[0:00] Most people spend hours every week doing
[0:02] repetitive work that could easily be
[0:05] done in minutes. So, checking emails,
[0:08] following up with leads, writing
[0:09] reports, and then copying data from one
[0:12] app to another might seem small on their
[0:15] own, but together they do take up a huge
[0:18] amount of time. And the surprising part
[0:20] is that most people already have access
[0:23] to AI, yet they're still using it like a
[0:26] simple tool instead of something that
[0:29] can actually work for them. So, in this
[0:31] video, you're going to change that.
[0:33] We're going to change that because we're
[0:35] going to build your first AI automation
[0:37] agent step-by-step. Not just something
[0:39] that gives you answers, but a system
[0:43] that can take real action in the
[0:45] background. So, that by the end, you're
[0:46] going to have an agent that can sort
[0:48] through emails, flag urgent messages,
[0:51] follow up with leads, and even generate
[0:54] reports automatically. So, this is the
[0:57] kind of setup that keeps working even
[0:59] when you're not. Everything here is
[1:01] designed for beginners. There's no
[1:02] coding, no complicated setup, and no
[1:05] technical background needed. You'll see
[1:07] exactly what to click, what to type, and
[1:09] how each step connects so you can follow
[1:11] along without getting lost. And once
[1:13] this is set up, it doesn't stop working.
[1:15] It runs in the background, it handles
[1:17] repetitive tasks, and it gives you back
[1:19] time every single day. The best AI
[1:22] automation tool at this moment is Base
[1:23] 44, and I added a special link in the
[1:25] description so you can check it out,
[1:27] too. Now, if you want to master Base 44
[1:29] and learn how to build profitable AI
[1:31] automations agents websites and
[1:33] mobile apps with AI, I've created a
[1:35] complete masterclass that shows you
[1:37] exactly how to do it all step-by-step.
[1:40] And this masterclass normally costs $299
[1:42] to join, but since you are watching this
[1:44] video, thank you very much, you can join
[1:45] completely free. Just check out that
[1:47] link in the description down below to
[1:48] get free access to my Base 44
[1:50] masterclass, and you can start building
[1:52] your own AI-powered business today. So,
[1:54] before we build anything, you do need to
[1:57] understand one key difference because
[1:59] this is what separates basic AI usage
[2:01] from real automation. Most people assume
[2:04] AI automation simply means chatting with
[2:06] a bot, typing a request, and getting a
[2:07] response back, and that's not what it
[2:09] actually is. A chatbot gives you answers
[2:12] while an AI agent takes action. And that
[2:15] difference completely changes how you
[2:17] use AI in your daily work. So, for
[2:19] example, if you ask a chatbot draft an
[2:21] email reply to follow up on my clients
[2:23] booking today, it will generate a
[2:25] well-written for you. And it sounds
[2:27] helpful, and it is, but your work isn't
[2:29] finished. You still have to open Gmail.
[2:31] You still have to go through each
[2:32] message and copy the response and paste
[2:34] it and then send it manually. Now,
[2:36] compare that to an AI agent. You give it
[2:38] one clear instruction, just something
[2:40] like send appointment confirmation to
[2:42] all the clients that we have on our
[2:43] sheet, and that actually completes the
[2:45] task. No copying, no switching between
[2:48] tabs, no repetitive sending. The whole
[2:50] system just handles everything for you.
[2:52] And this is what AI automation really
[2:54] means. It goes beyond generating ideas
[2:57] or tasks, and it moves into actually
[2:58] completing tasks on your behalf. And
[3:01] that shift is usually the moment when
[3:03] beginners start to see the real value. A
[3:05] chatbot helps you think faster, but an
[3:07] AI agent reduces the amount of work you
[3:09] have to do. That's the real advantage
[3:11] here. So, let me show you a quick
[3:13] side-by-side so you can clearly see
[3:15] what's happening here. On the left side,
[3:17] we have a normal chatbot, and let's
[3:19] type, "Can you help me follow up with
[3:21] leads for my salon business?" And it
[3:23] gives me a clean message template. It
[3:24] looks polished, and it's ready to use,
[3:26] but I still need to copy it to paste it
[3:28] into email and then send it one by one.
