WEBVTT 00:00:00.000 --> 00:00:03.520 Corporate America is starting to ration AI. Beat. 00:00:03.740 --> 00:00:06.280 The days of giving employees random tools are 00:00:06.280 --> 00:00:08.839 over. They really are. We are moving away from 00:00:08.839 --> 00:00:11.939 the chaotic Wild West. Companies are simply tired 00:00:11.939 --> 00:00:14.580 of paying massive monthly bills. Right. The budgeting 00:00:14.580 --> 00:00:16.879 got totally out of hand. Now, executives are 00:00:16.879 --> 00:00:19.620 asking a much harder question. What are we actually 00:00:19.620 --> 00:00:23.579 getting from all this? Two sec silence. Welcome 00:00:23.579 --> 00:00:25.940 to this deep dive. Thanks for having me. today 00:00:25.940 --> 00:00:29.059 we are exploring a strange transition in corporate 00:00:29.059 --> 00:00:31.449 tech we have an Excellent source article for 00:00:31.449 --> 00:00:33.770 you today. It is a really solid breakdown. It 00:00:33.770 --> 00:00:36.689 focuses on building business systems with cloud 00:00:36.689 --> 00:00:39.329 -managed agents. The goal for our conversation 00:00:39.329 --> 00:00:42.630 today is simple but profound. We want to understand 00:00:42.630 --> 00:00:45.509 how we move past messy chatbots. We're moving 00:00:45.509 --> 00:00:48.630 towards structured, cloud -based AI employees. 00:00:49.130 --> 00:00:51.149 Systems that actually do real business work. 00:00:51.350 --> 00:00:54.109 Exactly. Today, we will explore what these managed 00:00:54.109 --> 00:00:56.729 agents are. We will look at how they use actual 00:00:56.729 --> 00:00:59.670 sleep cycles. Which is a brilliant concept. process 00:00:59.670 --> 00:01:01.890 their day just like we do. We will also examine 00:01:01.890 --> 00:01:04.290 the most profitable business use cases. Yeah, 00:01:04.370 --> 00:01:07.329 the real money makers. And finally, we will discuss 00:01:07.329 --> 00:01:09.989 how you can start building them. You want to 00:01:09.989 --> 00:01:12.909 do this without creating a chaotic bot army. 00:01:13.129 --> 00:01:15.430 Because that is a trap a lot of people fall into. 00:01:15.590 --> 00:01:18.409 The current state of AI is incredibly messy right 00:01:18.409 --> 00:01:21.069 now. Companies are paying for a massive scatter 00:01:21.069 --> 00:01:23.969 of tools. It is everywhere. You have one tool 00:01:23.969 --> 00:01:27.290 just for writing copy. You have another separate 00:01:27.290 --> 00:01:29.310 tool for technical research. You probably have 00:01:29.310 --> 00:01:32.030 another for coding or internal search. I will 00:01:32.030 --> 00:01:34.980 be completely honest with you here. I still struggle 00:01:34.980 --> 00:01:37.260 with this chaotic setup myself daily. Oh, totally. 00:01:37.599 --> 00:01:40.060 We all do. I feel completely overwhelmed by having 00:01:40.060 --> 00:01:43.180 a disconnected stack. My AI tools are scattered 00:01:43.180 --> 00:01:46.099 across my entire workflow. Right. I am constantly 00:01:46.099 --> 00:01:48.579 copying and pasting between five different tabs. 00:01:48.719 --> 00:01:50.840 It honestly drives me crazy sometimes. You are 00:01:50.840 --> 00:01:52.620 definitely not alone in feeling that way. Yeah. 00:01:52.640 --> 00:01:54.719 That is the standard experience for almost everyone 00:01:54.719 --> 00:01:57.680 right now. It really is. And that scattered usage 00:01:57.680 --> 00:02:00.319 does not connect to business results. Yeah. After 00:02:00.319 --> 00:02:02.629 one month. The corporate billing looks absolutely 00:02:02.629 --> 00:02:04.890 insane. Just a wall of random subscriptions. 