Imagine an AI, one that doesn't just chat with
you, but actually acts, updating spreadsheets
automatically, sending those crucial notifications,
maybe managing your digital data without you
lifting a finger. What if you could build that
yourself? Beat. That's really the profound power
of AI agents. Welcome to the deep dive. This
is where we try to cut through the noise, just
killing complex topics down to the core of what
truly matters. Today, we're taking a deep dive
into the fascinating and pretty empowering world
of AI agents. You've almost certainly interacted
with tools like ChatGPT, right? You prompt it,
it gives you something back. But what if you
could move beyond just being a user? to becoming
an architect, actually creating your own bespoke
AI assistant. So today we're going to unpack
the essential components that make an AI agent
tick. We'll walk you through a surprisingly practical
example of building one, kind of from the ground
up. And then we'll explore why this isn't just
a neat trick, but maybe a skill becoming utterly
foundational for the future. The future of how
we work, how we interact with tech. Get ready
to shift your perspective, maybe from just a
consumer of AI to its creator. Okay, so... Let's
begin by really unpacking this idea. We're all
familiar with chatbots. They're designed mostly
to talk, to generate text. But an AI agent, that's
a different beast entirely. It's purpose -built
to perform concrete tasks in the digital world.
What's the fundamental shift in capability we're
talking about here? Yeah, what's truly fascinating
here is precisely that shift. Think of it like
this. ChatGQT can talk to you, answer your questions.
An AI agent, though, can talk to you. Understand
what you mean, then seamlessly turn around and
talk to Google Sheets to actually do something
concrete, update a record maybe. Or it could
talk to Slack to send a message. It really moves
beyond just conversation to actual execution.
Right, execution. And to get that level of execution,
it's not just one big black box, is it? It's
composed of three essential elements, almost
like a digital nervous system guiding its actions.
What are these core components? Exactly. So first
we have the brain. This is the large language
model itself in LLM, maybe something like OpenAI's
GPT -4. It's doing the heavy lifting, you know,
understanding language, reasoning, generating
responses. But crucially, for an agent to be
truly useful, this brain needs memory. Without
it, every single interaction is a completely
fresh start. Imagine trying to have a coherent
conversation with someone who instantly forgets
everything you just said. Yeah, that sounds incredibly
frustrating, like having amnesia between sentences,
basically. Exactly. It makes the agent pretty
useless for any complex multi -step task. Okay,
so memory is key. A truly forgetful assistant
would be, well, not very helpful. So with that
memory layer in place, what then allows this
brain to actually do things out there in the
digital world? Ah, that's where the tools come
in. These are effectively the hands of the agent.
They let it interact with the outside world through
what we call APIs. An API application programming
interface. It's basically a standardized way
for different software apps to talk to each other.
So we're talking about integrating with services
like Google Sheets, Slack, Notion, various databases,
email platforms, really any digital service that
offers an API. This is how your agent can read
data, write new information, or modify existing
records. Got it. The brain thinks and remembers,
the tools act. What's the secret ingredient then,
the thing that truly brings this agent to life
and dictates how it uses those tools? Is there
a kind of command center orchestrating it all?
There absolutely is, and this is arguably the
most critical part. We can call it the brain
stem, the system prompt. Now, this isn't just
a simple instruction like be helpful. No, it's
a meticulously crafted set of detailed, clear
guidelines you give to the brain. It defines
the agent's entire role, its precise objectives,
how it should behave in different situations,
and most importantly, how and when it must use
those tools. A really well -written system prompt.
That's where your human intent literally breeds
life into an otherwise generic language model.
It transforms it into a truly intelligent, autonomous
agent designed for a specific purpose. That's
where the real leverage is in shaping its intelligence.
OK, so if we boil that down, what's the core
defining difference between a conventional chatbot
you might use every day and a fully fledged AI
agent? Well, simply put, an agent doesn't just
converse. It takes concrete, economist actions
in the digital realm based on that conversation.
And that ability to take concrete action. That's
what truly unlocks their power. OK, now let's
move from the abstract to the actionable, because
here's where it gets really interesting. The
source material we're diving into today walks
us through a fantastic, practical example, building
a subscription tracker AI agent. It sounds simple,
but it's incredibly useful, and it really kind
of demystifies the whole process. How does this
agent actually function from a user's perspective?
It really is a perfect starter project, yeah.
You interact with it using natural language,
just like you would with any chatbot. For instance,
you might just say, hey, I just subscribed to
Spotify Premium for 120 ,000 VND per month. The
agent then quietly goes to work and parses that
message, intelligently pulling out the specific
service name that cost the frequency. OK, but
what if you forget a detail? Or maybe you're
a bit vague. Does it just guess? or does it actually
engage with you? That's a great question. If
something's missing or if it's unclear, it's
actually designed to ask for clarification. Hey,
you mentioned Spotify. What was the cost again?
Something like that. And then crucially, before
it does anything irreversible, like writing to
your spreadsheet, it asks for your explicit confirmation.