[3:30] Now, on the right side, I give the AI
[3:32] agent this instruction, "Send a
[3:34] personalized follow-up to all of our
[3:36] clients." And that's it. The agent sends
[3:38] everything automatically. There's no
[3:40] manual work, no waiting around, and no
[3:43] chance of forgetting someone. The task
[3:45] just gets done in the background. And
[3:47] this is why more businesses are moving
[3:49] towards automation. The difference in
[3:51] speed is massive. A person can forget, a
[3:54] chatbot waits for you to act, an AI
[3:56] agent just moves and finishes the job.
[3:59] So, now let's make this real. I'm going
[4:00] to show you an AI agent handling an
[4:02] actual task, so you can see exactly how
[4:04] it works before we build your own.
[4:06] So, now we're going to build your first
[4:08] real AI agent. And by the end of this
[4:10] part, you're going to have something
[4:11] that actually works in the background
[4:14] for you, not just something you test
[4:16] once and forget about. So, this one is
[4:18] going to handle your inbox while you
[4:20] sleep. So, when you wake up, a big chunk
[4:22] of your routine is already done. So,
[4:24] let's start by opening up your browser
[4:26] and going to Base 44. And once you land
[4:28] on the sign-up page here, click get
[4:30] started. You'll see a simple account
[4:32] creation screen, and you can either sign
[4:34] up with Google or use your email. It
[4:36] only takes about a minute or two, so
[4:37] don't overthink it here. And this step
[4:39] matters more than it seems because
[4:41] everything we're about to build will
[4:42] live inside this dashboard. And once you
[4:45] are in, you'll land on the main
[4:46] interface here, and it's pretty
[4:48] straightforward. So, just take a few
[4:50] seconds to, you know, look around and
[4:52] get comfortable. And on the left side
[4:54] here, you'll see your agents panel, and
[4:55] that's where all the agents you create
[4:57] will be listed. In the center here,
[4:59] you'll see the prompt workspace, and
[5:01] this is where everything happens. You
[5:02] don't drag blocks or write code here,
[5:04] you just describe what you want, and the
[5:06] system builds it for you. And that's
[5:08] really the key idea you need to
[5:10] understand before moving forward. You
[5:12] don't need to code anything. You're not
[5:14] setting up complicated logic. You're
[5:17] simply telling the system what job you
[5:18] want handled, and it translates that
[5:20] into a working process behind the
[5:22] scenes. Once it clicks, everything else
[5:24] becomes a lot easier. So, now let's go
[5:26] ahead and build your first one. So,
[5:28] let's start with a problem almost
[5:30] everyone already knows. You wake up, you
[5:33] open up your inbox, and immediately feel
[5:35] behind. Important emails are mixed in
[5:38] with spam, client messages are buried
[5:39] under promotions, and small replies that
[5:42] should take a minute somehow end up
[5:44] eating a a part of your morning. It's
[5:47] repetitive, it's annoying, and it's
[5:49] exactly the kind of work AI is good at
[5:51] handling. So now click create new agent,
[5:53] a prompt box will appear, and this is
[5:55] where we tell the agent what job we want
[5:58] it to handle. And we'll name this one
[6:00] email management agent. So now type in
[6:02] this exact prompt, scan my inbox
[6:04] overnight, flag urgent emails, and send
[6:06] responses to simple ones. So keep your
[6:08] prompt simple like that, you don't need
[6:10] to overexplain or make it sound
[6:11] technical. Just tell it the task and
[6:14] when it should happen and what outcome
[6:15] you want. And that's enough for the
[6:17] system to understand what you're trying
[6:20] to build, and you'll see it start
[6:21] putting the steps together all
[6:23] automatically. And after that, it asks
[6:25] which email provider we're using, and I
[6:27] respond with Gmail. And at this point it
[6:29] will ask for permission to connect to
[6:31] your Gmail. So go ahead and authorize
[6:33] it. And what's nice here is that you're
[6:35] not manually building some complicated
[6:37] workflow with logic blocks and
[6:39] conditions. You're giving one
[6:41] instruction, and then the platform
[6:43] translates that into a whole working
[6:45] process for you.