00:02:05.530 --> 00:02:08.729 Exactly. Companies pay for empty AI seats nobody 00:02:08.729 --> 00:02:12.990 uses. They pay for extra API calls across fragmented 00:02:12.990 --> 00:02:15.849 platforms. Yeah. They pay for random tools different 00:02:15.849 --> 00:02:18.949 teams signed up for. That is when the core conversation 00:02:18.949 --> 00:02:21.689 at the executive level changes. They stop asking 00:02:21.689 --> 00:02:24.669 what the AI can technically do. Right. They pivot 00:02:24.669 --> 00:02:26.729 entirely. They start asking which systems are 00:02:26.729 --> 00:02:28.849 actually worth paying for. This is where cloud 00:02:28.849 --> 00:02:30.870 managed agents become incredible. incredibly 00:02:30.870 --> 00:02:33.349 interesting. Before this, developers used something 00:02:33.349 --> 00:02:36.050 called cloud code. Right. The local version. 00:02:36.169 --> 00:02:38.610 It ran locally on your own physical computer, 00:02:38.889 --> 00:02:41.129 which sounds great for personal testing and hacking 00:02:41.129 --> 00:02:43.310 around. It was fantastic for personal coding 00:02:43.310 --> 00:02:46.849 projects. But a local workspace only works for 00:02:46.849 --> 00:02:49.729 one single user. Right. It is completely trapped 00:02:49.729 --> 00:02:52.669 on your laptop. Cloud managed agents move that 00:02:52.669 --> 00:02:55.550 power directly into the cloud. The agent can 00:02:55.550 --> 00:02:58.349 now run inside a shared product. It can run inside 00:02:58.349 --> 00:03:01.009 a client system or broader workflow. But a basic 00:03:01.009 --> 00:03:03.430 chatbot already exists in the cloud. It just 00:03:03.430 --> 00:03:05.849 answers a single prompt when you type it. That 00:03:05.849 --> 00:03:09.439 is true. But a chatbot is purely reactive. A 00:03:09.439 --> 00:03:12.340 managed agent actually follows a persistent specific 00:03:12.340 --> 00:03:15.419 role. It uses your company's business context 00:03:15.419 --> 00:03:18.599 automatically. It securely accesses your internal 00:03:18.599 --> 00:03:22.159 databases. It supports repeated complex workflows 00:03:22.159 --> 00:03:25.180 over long periods. It basically becomes part 00:03:25.180 --> 00:03:27.599 of the company's central operating system. Let 00:03:27.599 --> 00:03:29.300 me pause and ask you something about that transition. 00:03:29.680 --> 00:03:32.419 Why does running in the cloud fundamentally change 00:03:32.419 --> 00:03:35.699 the business value? Beat. Why is it so different 00:03:35.699 --> 00:03:38.560 from running on a local machine? A local machine 00:03:38.560 --> 00:03:41.259 limits the AI to your personal hours. When you 00:03:41.259 --> 00:03:44.280 close your laptop, the AI stops working. In the 00:03:44.280 --> 00:03:46.419 cloud, the system runs continuously for everyone. 00:03:46.659 --> 00:03:49.039 It integrates directly with your company -wide 00:03:49.039 --> 00:03:51.580 workflows over time. It becomes a shared resource 00:03:51.580 --> 00:03:54.180 rather than a private calculator. So cloud means 00:03:54.180 --> 00:03:56.280 it supports team workflows over time, not just 00:03:56.280 --> 00:03:58.979 isolated solo tasks. Exactly. But for a cloud 00:03:58.979 --> 00:04:02.069 agent to be useful over time... It needs one 00:04:02.069 --> 00:04:05.310 crucial thing. It needs a reliable memory. It 00:04:05.310 --> 00:04:07.569 cannot wake up with amnesia every single day. 00:04:07.669 --> 00:04:09.949 If you have ever used a standard chatbot, you 00:04:09.949 --> 00:04:13.069 know this pain. It is the absolute biggest limitation 00:04:13.069 --> 00:04:16.449 of AI today. The model might be brilliant