:12.950 --> 00:14:15.970 papers on topic Y, summarize the abstract, methodology, 00:14:16.110 --> 00:14:18.370 and conclusions for each, and the format as, 00:14:18.490 --> 00:14:21.210 use APA -style citation and please exclude review 00:14:21.210 --> 00:14:23.850 articles. It's all about being incredibly precise 00:14:23.850 --> 00:14:26.129 with those instructions. Totally. And, you know, 00:14:26.169 --> 00:14:27.990 even with these principles, I gotta admit, I 00:14:27.990 --> 00:14:29.889 still wrestle with prompt drift myself sometimes. 00:14:30.279 --> 00:14:32.399 Definitely a continuous learning curve. It really 00:14:32.399 --> 00:14:34.100 is an art, as much as it is a science getting 00:14:34.100 --> 00:14:37.159 those problems just right. And be mindful of 00:14:37.159 --> 00:14:39.259 any sensitive information you feed into your 00:14:39.259 --> 00:14:41.840 queries. Always take a moment to understand the 00:14:41.840 --> 00:14:44.659 data policies of the services you're using. Your 00:14:44.659 --> 00:14:46.960 data is your responsibility, ultimately. And 00:14:46.960 --> 00:14:49.779 this is such a critical point. Human in the loop 00:14:49.779 --> 00:14:52.740 is essential. Please don't blindly trust the 00:14:52.740 --> 00:14:55.200 AI's output. Use it as an incredibly powerful 00:14:55.200 --> 00:14:58.220 assistant, yes, but not as an infallible oracle. 00:14:58.679 --> 00:15:01.580 Always review and verify any critical information 00:15:01.580 --> 00:15:04.860 yourself. Your judgment is still paramount. Absolutely. 00:15:04.940 --> 00:15:07.220 And just a quick practical tip. Avoid creating 00:15:07.220 --> 00:15:09.259 unintended loops in your automated workflows. 00:15:09.779 --> 00:15:11.820 This prevents agents from accidentally running 00:15:11.820 --> 00:15:14.000 out of control and racking up large, unexpected 00:15:14.000 --> 00:15:16.399 costs. That's a very real risk if you're not 00:15:16.399 --> 00:15:18.440 careful with automation logic. You mentioned 00:15:18.440 --> 00:15:21.240 it's vital, but why exactly is that human -in 00:15:21.240 --> 00:15:23.559 -the -loop aspect so incredibly important with 00:15:23.559 --> 00:15:26.620 these agents? Because AI is a powerful assistant, 00:15:27.019 --> 00:15:30.519 yes, but it's not an infallible oracle. Verification 00:15:30.519 --> 00:15:33.250 is key. Okay, so once you've mastered building 00:15:33.250 --> 00:15:35.769 a single agent, the next really fascinating step 00:15:35.769 --> 00:15:39.029 is to think about multi -agent systems, sometimes 00:15:39.029 --> 00:15:42.429 called agent swarms. This is a more advanced 00:15:42.429 --> 00:15:45.509 concept where specialized agents actually collaborate 00:15:45.509 --> 00:15:47.330 to achieve a much larger goal. It's like building 00:15:47.330 --> 00:15:50.470 an entire analytical team, but powered by AI. 00:15:50.570 --> 00:15:52.570 Yeah, and you can absolutely build this within 00:15:52.570 --> 00:15:55.149 Antedan's framework. Imagine having a coordinator 00:15:55.149 --> 00:15:57.470 agent that receives a complex, high -level goal. 00:15:57.929 --> 00:16:00.029 Something like, prepare a full investment report 00:16:00.029 --> 00:16:03.629 on company X. big task. Right. The coordinator 00:16:03.629 --> 00:16:06.029 agent then intelligently breaks that large task 00:16:06.029 --> 00:16:08.990 down into smaller, more manageable subtasks. 