[6:47] And once that connection is done, the
[6:48] next step is giving your agent the right
[6:50] knowledge so that it can respond
[6:52] properly. So here, let's go to brain,
[6:54] then click upload knowledge, and this
[6:56] part is important because it gives your
[6:58] agent context. You can upload things
[7:00] like facts, SOPs, service menus, pricing
[7:03] sheets, client scripts, or company
[7:04] policies. And let's say this is for a
[7:06] salon booking business. And in that
[7:08] case, you'd upload your service list,
[7:10] your pricing, your opening hours, your
[7:12] cancellation policy, and common customer
[7:14] questions. And that way when the agent
[7:16] replies, it's not just guessing or
[7:18] giving some generic answer, it's using
[7:20] your actual business information to
[7:22] respond. And that leads to fewer
[7:24] mistakes and more accurate replies and a
[7:26] much more consistent tone. It stops
[7:28] sounding like a random AI tool and
[7:30] starts sounding like something that
[7:32] actually understands how your business
[7:34] works.
[7:35] So now let's test it. I'm going to send
[7:37] an email to my own inbox with a meeting
[7:40] confirmation request. And since the
[7:42] knowledge is already uploaded, the agent
[7:44] can use that information right away. So,
[7:46] if someone asks about, say, services,
[7:49] refund rules, availability, or business
[7:51] hours, it already has what it needs to
[7:53] respond properly. And now, watch what
[7:55] happens. The agent scans the unread
[7:58] email and then responds to the inquiry
[8:00] almost instantly. And this is usually
[8:02] the moment when people start to really
[8:04] get it because the value becomes obvious
[8:06] very quickly. A task that normally takes
[8:09] 20 to 30 minutes of sorting and reading
[8:11] and replying is already handled for you.
[8:13] And just like that, you've built your
[8:15] first working AI agent. It can now run
[8:17] automatically every night, which means
[8:19] tomorrow morning your inbox can already
[8:21] be organized before your day even
[8:23] starts. But here's the thing, as you've
[8:25] seen, Base 44 is incredibly powerful,
[8:27] but most people still don't know how to
[8:29] use it properly. They end up building
[8:31] basic apps that don't make money or
[8:33] websites that can't even convert. And
[8:35] that's exactly why I created my own
[8:38] complete Base 44 masterclass. Inside
[8:40] this course, I'm going to show you
[8:41] step-by-step how to build profitable
[8:43] SaaS businesses, high-converting
[8:45] websites, and mobile apps, all using AI
[8:47] with zero coding required, of course.
[8:49] You're going to learn how to build SaaS
[8:51] apps that solve real problems and
[8:52] generate recurring revenue. Also, the
[8:55] exact prompts and strategies that I use
[8:57] to create professional websites in
[8:58] minutes, along with how to clone
[9:00] successful apps and then add your own
[9:02] profitable twist, and my proven system
[9:04] for turning Base 44 projects into actual
[9:06] income streams. This is not just theory.
[9:08] I'm going to walk you through real
[9:10] builds. I'm going to show you my exact
[9:11] process, and of course, give you the
[9:13] templates and frameworks that have
[9:15] helped my students launch successful
[9:17] AI-powered businesses. So, if you're
[9:19] serious, serious about building
[9:20] something profitable with AI in 2026,
[9:23] you got to click the link in the
[9:24] description below to join my Base 44
[9:27] masterclass. Your future self will thank
[9:29] you for taking action today instead of
[9:30] just watching another tutorial. All
[9:32] right, so this is only the first
[9:34] example. Let's go ahead and check out
[9:35] some others. So, we're going to build
[9:37] now an agent that can actually help
[9:40] generate revenue because missed leads
[9:42] are one of the biggest silent killers in
[9:44] any business. A lead comes in, you plan
[9:47] to respond, and something else takes
[9:48] your attention. 10 minutes turns into 2
[9:51] hours, 2 hours turns into the next day,
[9:53] and by then the opportunity is gone. And
[9:55] that's the exact gap that this agent is
[9:57] designed to fix. So, here click super
[10:00] agent and give it a name. This time
[10:02] we're in creating a follow-up system, so
[10:04] the goal is simple. The moment a lead
[10:06] appears, a response is already in
[10:08] motion. In the prompt box, type in this
[10:10] exactly. You are a lead follow-up agent
[10:13] for my real estate property company.