during 00:04:16.449 --> 00:04:20.110 one single conversation. Right. But it forgets 00:04:20.110 --> 00:04:22.250 everything the second after that session ends. 00:04:22.410 --> 00:04:25.649 In a fast -paced business setting, that amnesia 00:04:25.649 --> 00:04:28.670 is a nightmare. Making employees repeat rules 00:04:28.670 --> 00:04:31.430 and tone daily is terrible. It makes the agent 00:04:31.430 --> 00:04:34.110 useless. You cannot repeat project deadlines 00:04:34.110 --> 00:04:37.189 every single morning. The human spends more time 00:04:37.189 --> 00:04:40.089 managing the AI than working. Exactly. The return 00:04:40.089 --> 00:04:43.009 on investment vanishes instantly. A useful business 00:04:43.009 --> 00:04:46.170 assistant must carry vital context forward. This 00:04:46.170 --> 00:04:48.550 is why cloud -managed agents utilize a dedicated 00:04:48.550 --> 00:04:51.589 memory system. To fix the amnesia, developers 00:04:51.589 --> 00:04:54.089 give them cognitive architecture. Let me stop 00:04:54.089 --> 00:04:56.240 you right there for clarity. What does cognitive 00:04:56.240 --> 00:04:58.180 architecture actually mean in plain English? 00:04:58.439 --> 00:05:01.420 The structure built so an agent can remember 00:05:01.420 --> 00:05:03.339 and review its work. That makes perfect sense. 00:05:03.399 --> 00:05:05.959 It is basically a filing system. Yes, and it 00:05:05.959 --> 00:05:08.680 involves several distinct layers of memory. Short 00:05:08.680 --> 00:05:11.139 -term memory keeps track of the immediate current 00:05:11.139 --> 00:05:14.399 task. Okay. Long -term memory stores useful information 00:05:14.399 --> 00:05:16.990 for all future sessions. How does it actually 00:05:16.990 --> 00:05:19.149 store those long -term memories? Does it just 00:05:19.149 --> 00:05:22.829 bloat the active prompt with endless text? No, 00:05:22.930 --> 00:05:24.709 that would get way too expensive and confusing. 00:05:24.990 --> 00:05:28.009 Instead, it uses simple, readable text files 00:05:28.009 --> 00:05:30.790 called markdown files. Like basic text documents. 00:05:30.930 --> 00:05:34.949 Exactly. You might see files like dailylog .md 00:05:34.949 --> 00:05:38.910 or a file called userpreferences .md. Right. 00:05:39.009 --> 00:05:42.610 And a very important file called decisions. This 00:05:42.610 --> 00:05:45.610 leads us directly to the concept of the sleep 00:05:45.610 --> 00:05:49.329 cycle beat. I find this mechanism absolutely 00:05:49.329 --> 00:05:51.670 fascinating. It is really cool. At the end of 00:05:51.670 --> 00:05:53.930 the day, the agent reviews its work. It saves 00:05:53.930 --> 00:05:56.149 important decisions and updates those markdown 00:05:56.149 --> 00:05:59.149 files. It ignores useless conversational details 00:05:59.149 --> 00:06:01.329 and writes a summary for tomorrow. Right, and 00:06:01.329 --> 00:06:02.889 it does this completely automatically in the 00:06:02.889 --> 00:06:05.370 background. It parses its own daily transcript 00:06:05.370 --> 00:06:08.069 to find the gold. Okay, let's unpack this sleep 00:06:08.069 --> 00:06:10.870 cycle idea, two -sec silence. It sounds exactly 00:06:10.870 --> 00:06:13.750 like a head chef closing down a kitchen. I love 00:06:13.750 --> 00:06:16.149 that analogy. You do not keep the potato peels 00:06:16.149 --> 00:06:18.750 from the chaos. You throw the trash away and 00:06:18.750 --> 00:06:21.250 wipe the counters. But you write down the new 00:06:21.250 --> 00:06:24.250 recipe tweak you perfected. you leave that specific 00:06:24.250 --> 00:06:26.810 note on the counter for the morning shift. That 00:06:26.810 --> 00:06:28.870 is a perfect way to visualize the mechanism. 