AI Automation Full Course for Beginners 2026

[00:00] Most people spend hours every week doing

[00:02] repetitive work that could easily be

[00:05] done in minutes. So, checking emails,

[00:08] following up with leads, writing

[00:09] reports, and then copying data from one

[00:12] app to another might seem small on their

[00:15] own, but together they do take up a huge

[00:18] amount of time. And the surprising part

[00:20] is that most people already have access

[00:23] to AI, yet they're still using it like a

[00:26] simple tool instead of something that

[00:29] can actually work for them. So, in this

[00:31] video, you're going to change that.

[00:33] We're going to change that because we're

[00:35] going to build your first AI automation

[00:37] agent step-by-step. Not just something

[00:39] that gives you answers, but a system

[00:43] that can take real action in the

[00:45] background. So, that by the end, you're

[00:46] going to have an agent that can sort

[00:48] through emails, flag urgent messages,

[00:51] follow up with leads, and even generate

[00:54] reports automatically. So, this is the

[00:57] kind of setup that keeps working even

[00:59] when you're not. Everything here is

[01:01] designed for beginners. There's no

[01:02] coding, no complicated setup, and no

[01:05] technical background needed. You'll see

[01:07] exactly what to click, what to type, and

[01:09] how each step connects so you can follow

[01:11] along without getting lost. And once

[01:13] this is set up, it doesn't stop working.

[01:15] It runs in the background, it handles

[01:17] repetitive tasks, and it gives you back

[01:19] time every single day. The best AI

[01:22] automation tool at this moment is Base

[01:23] 44, and I added a special link in the

[01:25] description so you can check it out,

[01:27] too. Now, if you want to master Base 44

[01:29] and learn how to build profitable AI

[01:31] automations agents websites and

[01:33] mobile apps with AI, I've created a

[01:35] complete masterclass that shows you

[01:37] exactly how to do it all step-by-step.

[01:40] And this masterclass normally costs $299

[01:42] to join, but since you are watching this

[01:44] video, thank you very much, you can join

[01:45] completely free. Just check out that

[01:47] link in the description down below to

[01:48] get free access to my Base 44

[01:50] masterclass, and you can start building

[01:52] your own AI-powered business today. So,

[01:54] before we build anything, you do need to

[01:57] understand one key difference because

[01:59] this is what separates basic AI usage

[02:01] from real automation. Most people assume

[02:04] AI automation simply means chatting with

[02:06] a bot, typing a request, and getting a

[02:07] response back, and that's not what it

[02:09] actually is. A chatbot gives you answers

[02:12] while an AI agent takes action. And that

[02:15] difference completely changes how you

[02:17] use AI in your daily work. So, for

[02:19] example, if you ask a chatbot draft an

[02:21] email reply to follow up on my clients

[02:23] booking today, it will generate a

[02:25] well-written for you. And it sounds

[02:27] helpful, and it is, but your work isn't

[02:29] finished. You still have to open Gmail.

[02:31] You still have to go through each

[02:32] message and copy the response and paste

[02:34] it and then send it manually. Now,

[02:36] compare that to an AI agent. You give it

[02:38] one clear instruction, just something

[02:40] like send appointment confirmation to

[02:42] all the clients that we have on our

[02:43] sheet, and that actually completes the

[02:45] task. No copying, no switching between

[02:48] tabs, no repetitive sending. The whole

[02:50] system just handles everything for you.

[02:52] And this is what AI automation really

[02:54] means. It goes beyond generating ideas

[02:57] or tasks, and it moves into actually

[02:58] completing tasks on your behalf. And

[03:01] that shift is usually the moment when

[03:03] beginners start to see the real value. A

[03:05] chatbot helps you think faster, but an

[03:07] AI agent reduces the amount of work you

[03:09] have to do. That's the real advantage

[03:11] here. So, let me show you a quick

[03:13] side-by-side so you can clearly see

[03:15] what's happening here. On the left side,

[03:17] we have a normal chatbot, and let's

[03:19] type, "Can you help me follow up with

[03:21] leads for my salon business?" And it

[03:23] gives me a clean message template. It

[03:24] looks polished, and it's ready to use,

[03:26] but I still need to copy it to paste it

[03:28] into email and then send it one by one.