[10:15] When a new lead comes in, send a
[10:17] personalized follow-up automatically.
[10:20] Let's keep the structure clear and
[10:21] direct. Start with the role, then the
[10:23] trigger, and then the action. And that
[10:25] format helps the platform understand
[10:27] what you want and build the workflow
[10:29] correctly without extra adjustments. So,
[10:32] to complete the setup, you'll answer a
[10:33] few questions from super agent, and
[10:35] these questions help define how the
[10:37] agent should behave and where it should
[10:40] get its data from. The next step is
[10:42] connecting your lead source. So, use
[10:44] Google Sheets for this example.
[10:46] Authorize access to your Google account,
[10:48] so the system can read your data. And
[10:51] this option just keeps things simple and
[10:53] easy to follow, especially if you're
[10:54] just getting started. And once it is
[10:57] connected, select the spreadsheet where
[10:59] your leads are stored. And for this
[11:01] demo, just keep it simple again with
[11:02] columns like name and email. And that's
[11:05] enough for the agent to personalize
[11:07] messages and know where to send them.
[11:10] And this step plays a big role in how
[11:12] natural the output feels because the
[11:14] cleaner your sheet is, the better the
[11:16] responses will sound. And if your data
[11:17] is organized, then the messages will
[11:19] feel intentional and relevant rather
[11:21] than generic. You're essentially giving
[11:23] the agent the context it needs before it
[11:26] begins working. And after connecting the
[11:28] Google Sheet, the AI agent begins
[11:30] sending customized follow-up emails
[11:31] automatically. And this is the point
[11:33] where businesses stop losing warm leads
[11:36] simply because of slow responses. And
[11:38] timing matters here. In many cases, the
[11:41] first helpful reply is the one that gets
[11:43] the conversion. And at this stage,
[11:45] you've built your second working AI
[11:47] agent. Responds faster than most teams,
[11:49] and it does it consistently. No missed
[11:51] follow-ups, no delays, and no reliance
[11:53] on someone remembering to reply. Not
[11:56] long ago, workflows like this required
[11:58] developers, custom scripts, and ongoing
[12:00] maintenance. Here, the same result comes
[12:02] from a few clear instructions and Mayb
[12:05] simple connection. And the next part
[12:07] will make this even clearer because
[12:09] there's a reason that this approach
[12:11] feels easier than traditional automation
[12:13] tools, and most people end up
[12:15] overcomplicating it without even
[12:17] realizing why.
[12:19] So, let's build an agent for reporting
[12:21] because this is one of those tasks that
[12:23] sound small until you realize how much
[12:25] time it quietly takes every single week.
[12:28] A lot of business owners still do this
[12:29] manually. They open up their sales
[12:31] dashboard, copy numbers into a
[12:33] spreadsheet, check what changed, look
[12:35] for patterns, write a summary, and then
[12:37] send it to the team. None of that sounds
[12:39] difficult on its own, but together, it
[12:41] adds up fast. And the frustrating part
[12:44] is that it's not a one-time task. You do
[12:46] it again next week, and then again after
[12:48] that. So, we're going to automate the
[12:50] whole thing. Let's go back to the Base44
[12:52] dashboard here and click create new
[12:53] agent. As the setup begins, answer the
[12:56] questions it gives you so the platform
[12:57] can shape the agent around the tasks
[12:59] that you want it to handle. To connect
[13:01] the data source, choose the integration
[13:03] that you want to use. And for this
[13:05] example, I'm choosing Google Sheets
[13:06] because, again, it's simple, it's
[13:08] familiar, and easy to test with real
[13:10] business data. And for the prompt, type
[13:12] in this exactly: Use my Google Sheet
[13:14] called Weekly Sales Dashboard with one
[13:16] tab named Sales Data. Please create a
[13:19] report every Friday at 4:00 p.m. that
[13:21] summarizes weekly revenue, order volume,
[13:23] top product, refund trends, and
[13:26] best-performing sales channel. And then
[13:28] send the report to Telegram in
[13:29] #weekly-reports
[13:31] with a short business summary and key
[13:33] insights. So, that single prompt is
[13:36] doing a lot of work. It tells the agent
[13:37] where to get the data, what to look for,
[13:40] when to run the task, and where to send
[13:41] the final result. In other words, you're
[13:43] giving it three jobs in one instruction.