00:06:29.110 --> 00:06:32.089 And that is exactly how the next day's startup 00:06:32.089 --> 00:06:35.129 flow feels. Before the agent answers any new 00:06:35.129 --> 00:06:38.490 questions, it reads its notes, it reads yesterday's 00:06:38.490 --> 00:06:41.910 summary, and checks any open tasks. So it preps 00:06:41.910 --> 00:06:44.310 itself? Yeah. It reviews the current rules and 00:06:44.310 --> 00:06:46.750 the updated preferences. The human user does 00:06:46.750 --> 00:06:49.370 not have to repeat the background context. I 00:06:49.370 --> 00:06:51.430 am really curious about the actual mechanics 00:06:51.430 --> 00:06:54.129 of that review. How does the agent actually know 00:06:54.129 --> 00:06:56.509 what to remember? That is the tricky part. How 00:06:56.509 --> 00:06:59.430 does it separate vital facts from random chatter 00:06:59.430 --> 00:07:01.990 during its sleep cycle? You have to give it strict 00:07:01.990 --> 00:07:05.370 prompt rules for its evening review. You explicitly 00:07:05.370 --> 00:07:08.269 instruct it to only save decisions and process 00:07:08.269 --> 00:07:11.189 changes. Okay. That strict filter forces it to 00:07:11.189 --> 00:07:13.730 drop all the conversational noise. It actively 00:07:13.730 --> 00:07:16.189 deletes the filler and only keeps the signal. 00:07:16.310 --> 00:07:18.689 It relies on your specific rules to filter out 00:07:18.689 --> 00:07:22.029 the random, useless daily details. Spot on. Now 00:07:22.029 --> 00:07:24.230 that the agent has a functioning memory, we have 00:07:24.230 --> 00:07:27.449 to give it a job. And this is where most companies 00:07:27.449 --> 00:07:29.689 make a critical mistake. The source material 00:07:29.689 --> 00:07:32.899 is very clear on this next point. Do not start 00:07:32.899 --> 00:07:36.439 by asking what the agent can do. Yeah. Instead, 00:07:36.600 --> 00:07:39.819 ask what painful, expensive business workflow 00:07:39.819 --> 00:07:43.220 it can fix. What's fascinating here is how unglamorous 00:07:43.220 --> 00:07:46.600 these valuable problems actually are. Businesses 00:07:46.600 --> 00:07:48.839 complain about the exact same mundane things 00:07:48.839 --> 00:07:52.420 every week. Highly paid employees waste hours 00:07:52.420 --> 00:07:56.279 searching for simple internal information. Managers 00:07:56.279 --> 00:07:58.680 repeat the same onboarding instructions to every 00:07:58.680 --> 00:08:01.860 new client. Weekly status reports take hours 00:08:01.860 --> 00:08:09.660 to write manually. You feel this. Oh, absolutely. 00:08:09.819 --> 00:08:12.379 The source outlines five specific business use 00:08:12.379 --> 00:08:14.720 cases to solve this. The first group is what 00:08:14.720 --> 00:08:17.279 I would call information wrangling. Yeah, making 00:08:17.279 --> 00:08:18.939 sense of the mess. This includes the internal 00:08:18.939 --> 00:08:21.220 knowledge assistant. Right. Company information 00:08:21.220 --> 00:08:24.100 is usually scattered across Slack, Notion, and 00:08:24.100 --> 00:08:27.899 CRMs. It is a tangled mess of overlapping documents. 00:08:28.139 --> 00:08:30.800 This assistant searches policies and standard 00:08:30.800 --> 00:08:33.549 operating procedures instantly. Employees do 00:08:33.549 --> 00:08:36.169 not waste time digging through ancient disorganized 00:08:36.169 --> 00:08:38.490 folders. The second use case in this group is 00:08:38.490 --> 00:08:41.389 client onboarding. This involves organizing intake 00:08:41.389 --> 00:08:45.110 forms and drafting initial kickoff briefs. Whoa. 00:08:45.769 --> 00:08:49.049 Imagine an agent seamlessly turning a chaotic 00:08:49.049 --> 00:08:51.830 pile of emails and forms into a perfect kickoff 00:08:51.830 --> 00:08:54.909 brief overnight. It's absolute magic for modern 00:08:54.909 --> 00:08:57.570 service businesses. Every new client needs intake 00:08:57.570 --> 00:09:00.950 forms, access requests, and specific timelines. 