Okay, add Spotify. 120k VND monthly, is that
correct? That confirmation step is key for keeping
you in control, maintaining transparency. Once
you confirm, then it automatically adds a new
row to a designated Google Sheet with all the
correct structured information. And this basic
project, it isn't just a novelty. It's a perfect
foundational blueprint for way more complex real
-world agents. Think automated expense management
or streamlined customer support. That is fascinating.
Simple but powerful. Yeah. So to build something
like this, what are the absolute basics? The
accounts or platforms you need to get your hands
on? Right. The essentials. You'll need access
to N8AN. That's the workflow automation tool
used in the example. You'll need an OpenAI API
key for the brain. and of course a Google account
to connect your Google Sheets. Gotcha. NEN, OpenAI,
API, Google account. Now let's talk setup configuring
that brain and equipping those hands within NEN.
Where do you even begin in the workflow? Okay,
so you start by setting up what's called a chat
trigger in NEN. Think of this as your main communication
gateway, right? The entry point for your conversations
with the agent. From there, you add the central
AI agent node itself. This is where all the intelligence
will live. Then you connect the language model.
The source material suggests a lightweight GPT
model, maybe something like GPT 4 .1 mini. Sorry.
The source actually recommended GPT 3 .5 Turbo,
or a specialized mini model for beginners. It
gives a good balance of performance, speed, and
cost. And you connect your OpenAI API key here.
It's worth pointing out that's a separate access
key. It's different from just having a regular
chat GPT plus subscription. OK. And as we touched
on earlier, a raw LLM is essentially stateless
by default. It forgets everything. It seems like
a huge problem. So what's a critical step to
give it a working memory to allow for fluid ongoing
conversations? That's right. By default, they
have that immediate amnesia. So to overcome this,
we add a simple memory component. By setting
a context window length, say, maybe around 14
interactions back, it lets the agent remember
the previous parts of your conversation. This
is absolutely vital for a useful assistant. It
stops it from asking the same things over and
over or just losing track of what you're trying
to do. It allows for that natural flowing dialogue
without racking up huge processing costs for
you either. Makes sense. So once the brain can
think and remember, you then give it its hands,
specifically a Google sheet in this case, how
do you set that part up and connect it? Right,
the hands. First, you just set up a simple subscription
tracker sheet in Google Docs. Give it clear columns
like service name, cost, status, whatever you
need. Then, back in AGN, you link the sheet using
the Google Sheets tool. The key here is configuring
the tool. You tell it to append row for new entries.
Then you map the columns in your sheet to the
data the AI needs to fill in. You'll notice a
little sparkle icon in AAN next to the fields.
That sparkle icon tells the AI to infer the data
for those columns from what you, the user, typed
in. It's pretty smart. Oh, and it's also a good
idea to rename the tool to something clear, like
add new subscription. That helps the AI understand
exactly what that tool is for. OK, mapping the
columns, using the sparkle icon. And then comes
what you call the true magic, right? The brain
stem, the system prompt. This is basically the
detailed job description for your agent. How
do you even go about crafting something so critical?
It sounds kind of intimidating. It is critical,
but it doesn't have to be intimidating. It really
is an art form, though. The source material suggests
a brilliant approach, actually. Use ChatGPT itself
to help you write the prompt. You feed ChatGPT
the details of your workflow, what you want the
agent to do, and you give it a strict structure
for the prompt you need back. You tell it. Define
the agent's precise role, its core objective,
the entire interaction flow definitely, including
that crucial confirmation step we talked about,
specific tool usage rules, and even detailed
data formatting instructions. The amazing thing
is, even a tiny change in wording in this prompt
can dramatically alter how the agent behaves.
So once ChatGPT helps generate that powerful
prompt, you just copy it into the AI agent node's
system message option in NAN. Simple as that.
Wow. Using AI to bootstrap the AI's instructions
as ever. And then the moment of truth. You test
it. How does that usually play out? Testing is
absolutely essential, yeah. You type something
like, add my new Netflix subscription. It costs
260 ,000 VND a month. The agent analyzes your
input, summarizes it back to you, asks for confirmation.
OK, add Netflix, 260k VND monthly. Sound right.
And only after you explicitly say yes does it
activate that Google Sheets tool. And boom, a
new row appears in your spreadsheet. It's incredibly
satisfying when it works. And the power of a
well -crafted prompt really shines through when
it handles ambiguity, too. If you leave something
out, it should ask, what's the cost? That shows
the prompt is working intelligently. OK, so if
I had to distill everything we've discussed about
building this agent, what's the single most critical
part for really making it intelligent? The system
prompt. It's the operating brain. Its clarity
fundamentally determines the agent's intelligence
and effectiveness. Simple as that. Mid -roll
sponsor read. All right. Once you have those
basics down, the real fun begins, I imagine.
Leveling up your agent. What are some of the
first ways you can make it even smarter? more
robust? Yeah, good question. First, a really
common enhancement is duplicate detection. You
wouldn't want an intelligent assistant adding
the same Netflix subscription twice, right? So
to implement this, you'd add a second Google
Sheets tool. This one you configure to get rows,
maybe rename it check existing subscriptions.