[13:45] Get the data, understand the data, and
[13:47] send the result. And after that, enter
[13:50] the specific Google Sheet URL so the
[13:51] agent knows exactly which file to use.
[13:54] And the next step is connecting
[13:56] Telegram, so the report has somewhere to
[13:58] go once it's generated. Set up the AI
[14:01] agent inside Telegram so I can send the
[14:03] final output directly into the right
[14:05] place. And once that's ready, let's go
[14:07] ahead and test it live inside Telegram.
[14:09] I'm going to ask the agent to generate
[14:10] this week's report right now so we can
[14:12] see the result before waiting for the
[14:14] scheduled Friday run. And perfect. The
[14:17] report comes in through Telegram, and
[14:19] that's exactly what we wanted here. The
[14:21] summary is there, the numbers are pulled
[14:23] in, and the key insights are already
[14:25] written out without needing to build the
[14:27] report manually. And that basic
[14:29] structure can power a lot of useful
[14:31] automations, but reporting is one of the
[14:33] best examples because it turns recurring
[14:36] admin work into something fully
[14:37] automatic. It keeps happening on
[14:39] schedule, the output stays consistent,
[14:41] and no one has to remember to do it. And
[14:44] once you see it working live, the bigger
[14:46] question then starts to come up
[14:47] naturally. If building something like
[14:49] this can be this straightforward, why do
[14:52] so many people still struggle with
[14:54] automation? And that's what I want to
[14:56] get into next because there's a reason
[14:58] this feels easier than traditional
[15:00] automation tools, and the old the way
[15:02] just tends to lose most beginners very
[15:04] quickly. So, let's talk about why this
[15:06] feels so much easier, especially if
[15:08] you've tried automation tools before,
[15:10] and it just didn't stick with you. So,
[15:12] most people hit a wall pretty quickly
[15:14] the traditional way. You open up a
[15:16] workflow builder, and suddenly you're
[15:18] dragging blocks across the the setting
[15:20] conditions, writing logic, testing if it
[15:23] works, fixing errors when it doesn't,
[15:24] and then repeating the whole process all
[15:26] over again. And it starts to feel less
[15:28] like solving a simple problem and more
[15:30] like trying to learn a new system from
[15:32] scratch. For beginners, it can feel like
[15:34] learning a second language. One small
[15:37] mistake, like a broken trigger or a
[15:38] missing condition, can stop the entire
[15:41] workflow from working, and then you're
[15:42] stuck trying to figure out what went
[15:44] wrong. Now, compare that to what we just
[15:46] did. We didn't touch any complex
[15:48] builders, we didn't set up logics
[15:50] step-by-step. We simply typed a prompt,
[15:52] described what we wanted, and the system
[15:54] handled the rest. And that's the
[15:56] difference here. The older approach
[15:58] forces you to think like a developer.
[16:00] You have to break everything down into
[16:02] steps and conditions and technical
[16:04] logic. Base 44 shifts that completely.
[16:07] It lets you think like an operator. You
[16:09] focus on the outcome, you describe the
[16:11] job, and the platform builds the process
[16:13] behind the scenes. And that change
[16:15] removes a lot of friction. There's less
[16:17] setup, fewer points where things can
[16:18] break, and more time spent actually
[16:20] getting results. And that speed matters
[16:23] more than people realize, because most
[16:24] people don't struggle because they lack
[16:27] ideas. No, they struggle because the
[16:29] setup takes too long, and they lose
[16:31] momentum before anything is even
[16:32] finished.