00:09:01.190 --> 00:09:03.850 Usually a human spends three hours hunting down 00:09:03.850 --> 00:09:06.169 missing attachments. The agent cleans up that 00:09:06.169 --> 00:09:08.690 messy input perfectly while you sleep. The next 00:09:08.690 --> 00:09:10.639 group of use cases. cases focuses on writing 00:09:10.639 --> 00:09:13.460 and pipeline velocity. Use case three is the 00:09:13.460 --> 00:09:15.659 reporting assistant. Nobody likes writing weekly 00:09:15.659 --> 00:09:18.120 status reports from scratch. No one. The agent 00:09:18.120 --> 00:09:19.840 collects the scattered meeting notes from the 00:09:19.840 --> 00:09:22.440 entire week. It highlights the critical blockers 00:09:22.440 --> 00:09:24.740 and formats a clean document automatically. Use 00:09:24.740 --> 00:09:27.220 case four is sales follow up. This one feels 00:09:27.220 --> 00:09:29.019 like it connects directly to the bottom line. 00:09:29.139 --> 00:09:31.779 It absolutely connects directly to revenue generation. 00:09:32.799 --> 00:09:35.559 Sales teams constantly lose deals because their 00:09:35.559 --> 00:09:39.100 internal notes are messy. Often, follow -up emails 00:09:39.100 --> 00:09:42.919 are sent three days late. The managed agent quietly 00:09:42.919 --> 00:09:45.100 listens to the meeting transcript in the background. 00:09:45.259 --> 00:09:48.960 It turns that transcript into updated deal statuses 00:09:48.960 --> 00:09:52.120 in the CRM. It even drafts the customized follow 00:09:52.120 --> 00:09:54.399 -up emails automatically after the call ends. 00:09:54.600 --> 00:09:57.500 The final use case is vertical sauce. This means 00:09:57.500 --> 00:10:00.179 building assistance for very specific, narrow 00:10:00.179 --> 00:10:02.500 industries. Yes. Instead of a general assistant, 00:10:02.720 --> 00:10:04.860 you build an expert. Right. You might build an 00:10:04.860 --> 00:10:06.639 assistant just for real estate listing notes, 00:10:06.720 --> 00:10:09.600 or you build an assistant strictly for legal 00:10:09.600 --> 00:10:12.399 case note organization. Looking back at the reporting 00:10:12.399 --> 00:10:14.519 assistant use case, I have a fundamental question. 00:10:14.980 --> 00:10:17.740 Why does the source suggest aiming for a 70 % 00:10:17.740 --> 00:10:21.759 to 80 % finished draft? Beep. Why not just promise 00:10:21.759 --> 00:10:24.399 the client full automation? Promising full automation 00:10:24.399 --> 00:10:27.879 usually sets you up for inevitable failure. AI 00:10:27.879 --> 00:10:30.639 models still hallucinate and edge cases always 00:10:30.639 --> 00:10:34.000 exist in business. Delivering a solid draft manages 00:10:34.000 --> 00:10:36.899 expectations and keeps humans safely in control. 00:10:37.200 --> 00:10:40.340 It is much easier to sell a highly reliable first 00:10:40.340 --> 00:10:43.620 draft. Right. A draft shifts the AI from an autonomous 00:10:43.620 --> 00:10:46.679 manager to a helpful intern. A draft is safer 00:10:46.679 --> 00:10:49.179 to trust and sell, keeping a human in the loop. 00:10:49.320 --> 00:10:52.559 Exactly. Sponsor placeholder. Welcome back to 00:10:52.559 --> 00:10:55.679 the Deep Dive. With all these incredible, profitable 00:10:55.679 --> 00:10:58.679 use cases, a big strategic question remains. 