[03:30] Now, on the right side, I give the AI

[03:32] agent this instruction, "Send a

[03:34] personalized follow-up to all of our

[03:36] clients." And that's it. The agent sends

[03:38] everything automatically. There's no

[03:40] manual work, no waiting around, and no

[03:43] chance of forgetting someone. The task

[03:45] just gets done in the background. And

[03:47] this is why more businesses are moving

[03:49] towards automation. The difference in

[03:51] speed is massive. A person can forget, a

[03:54] chatbot waits for you to act, an AI

[03:56] agent just moves and finishes the job.

[03:59] So, now let's make this real. I'm going

[04:00] to show you an AI agent handling an

[04:02] actual task, so you can see exactly how

[04:04] it works before we build your own.

[04:06] So, now we're going to build your first

[04:08] real AI agent. And by the end of this

[04:10] part, you're going to have something

[04:11] that actually works in the background

[04:14] for you, not just something you test

[04:16] once and forget about. So, this one is

[04:18] going to handle your inbox while you

[04:20] sleep. So, when you wake up, a big chunk

[04:22] of your routine is already done. So,

[04:24] let's start by opening up your browser

[04:26] and going to Base 44. And once you land

[04:28] on the sign-up page here, click get

[04:30] started. You'll see a simple account

[04:32] creation screen, and you can either sign

[04:34] up with Google or use your email. It

[04:36] only takes about a minute or two, so

[04:37] don't overthink it here. And this step

[04:39] matters more than it seems because

[04:41] everything we're about to build will

[04:42] live inside this dashboard. And once you

[04:45] are in, you'll land on the main

[04:46] interface here, and it's pretty

[04:48] straightforward. So, just take a few

[04:50] seconds to, you know, look around and

[04:52] get comfortable. And on the left side

[04:54] here, you'll see your agents panel, and

[04:55] that's where all the agents you create

[04:57] will be listed. In the center here,

[04:59] you'll see the prompt workspace, and

[05:01] this is where everything happens. You

[05:02] don't drag blocks or write code here,

[05:04] you just describe what you want, and the

[05:06] system builds it for you. And that's

[05:08] really the key idea you need to

[05:10] understand before moving forward. You

[05:12] don't need to code anything. You're not

[05:14] setting up complicated logic. You're

[05:17] simply telling the system what job you

[05:18] want handled, and it translates that

[05:20] into a working process behind the

[05:22] scenes. Once it clicks, everything else

[05:24] becomes a lot easier. So, now let's go

[05:26] ahead and build your first one. So,

[05:28] let's start with a problem almost

[05:30] everyone already knows. You wake up, you

[05:33] open up your inbox, and immediately feel

[05:35] behind. Important emails are mixed in

[05:38] with spam, client messages are buried

[05:39] under promotions, and small replies that

[05:42] should take a minute somehow end up

[05:44] eating a a part of your morning. It's

[05:47] repetitive, it's annoying, and it's

[05:49] exactly the kind of work AI is good at

[05:51] handling. So now click create new agent,

[05:53] a prompt box will appear, and this is

[05:55] where we tell the agent what job we want

[05:58] it to handle. And we'll name this one

[06:00] email management agent. So now type in

[06:02] this exact prompt, scan my inbox

[06:04] overnight, flag urgent emails, and send

[06:06] responses to simple ones. So keep your

[06:08] prompt simple like that, you don't need

[06:10] to overexplain or make it sound

[06:11] technical. Just tell it the task and

[06:14] when it should happen and what outcome

[06:15] you want. And that's enough for the

[06:17] system to understand what you're trying

[06:20] to build, and you'll see it start

[06:21] putting the steps together all

[06:23] automatically. And after that, it asks

[06:25] which email provider we're using, and I

[06:27] respond with Gmail. And at this point it

[06:29] will ask for permission to connect to

[06:31] your Gmail. So go ahead and authorize

[06:33] it. And what's nice here is that you're

[06:35] not manually building some complicated

[06:37] workflow with logic blocks and

[06:39] conditions. You're giving one

[06:41] instruction, and then the platform

[06:43] translates that into a whole working

[06:45] process for you.