[16:33] So, let me show you where this starts to
[16:35] become really powerful, and it all comes
[16:38] down to integrations. An AI agent on its
[16:40] own is useful, but once it can move
[16:42] between different apps and handle data
[16:44] across them, then that's when it starts
[16:46] to feel like a real system working for
[16:48] you. So, let's click on integrations
[16:50] here. We're going to connect a few tools
[16:52] live, so you can see how quickly this
[16:53] comes together. Start with Gmail, click
[16:56] connect, authorize access, and that's
[16:58] it. The setup is now done. Your AI agent
[17:00] can now send emails directly, which
[17:02] means things like reports and updates or
[17:04] follow-ups can go out automatically
[17:06] without you touching anything. So, next
[17:08] up, connect Google Calendar. Authorize
[17:11] access the same way. Once that's done,
[17:13] your agent can now check your schedule
[17:15] before sending anything, and that means
[17:17] it won't send reports at the wrong time
[17:19] or conflict with your workflow. It can
[17:21] actually work around your day. So, now
[17:24] connect Google Analytics and go through
[17:26] again the same process and authorize
[17:28] access. And once it's connected, your
[17:30] agent can now pull in data like website
[17:33] traffic and sessions and bounce rate and
[17:35] conversions and combine that with your
[17:37] existing data. And at this point, you've
[17:39] connected three tools in just a few
[17:41] minutes. And if you include the Google
[17:43] Sheet we connected earlier, that's
[17:44] already four integrations working
[17:46] together inside one system. And that
[17:49] speed is what makes this different.
[17:50] Older automation setups usually involve
[17:52] going through API documentation and
[17:54] generating tokens, mapping fields
[17:56] manually, and then testing everything
[17:58] step-by-step. And that process can take
[18:00] hours or even days if you're not
[18:02] familiar with it. Here, it's just point
[18:04] and click. You connect what you need and
[18:05] the system handles the rest. And so, the
[18:07] focus isn't on making things technically
[18:09] complex. The focus is getting something
[18:12] working as quickly as possible so you
[18:14] can actually use it. So, now that we've
[18:17] seen how the workflow actually comes
[18:19] together, it does help to look at a
[18:21] couple of simple use cases that you can
[18:23] apply right away. And these aren't
[18:24] complicated builds, but they do solve
[18:26] real problems and save time almost
[18:28] immediately once they're set up. What
[18:30] matters here is not how advanced the
[18:32] setup looks, but how practical it is.
[18:35] And these examples show how flexible AI
[18:37] automation can be even with very simple
[18:38] instructions. So, once you understand
[18:40] the pattern, you can take the same idea
[18:43] and then apply it to almost any
[18:44] repetitive task that you deal with.
[18:47] So, for example, let's start with
[18:48] something more personal so you can see
[18:51] that this isn't only useful for business
[18:53] tasks. A travel planning agent is one of
[18:55] the easiest automations to build, and
[18:57] it's also one of the most practical
[18:59] because it saves you from doing the kind
[19:01] of research that usually takes way
[19:03] longer than it should. So, go back to
[19:05] the dashboard here, and then let's go
[19:06] ahead and click create new agent. And
[19:08] when the prompt box appears, type this
[19:10] exactly. You're going to find the best
[19:12] flight and hotel deals. That's it.
[19:14] That's enough to get the workflow
[19:16] moving. And once you enter it, the
[19:17] platform begins building the process for
[19:19] you. It starts by searching for flight
[19:21] options, and it compares hotel prices.
[19:23] And after that, it ranks the best
[19:25] choices based on things like price and
[19:27] convenience. And once everything is
[19:29] processed, it sends you a summary within
[19:31] seconds. And this is the point where AI
[19:33] starts to feel genuinely useful in
[19:34] everyday life. You're no longer opening
[19:36] 10 tabs, checking different websites,
[19:39] comparing prices manually, and trying to
[19:41] remember which option was actually the
[19:43] best. The work is already done for you
[19:45] here, and you get a cleaner decision
[19:46] much faster. And what you end up with is
[19:49] simple but valuable. Faster decisions,
[19:51] less stress, and often better deals
[19:52] because the comparison happens so
[19:54] quickly. And once you understand the
[19:56] pattern, you can use the same structure
[19:58] for other personal tasks, too. You can
[20:01] apply it to meal planning, daily
[20:02] scheduling, budget tracking, or even
[20:04] study reminders. The logic stays the
[20:06] same, only the outcome changes. Let's
[20:09] switch things up to a business use case,
[20:11] because customer support is one of the
[20:12] highest value automations that you can
[20:14] build. Because slow support creates
[20:16] problems very quickly. So, when
[20:18] customers ask a simple question and then
[20:20] don't get a response fast enough, then
[20:23] confidence drops almost immediately.
[20:25] Even when the issue was small, the delay
[20:27] makes the business feel disorganized.