00:10:59.820 --> 00:11:02.399 Why should a business use clawed managed agents 00:11:02.399 --> 00:11:05.059 instead of other popular platforms? Why not just 00:11:05.059 --> 00:11:07.460 use something accessible like OpenClaw? Right. 00:11:07.580 --> 00:11:09.899 Or why not just spin up hundreds of simple single 00:11:09.899 --> 00:11:12.879 task bots? OpenClaw is still a fantastic tool 00:11:12.879 --> 00:11:15.759 for many situations. It's absolutely perfect 00:11:15.759 --> 00:11:18.840 for quick demos and rapidly testing ideas. Okay. 00:11:19.059 --> 00:11:21.679 It's also really good for less technical users 00:11:21.679 --> 00:11:23.679 who want to experiment. But a quick demo is not 00:11:23.679 --> 00:11:26.019 a daily reliable business tool. It is just a 00:11:26.019 --> 00:11:28.559 proof of concept. Exactly. A demo is just the 00:11:28.559 --> 00:11:30.899 very first. step of the journey. Cloud managed 00:11:30.899 --> 00:11:33.720 agents are designed for deep, reliable, continuous 00:11:33.720 --> 00:11:36.539 workflows. They are built for serious system 00:11:36.539 --> 00:11:40.000 integration and strict operational control. You 00:11:40.000 --> 00:11:41.759 can tether them securely to your proprietary 00:11:41.759 --> 00:11:44.620 company databases. I really love the forward 00:11:44.620 --> 00:11:47.320 slash new employee command idea from the source. 00:11:47.519 --> 00:11:50.440 It treats the creation of an agent exactly like 00:11:50.440 --> 00:11:52.600 a human job interview. It is a brilliant mental 00:11:52.600 --> 00:11:55.529 model for system builders. Yeah. Instead of randomly 00:11:55.529 --> 00:11:58.190 spinning up an agent, you must answer rigorous 00:11:58.190 --> 00:12:02.710 questions. What exactly is this AI employee responsible 00:12:02.710 --> 00:12:06.110 for doing? What sensitive actions should it never, 00:12:06.190 --> 00:12:10.210 ever perform? When must it pause and ask for 00:12:10.210 --> 00:12:13.009 explicit human approval? Establishing those boundaries 00:12:13.009 --> 00:12:15.230 prevents the trap of having too many agents. 00:12:15.490 --> 00:12:18.029 People on social media love bragging about having 00:12:18.029 --> 00:12:20.929 200 AI agents working simultaneously. It sounds 00:12:20.929 --> 00:12:23.009 incredibly impressive on a viral post. Yeah. 00:12:23.259 --> 00:12:25.299 But in reality, it usually creates a massive 00:12:25.299 --> 00:12:27.980 new operational mess. If you have 200 agents 00:12:27.980 --> 00:12:31.259 generating long, random updates, you fail. You 00:12:31.259 --> 00:12:33.059 just become a human bottleneck reading their 00:12:33.059 --> 00:12:35.379 endless spam. You spend your whole day reviewing 00:12:35.379 --> 00:12:38.960 robotic busywork. Yes. And that defeats the entire 00:12:38.960 --> 00:12:42.000 purpose of automation. Your day turns into reading 00:12:42.000 --> 00:12:44.940 repetitive agent outputs instead of making strategic 00:12:44.940 --> 00:12:47.860 progress. Right. The true goal is to drastically 00:12:47.860 --> 00:12:51.799 reduce manual human work. It is not to create 00:12:51.799 --> 00:12:55.580 a brand new layer of AI middle management. Let 00:12:55.580 --> 00:12:58.200 me push back on that idea for a second. Doesn't 00:12:58.200 --> 00:13:00.120 having more agents fundamentally mean you have 00:13:00.120 --> 00:13:03.129 more productivity? If I have more digital workers, 00:13:03.289 --> 00:13:05.830 that should equal more total output. It seems 00:13:05.830 --> 00:13:08.129 logical, but it breaks down in practice. Without 