Then you update your system prompt again. The
prompt is key. to always check for duplicates
before adding anything new. If it finds one,
the prompt tells it to ask you, the user, what
to do. Hey, I see Netflix already. Do you want
to update it or add this one anyway? Ah, OK.
Checking first. And building on that, what about
making it capable of modifying existing entries,
not just adding new ones? That seems like a natural
next step. Exactly. That's update functionality.
So you add a third Google Sheets tool. This one's
set to update row, maybe named update subscription.
And this is where your system prompt gets even
more sophisticated. You have to instruct the
agent on the precise logic. When should it use
the add tool versus when should it use the update
tool? It's usually based on your response after
that duplicate check. layer of usefulness for
sure. Whoa. Hang on. Imagine scaling this further.
You could easily integrate, say, a FLAC tool
to notify your finance channel, whatever new
subscription is added, or maybe a Notion tool
to create detailed notes linked to each entry
automatically. It's really like stacking Lego
blocks of data and actions, all controlled by
just talking to it. Imagine orchestrating multiple
applications seamlessly, having them talk to
each other to automate entire workflows. The
possibilities really open up there. They absolutely
do. And as you venture deeper into building these
agents, there are some essential golden roles
for success, let's say. First, and this might
sound obvious, but trust me, it's crucial. Say
frequently, like CMDC Trial plus S should become
pure muscle memory. Losing work. Especially when
you're deep and tweaking a prompt is just incredibly
frustrating. I still wrestle with pumped drift
myself, you know, where you think you've nailed
the perfect pumped and then suddenly the agent
starts doing something completely unexpected,
sending you down a debugging rabbit hole for
hours. Saving often lets me backtrack easily
to that last known good configuration before
things went sideways. It's like a digital undo
button for your sanity. Seriously. Oh, yeah,
that's a classic. Definitely been there in other
contexts. And then the wisdom of starts Simple.
Expand incrementally. Precisely. Don't try to
build Roman a day, right? Don't start with an
agent juggling 10 tools and super complex logic.
Get a single core function working perfectly
first. Then slowly, methodically, add layers
of complexity. Test each layer. Also, and we
really can't stress this enough, the system prompt
is key. Spend the most time refining it. Even
a tiny change in wording, a misplaced comma,
a subtle tweak in instruction can dramatically
alter the agent's behavior and performance. It's
that sensitive. And finally, test thoroughly.
Don't just test the happy path where everything
works perfectly. Try ambiguous inputs. Give it
incorrect data. Even throw edge cases at it that
it isn't explicitly designed for just to see
how it reacts. That's how you uncover vulnerabilities.
Right. Pushes boundaries. And a very practical
tip for anyone when diving into this, monitor
API costs. Yes, absolutely. Every time your agent
thinks, meaning it makes a call to the OpenAI
API, you incur a small fee. Those tiny fees can
add up really quickly, especially with frequent
testing or if the agent becomes heavily used.
So always start with those lightweight or mini
models we mentioned. And keep a close eye on
your usage dashboard provided by OpenAI or whichever
provider you use. It just saves you from any
nasty, unexpected surprises on your build demo
line. Good call. So for someone who's just starting
dipping their toes into building AI agents, what's
the single most important guiding rule to remember
throughout their journey? Start simple, make
sure your system prompt is crystal clear, and
test rigorously. That's the foundation. You know,
if we connect this to the bigger picture. learning
this skill, building these agents. It isn't just
about connecting apps or automating a few tedious
tasks. It's profoundly akin to, say, learning
programming back in the 80s or 90s or maybe database
management in the 2000s. This ability to converse
with, instruct, and orchestrate intelligent agents,
it really feels like a foundational skill for
the AI -driven world we are rapidly entering.
You're essentially moving from being just a passive
AI consumer to an active AI creator. And with
that comes complete data control. which is huge,
incredible process transparency, near infinite
customization possibilities, and perhaps most
importantly, you gain a deep first -hand understanding
of both AI's immense capabilities and its current
limitations. Ultimately, it's really about empowering
you in this new technological landscape. Yeah,
this deep dive really highlights how AI technology,
when understood and wielded by individuals, truly
empowers us. It's not necessarily about replacement,
but enhancement. Your journey, should you choose
to embark on it, is only just beginning. Which
really raises an important question for you,
the listener. What task, what tedious digital
chore in your own life or work could be fundamentally
transformed by your very own custom AI agent?
Yeah, think about it. Experiment. Break things.
Fix them. Don't be afraid to dive in and get
your hands dirty. Have fun with that creative
process. Every complex world -changing AI system
started with just a simple idea and a simple
first step. Go out there and build something
amazing. We hope this deep dive has given you
a useful shortcut to being well -informed and,
maybe more importantly, inspired you to explore
what's truly possible when you shift from just
consuming AI to actually creating with it. Ochiiro
music.