[20:30] So, now click create new agent, and in
[20:31] the prompt box, type this exactly. When
[20:34] a customer asks about order status,
[20:35] check the tracking sheet and send the
[20:37] latest update. And as soon as you enter
[20:39] that, the workflow starts building right
[20:41] away. And the next step is connecting
[20:43] the order sheet. So, integrate Google
[20:46] Sheets. And once that's connected, your
[20:48] AI agent can look up order status using
[20:50] an order ID or customer name, pull the
[20:52] latest tracking details from the sheet,
[20:55] and then send those details directly to
[20:56] the customer, and keep a record of what
[20:58] happened after the support interaction.
[21:01] After that, connect Gmail as well,
[21:02] because that gives the agent a way to
[21:04] monitor incoming emails for order status
[21:06] questions, and reply using the latest
[21:08] information from your tracking sheet.
[21:10] Send more personalized updates and log
[21:13] which emails have already been handled.
[21:15] From there, let's go ahead and enter the
[21:17] specific Google Sheet URL into Super
[21:19] Agent and then answer the remaining
[21:21] setup questions so it knows exactly
[21:23] where to pull the tracking data from and
[21:25] how it should respond. And once
[21:27] everything is connected, let's go ahead
[21:29] and test it with a real example. Ask the
[21:31] AI agent to look up the customer's order
[21:33] in your Google Sheet and send their
[21:35] shipping status, courier, and estimated
[21:37] delivery date. And as you can see, there
[21:39] it goes. The AI agent sends the email
[21:41] directly to the customer. It pulls in
[21:43] the shipping status, and estimated
[21:46] delivery from the Google Sheet. Then it
[21:48] turns that information into a clear
[21:49] update without anyone needing to check
[21:51] the order manually. And that's exactly
[21:54] how support like this becomes faster and
[21:56] more reliable. A task that usually
[21:58] involves opening the sheet and searching
[22:00] for the customer and checking the latest
[22:02] update and then writing the email and
[22:04] then sending it can all be handled now
[22:06] in seconds. The customer gets an answer
[22:08] quickly and your team doesn't have to
[22:09] keep repeating the same process all day.
[22:12] And fast replies like this naturally
[22:14] build trust because customers feel
[22:16] informed without needing a follow-up
[22:18] multiple times. There's no waiting,
[22:19] there's no back and forth, and there's
[22:21] no need for someone to manually check
[22:23] every single request. And everything
[22:25] just flows in the background and the
[22:27] experience feels smooth on both sides.
[22:29] And over time, this kind of set of
[22:31] changes how support feels inside a
[22:33] business. Simple questions no longer
[22:35] slow things down and your team can focus
[22:37] on situations that actually need
[22:39] attention. And once you see it working
[22:41] like this, we're going to take it a step
[22:42] further now because there are features
[22:45] that can make these agents feel even
[22:46] more capable, like memory, multi-step
[22:49] workflows, and decision-making logic. So
[22:52] far, the agents we've built focus on
[22:54] speed. They respond quickly, they handle
[22:56] tasks automatically, and they remove a
[22:58] lot of manual work. And that alone is
[23:00] already useful, but speed is only one
[23:02] part of it. What really changes the
[23:04] experience is when these agents start to
[23:06] feel more intelligent in how they
[23:08] operate. The next two features are what
[23:10] create that shift. They take what looks
[23:12] like simple automation and then turn it
[23:14] into something that can adapt and make
[23:16] decisions and handle more complex
[23:18] situations without needing constant
[23:20] input. And this is the point where it
[23:22] starts to feel less like a tool and more
[23:24] like a system that actually understands
[23:26] what it's doing. Let's start with memory
[23:29] because this is one of the most powerful
[23:31] features you can add to an AI agent.