00:13:08.129 --> 00:13:10.970 clear, restricted workflows, more agents just 00:13:10.970 --> 00:13:13.690 create exponentially more noise. Okay. They generate 00:13:13.690 --> 00:13:16.049 conflicting data and overlapping reports. You 00:13:16.049 --> 00:13:17.929 end up spending your entire day managing their 00:13:17.929 --> 00:13:20.350 inevitable mistakes. Wow. You literally become 00:13:20.350 --> 00:13:23.110 a full -time editor for robotic spam. No, having 00:13:23.110 --> 00:13:25.389 too many agents just makes you a bottleneck reading 00:13:25.389 --> 00:13:27.649 their messy outputs. Exactly. Quality workflow 00:13:27.649 --> 00:13:30.299 is always better than quantity of agents. Which 00:13:30.299 --> 00:13:32.940 brings us to the final strategic advice for system 00:13:32.940 --> 00:13:35.620 builders. How do you actually start building 00:13:35.620 --> 00:13:38.000 this the right way? How do you avoid the spam 00:13:38.000 --> 00:13:40.879 trap? The first absolute rule is to start very 00:13:40.879 --> 00:13:44.139 small. You have to pick one single, highly specific 00:13:44.139 --> 00:13:47.419 workflow. It needs to have messy, unstructured 00:13:47.419 --> 00:13:50.440 input on one side. It needs clear, structured 00:13:50.440 --> 00:13:52.679 output on the other side. For example, messy 00:13:52.679 --> 00:13:55.759 client call notes go in. A perfectly formatted, 00:13:55.799 --> 00:13:59.210 clean summary document comes out. Then you evaluate 00:13:59.210 --> 00:14:02.629 it using a very simple test scorecard. Did this 00:14:02.629 --> 00:14:05.730 specific agent actually save me time today? Yeah. 00:14:05.870 --> 00:14:09.210 Was the output easy to review and approve? Right. 00:14:09.309 --> 00:14:12.889 And most importantly, would I voluntarily use 00:14:12.889 --> 00:14:15.529 it again tomorrow? If the honest answer is no, 00:14:15.730 --> 00:14:18.230 you must stop and fix the workflow. Okay. Do 00:14:18.230 --> 00:14:20.350 not add more complex features to a fundamentally 00:14:20.350 --> 00:14:23.149 broken workflow. You also need to build a highly 00:14:23.149 --> 00:14:25.759 repeatable setup. You must define a clear role, 00:14:25.940 --> 00:14:29.059 strict inputs and an exact output format. You 00:14:29.059 --> 00:14:31.860 also need a rigid review rule and a clear failure 00:14:31.860 --> 00:14:34.000 rule. This discipline stopped you from building 00:14:34.000 --> 00:14:36.539 random disconnected vanity agents. You end up 00:14:36.539 --> 00:14:38.580 building a modular system you can easily reuse. 00:14:38.720 --> 00:14:40.340 Right. You can package it and deploy it for your 00:14:40.340 --> 00:14:43.080 very next client. Another massive piece of advice 00:14:43.080 --> 00:14:46.220 from the source is to pick one market. Do not 00:14:46.220 --> 00:14:48.700 try to build generic AI for general business. 00:14:49.100 --> 00:14:52.080 That is a very common trap. Instead, build an 00:14:52.080 --> 00:14:54.740 AI reporting assistant specifically for marketing 00:14:54.740 --> 00:14:58.000 agencies. Study exactly what marketing account 00:14:58.000 --> 00:15:01.059 managers do every single week. Find out which 00:15:01.059 --> 00:15:03.919 highly specific tasks waste their valuable time. 00:15:04.120 --> 00:15:06.460 The deeper you know the specific market, the 00:15:06.460 --> 00:15:09.179 better the final tool. Finally, you have to turn 00:15:09.179 --> 00:15:12.139 your internal tests into undeniable proof. You 00:15:12.139 --> 00:15:14.679 must track your before and after workflow metrics 00:15:14.679 --> 00:15:18.379 incredibly carefully. You need to say a client 00:15:18.379 --> 00:15:21.899 brief used to take 45 minutes. Now a high quality 00:15:21.899 --> 00:15:25.059 draft is ready for review in exactly eight minutes. 