00:16:09.090 --> 00:16:11.289 It then calls upon other specialist agents maybe 00:16:11.289 --> 00:16:13.830 using webhooks or other N8N triggers to handle 00:16:13.830 --> 00:16:15.970 those specific subtasks. For example, you might 00:16:15.970 --> 00:16:17.870 have a financial analyst agent whose job is just 00:16:17.870 --> 00:16:20.309 to pull specific financial statements, a news 00:16:20.309 --> 00:16:22.289 analyst agent to scan for recent relevant news, 00:16:22.289 --> 00:16:24.429 and a competitor research agent to benchmark 00:16:24.429 --> 00:16:27.169 key metrics against peers. Each agent has its 00:16:27.169 --> 00:16:30.590 own specialized job. Exactly. And then the coordinator 00:16:30.570 --> 00:16:33.129 agent collects all the results from each specialist 00:16:33.129 --> 00:16:36.210 and synthesizes them into one final, coherent, 00:16:36.370 --> 00:16:41.279 comprehensive report. Pause. Whoa. Just imagine 00:16:41.279 --> 00:16:43.399 scaling that kind of human -like intelligence 00:16:43.399 --> 00:16:46.799 and collaboration across countless complex tasks, 00:16:47.220 --> 00:16:49.120 effectively mimicking an entire team of highly 00:16:49.120 --> 00:16:51.720 specialized analysts working in perfect synchronicity. 00:16:52.279 --> 00:16:55.200 This approach truly allows for modular, scalable, 00:16:55.419 --> 00:16:58.139 and incredibly powerful AI systems. It's really 00:16:58.139 --> 00:16:59.620 transformative when you think about it. That 00:16:59.620 --> 00:17:01.769 sounds amazing. But for someone just starting 00:17:01.769 --> 00:17:04.289 out, what's the main benefit of an agent swarm 00:17:04.289 --> 00:17:06.930 compared to just building a single really robust 00:17:06.930 --> 00:17:09.650 agent? It really allows for a highly specialized 00:17:09.650 --> 00:17:12.130 collaborative work leading to incredibly powerful 00:17:12.130 --> 00:17:14.529 and nuanced systems overall. So what we've explored 00:17:14.529 --> 00:17:16.509 today really shows how these new code tools like 00:17:16.509 --> 00:17:19.109 NAM are truly democratizing AI. They're making 00:17:19.109 --> 00:17:21.430 incredibly sophisticated applications genuinely 00:17:21.430 --> 00:17:23.970 accessible to almost anyone, regardless of coding 00:17:23.970 --> 00:17:26.049 skill. Yeah, the paradigm shift here is pretty 00:17:26.049 --> 00:17:29.269 profound. It's moving away from just reactive 00:17:29.269 --> 00:17:32.869 chat bots towards these autonomous goal -oriented 00:17:32.869 --> 00:17:36.220 systems that actively work for you. And the key 00:17:36.220 --> 00:17:38.900 really is customization. The real power comes 00:17:38.900 --> 00:17:41.660 from tailoring these agents to your specific 00:17:41.660 --> 00:17:44.700 needs through carefully crafted system prompts 00:17:44.700 --> 00:17:47.420 and intelligent workflow design. Remember the 00:17:47.420 --> 00:17:50.559 advice. Start simple, test thoroughly, and then 00:17:50.559 --> 00:17:52.519 scale gradually as you get more comfortable. 00:17:52.940 --> 00:17:54.880 This isn't just another productivity hack. It 00:17:54.880 --> 00:17:57.299 feels more like a fundamental competitive advantage 00:17:57.299 --> 00:18:00.460 in today's increasingly complex data -rich world. 00:18:00.640 --> 00:18:02.460 So what does this all mean for you listening 00:18:02.460 --> 00:18:04.710 right now? Maybe challenge yourself to identify 00:18:04.710 --> 00:18:06.730 the biggest information bottlenecks in your daily 00:18:06.730 --> 00:18:09.349 work. Could an automated research agent like 00:18:09.349 --> 00:18:11.650 the one we discussed actually solve them? Think 00:18:11.650 --> 00:18:13.630 about how integrating this kind of automated 00:18:13.630 --> 00:18:16.549 intelligence into your processes could actively 00:18:16.549 --> 00:18:18.990 shape the future of how you work, how you innovate, 00:18:18.990 --> 00:18:21.289 and how you gain a decisive edge. That's been 00:18:21.289 --> 00:18:24.170 our deep dive into autonomous AI research agents. 00:18:24.569 --> 00:18:26.670 We really hope this provided you with some valuable 00:18:26.670 --> 00:18:29.210 insights and maybe an exciting framework for 00:18:29.210 --> 00:18:31.470 action. Thanks so much for joining us. Until 00:18:31.470 --> 00:18:33.920 next time. Outro Music