[23:33] When memory is enabled, the agent no
[23:35] longer treats every interaction as a
[23:37] completely new task. Rather, it builds
[23:40] on previous actions, which makes the
[23:42] entire workflow feel more consistent and
[23:44] closer to how a real assistant would
[23:46] operate. To see how this works in
[23:47] practice, let's go back to one of the
[23:49] agents that we've already built. I've
[23:51] made a small change to the data by
[23:52] updating a couple of rows in the sheet
[23:54] just to test whether the agent can
[23:56] recognize those changes instead of
[23:58] repeating the same output. So, now let's
[24:00] ask this exact question. Recently, I
[24:03] asked you to generate and send this
[24:04] week's sales report. Does it have any
[24:06] new data now? A basic chatbot would
[24:08] treat that as a brand new request. It
[24:10] wouldn't remember the previous report,
[24:12] and it wouldn't have any awareness of
[24:14] what changed. It would simply generate a
[24:16] fresh answer without any reference
[24:18] point. But in this case, the agent
[24:20] behaves differently. It checks the
[24:22] previous conversation history. It
[24:24] recalls the earlier report it generated,
[24:27] and then it goes back to the Google
[24:28] Sheet to review the latest data. It
[24:30] compares the updated rows with what it
[24:32] saw before, and within seconds, it sends
[24:34] a refreshed summary based on those
[24:36] differences. And you can immediately see
[24:39] the impact here. The total revenue
[24:40] reflects the new numbers, the order
[24:42] volume is updated, and the trend summary
[24:45] adjusts based on the latest data we
[24:46] added. Nothing is repeated blindly, and
[24:49] nothing is overlooked. And that's the
[24:50] real value of memory combined with live
[24:53] data access. The agent is no longer just
[24:55] answering a single prompt in isolation.
[24:57] It keeps track of what has already
[24:59] happened, connects it with new
[25:01] information, and then builds a response
[25:03] that actually moves forward instead of
[25:05] starting over again. To make the agent
[25:07] more intelligent, yep, the next step is
[25:09] adding decision-making into the workflow
[25:11] rather than having it follow a single
[25:13] fixed action every time. Conditional
[25:15] logic is what enables that behavior. It
[25:17] allows the agent to look at the data it
[25:19] receives, understand the situation, and
[25:21] then choose the appropriate action based
[25:23] on specific conditions. And at this
[25:25] point, the system starts to behave less
[25:27] like a simple automation and more like
[25:29] an assistant that can actually adjust
[25:31] its responses depending on what is
[25:33] happening. So, create another quick rule
[25:35] here and type this in exactly. If
[25:37] shipping status is delivered, send
[25:39] delivery confirmation. If shipping
[25:40] status is delayed, send apology and
[25:43] updated delivery estimate. And this
[25:45] setup works smoothly because it builds
[25:47] on the same order sheet that was already
[25:49] connected earlier. And the agent already
[25:51] has access to the shipping data, so it
[25:54] can immediately use that information
[25:56] without needing any additional setup. To
[25:58] test it out properly, let's trigger the
[26:00] agent to send updates to our clients.
[26:02] And as you watch the workflow run here
[26:04] on your screen, you'll notice that it no
[26:05] longer follows just one path. It reads
[26:08] the data first, then it branches into
[26:10] different actions depending on what it
[26:11] finds. If the Google sheet shows that
[26:14] the order is still in transit, the agent
[26:15] sends a standard update to the customer
[26:18] with the latest shipping status. The
[26:20] message stays clear and informative
[26:22] since everything is progressing
[26:24] normally.
[26:25] If the sheet shows that the order is
[26:28] delayed, then the agent adjusts its
[26:29] response and sends an apology message
[26:32] along with an updated delivery estimate.
[26:34] The tone and the content change
[26:36] automatically to match the situation,
[26:38] and all of this happens without any
[26:39] manual input. The agent reads the data,
[26:42] applies the condition, and then selects
[26:43] the correct response based on the
[26:45] information available. There's no need
[26:47] to manually check each order or decide
[26:50] which message should be sent. Automation
[26:52] just becomes much more useful when
[26:54] responses are no longer generic. They
[26:57] adapt based on real business data, which
[26:59] makes every interaction more accurate
[27:01] and more relevant for the customer.
[27:03] All right. So, earlier we talked about
[27:05] how much time gets lost in repetitive
[27:08] work. And now you've seen how to turn
[27:10] that into systems that actually run for
[27:12] you. You've built agents that don't just
[27:14] respond, but take action in the
[27:16] background and save you time every day.
[27:18] So, pick one task, automate it, and then
[27:20] build from there. That's it for this
[27:22] tutorial. Thank you for watching and
[27:23] investing your time with me today. I'll
[27:25] see you at the next one.