00:15:25.200 --> 00:15:27.139 That is the real proof that executives actually 00:15:27.139 --> 00:15:29.980 want to buy. I want to ask you about that specific 00:15:29.980 --> 00:15:33.100 market focus strategy. Why is picking one specific 00:15:33.100 --> 00:15:37.299 niche market so crucial for success? Beat. Especially 00:15:37.299 --> 00:15:39.740 when modern AI models can theoretically do almost 00:15:39.740 --> 00:15:42.019 anything. Because specific markets have highly 00:15:42.019 --> 00:15:44.519 unique repeatable documents and internal jargon. 00:15:45.199 --> 00:15:47.340 Dentists have different operational pain points 00:15:47.340 --> 00:15:50.120 than corporate lawyers. A generic AI requires 00:15:50.120 --> 00:15:52.639 too much custom instruction every single time 00:15:52.639 --> 00:15:56.019 you use it. A niche AI already knows the exact 00:15:56.019 --> 00:15:58.879 context and the specific rules. Understanding 00:15:58.879 --> 00:16:02.039 one market deeply makes it way easier to solve 00:16:02.039 --> 00:16:05.059 their specific expensive problems. Exactly. You 00:16:05.059 --> 00:16:07.740 ultimately sell the clear business result, not 00:16:07.740 --> 00:16:11.279 the underlying technology. Nobody buys AI just 00:16:11.279 --> 00:16:14.340 to have AI anymore. No. They buy a solution to 00:16:14.340 --> 00:16:17.379 a painful, expensive, operational bottleneck. 00:16:17.600 --> 00:16:20.120 Let us synthesize this entire journey for a moment. 00:16:20.590 --> 00:16:23.230 The era of buying random, disconnected AI seats 00:16:23.230 --> 00:16:25.769 is quickly ending. It really is. The wild west 00:16:25.769 --> 00:16:27.990 of scattered chatbots is closing. The future 00:16:27.990 --> 00:16:30.669 belongs to structured, cloud -based, managed 00:16:30.669 --> 00:16:33.450 agents. These are specialized systems that possess 00:16:33.450 --> 00:16:35.909 actual cognitive architecture. They have robust, 00:16:35.990 --> 00:16:39.269 organized memory files. They execute daily sleep 00:16:39.269 --> 00:16:41.750 cycles to clean up their context. They follow 00:16:41.750 --> 00:16:45.509 highly specific, rigid prompt rules. Most importantly, 00:16:45.649 --> 00:16:48.269 they solve incredibly painful, expensive, and 00:16:48.269 --> 00:16:50.399 specific business problems. Two sec silence. 00:16:50.860 --> 00:16:52.539 So I want to leave you with a final thought to 00:16:52.539 --> 00:16:54.860 mull over today. We are successfully building 00:16:54.860 --> 00:16:57.799 AI systems with absolutely perfect sleep cycles 00:16:57.799 --> 00:17:01.899 now. These digital agents cleanly review their 00:17:01.899 --> 00:17:04.920 chaotic day. Right. They neatly file away their 00:17:04.920 --> 00:17:07.880 important memories into readable documents. They 00:17:07.880 --> 00:17:10.420 prep smoothly for tomorrow without any residual 00:17:10.420 --> 00:17:13.039 stress or anxiety. Which is amazing. What does 00:17:13.039 --> 00:17:15.819 that actually say about our own human sleep cycles? 00:17:16.099 --> 00:17:19.259 Are we as good at cleanly putting our own work 00:17:19.259 --> 00:17:21.019 away at the end of the day? That is a great question. 00:17:21.220 --> 00:17:23.099 Or is the artificial intelligence already out 00:17:23.099 --> 00:17:25.880 managing our own mental bandwidth? Neat. Thank 00:17:25.880 --> 00:17:27.599 you for joining us on the Seep Dive. Take care 00:17:27.599 --> 00:17:27.920 out there.