[06:47] And once that connection is done, the

[06:48] next step is giving your agent the right

[06:50] knowledge so that it can respond

[06:52] properly. So here, let's go to brain,

[06:54] then click upload knowledge, and this

[06:56] part is important because it gives your

[06:58] agent context. You can upload things

[07:00] like facts, SOPs, service menus, pricing

[07:03] sheets, client scripts, or company

[07:04] policies. And let's say this is for a

[07:06] salon booking business. And in that

[07:08] case, you'd upload your service list,

[07:10] your pricing, your opening hours, your

[07:12] cancellation policy, and common customer

[07:14] questions. And that way when the agent

[07:16] replies, it's not just guessing or

[07:18] giving some generic answer, it's using

[07:20] your actual business information to

[07:22] respond. And that leads to fewer

[07:24] mistakes and more accurate replies and a

[07:26] much more consistent tone. It stops

[07:28] sounding like a random AI tool and

[07:30] starts sounding like something that

[07:32] actually understands how your business

[07:34] works.

[07:35] So now let's test it. I'm going to send

[07:37] an email to my own inbox with a meeting

[07:40] confirmation request. And since the

[07:42] knowledge is already uploaded, the agent

[07:44] can use that information right away. So,

[07:46] if someone asks about, say, services,

[07:49] refund rules, availability, or business

[07:51] hours, it already has what it needs to

[07:53] respond properly. And now, watch what

[07:55] happens. The agent scans the unread

[07:58] email and then responds to the inquiry

[08:00] almost instantly. And this is usually

[08:02] the moment when people start to really

[08:04] get it because the value becomes obvious

[08:06] very quickly. A task that normally takes

[08:09] 20 to 30 minutes of sorting and reading

[08:11] and replying is already handled for you.

[08:13] And just like that, you've built your

[08:15] first working AI agent. It can now run

[08:17] automatically every night, which means

[08:19] tomorrow morning your inbox can already

[08:21] be organized before your day even

[08:23] starts. But here's the thing, as you've

[08:25] seen, Base 44 is incredibly powerful,

[08:27] but most people still don't know how to

[08:29] use it properly. They end up building

[08:31] basic apps that don't make money or

[08:33] websites that can't even convert. And

[08:35] that's exactly why I created my own

[08:38] complete Base 44 masterclass. Inside

[08:40] this course, I'm going to show you

[08:41] step-by-step how to build profitable

[08:43] SaaS businesses, high-converting

[08:45] websites, and mobile apps, all using AI

[08:47] with zero coding required, of course.

[08:49] You're going to learn how to build SaaS

[08:51] apps that solve real problems and

[08:52] generate recurring revenue. Also, the

[08:55] exact prompts and strategies that I use

[08:57] to create professional websites in

[08:58] minutes, along with how to clone

[09:00] successful apps and then add your own

[09:02] profitable twist, and my proven system

[09:04] for turning Base 44 projects into actual

[09:06] income streams. This is not just theory.

[09:08] I'm going to walk you through real

[09:10] builds. I'm going to show you my exact

[09:11] process, and of course, give you the

[09:13] templates and frameworks that have

[09:15] helped my students launch successful

[09:17] AI-powered businesses. So, if you're

[09:19] serious, serious about building

[09:20] something profitable with AI in 2026,

[09:23] you got to click the link in the

[09:24] description below to join my Base 44

[09:27] masterclass. Your future self will thank

[09:29] you for taking action today instead of

[09:30] just watching another tutorial. All

[09:32] right, so this is only the first

[09:34] example. Let's go ahead and check out

[09:35] some others. So, we're going to build

[09:37] now an agent that can actually help

[09:40] generate revenue because missed leads

[09:42] are one of the biggest silent killers in

[09:44] any business. A lead comes in, you plan

[09:47] to respond, and something else takes

[09:48] your attention. 10 minutes turns into 2

[09:51] hours, 2 hours turns into the next day,

[09:53] and by then the opportunity is gone. And

[09:55] that's the exact gap that this agent is

[09:57] 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.