Welcome in this episode, before we dive into building some amazing automations and agents with N810,
let's start by clarifying a few basics.
So what exactly do we mean by automations?
A Automations and agents.
There is plenty of confusion around these terms.
So today, instead of just giving you the definitions, I will show you the differences using clear
practical examples directly in N810.
By the end of this episode, you will have a crystal clear understanding of each concept, and you will
also get a sneak peek into the workflows we will be building together.
So let's jump right in.
Let's start with automation.
So if we ask ChatGPT, what is automation?
In simple terms, it says that automation means setting things up to happen automatically without needing
a human to do them each time.
So for example, when someone fills out a form online.
The system automatically sends the, uh, the welcome email without you having to do it manually.
Automation means setting up tasks to run automatically without human intervention.
So in other words, you let technology handle repetitive tasks for you to help you understand how a
standard automation works.
I have prepared a simple automation in n810.
So a simple example could be collecting contact information from visitors to your website and automatically
storing their details in a spreadsheet.
Basically, every automation always includes two basic parts.
So the first one is trigger, which means something happens to start the automation.
And in that case we have a form submission.
So we can embed this form on our website.
So when the visitor fills out the form and provides all the details, our automation will be triggered
and all the data will be passed to a second node.
So to the action node and get stored in Google Sheets.
I have created a Google sheet to store our data.
We have two columns a name and email.
So now let's see it in action.
I'm going to test our workflow.
So our automation.
And the form submission pops up.
So let's enter my name.
And my email address.
Hit submit.
And now our automation has been triggered.
And these details have been passed to our action node.
So to Google Sheets.
Now let me check our Google Sheet.
As you can see it has been updated.
In a real world scenario, you would place a form on your website to capture your visitors details.
And when this workflow is active, every time when a new visitor fills out the form, this workflow
will be triggered and all the details will be passed to your Google Sheet automatically, so you can
reach out to your prospects later.
Now let's discuss AI automation and illustrate this with a practical example.
By the way, we create this automation from scratch in a future lesson.
But for now, imagine you are running a business and your inbox is constantly flooded with hundreds
of customer emails every day, like questions about payments, requests for services, consultations,
or general inquiries.
And manually sorting through each message is tedious and time consuming.
That's why we can Use large language models inside our automations, and I could handle this automatically.
And here is exactly how it works.
So you have trigger.
You receive a new email captured by the Gmail trigger node and the large language model of your choice.
In that case, we use GPT four mini reads and understands the email content.
It then automatically categorizes the email into a few groups, such as service requests, consultation
requests, payments, and others.
And based on this classification, the automation then routes each email to the correct department or
sends a suitable automatic replay.
For example, a customer sends an email asking how can I pay my latest invoice, and the AI instantly
recognises this as a payment question.
And it sends a helpful reply automatically, so it saves you a lot of time and ensures prompt responses.
N810 is amazing because it lets you effortlessly integrate powerful large language models like GPT for
all essentially ChatGPT, as well as other popular models like Deep Sea, grok cloud, etc..
And don't worry, we'll go through large language models and how to integrate them in the future lessons.
In this example, images are categorized and routed into relevant inboxes, so I have created corresponding
labels in my Gmail inbox.
And now every time I get a new email, this AI automation will automatically sort it and move it into
the inbox with the correct label.
Now what is an agent?
An AI agent is software that actively makes decisions, performs tasks independently, and interacts
with users or other software.
So you can think of it as your personal digital assistant, but capable of understanding your requests,
performing actions, and even remembering previous conversations.
So let's take a look in this example.
So as a trigger, a user sends a chat message through open chat.
So for example you ask what's the latest news on open AI.
And the agent is always powered by an LLM like ChatGPT for all and instantly understands your query.
Just like with AI automations in AI agents, we also use large language models.
So think of these this LMS as the brain of our AI agent.
They are responsible for understanding your requests, making smart decisions, and using the right
tools to carry out tasks independently.
The agent then uses built in tools, so in that case, we connected SAP API to search the internet for
real time information and memory.
So it's window buffer memory node to keep track of previous conversations.
Alright guys, so let's test our agent to see how it works in action.
And because it uses SAP API to search the internet for real time information, we can ask it, for example,
to retrieve the most recent news about a agent.
And we can test this agent by clicking on open chat button.
And here you can interact with our agent.
So let's ask it for.
The newest.
Articles.
About my agent.
And now as you can see, it's looking for the newest articles about my agent.
And it should get a response in a sec.
Successfully retrieved some information for us.
So here are the newest articles about master agent.
The Chinese agent draws comparison.
Competition.
It also gave us links so we can go directly to the article and read it.
So yes, it provided seven seven articles in total.
On the right hand side, you can see the entire process on how our agent performed this search, but
we will go into detail how it works in the next lessons.
I'm going to dive deep into agents and how they work.
In the next lesson.
This was just a short introduction to help you understand how agents differ from AI automation.
Now let's quickly recap what we covered today.
So automation is about setting up tasks to run automatically without human intervention.
It follows a simple structure.
So in every automation we have a trigger.
An event happens at an action.
So a task is executed.
A automation builds on this by adding artificial intelligence.
So a large language model acts as the brain and enables the automation to make smart decisions, analyze
data and respond intelligently.
And agents, take it a step further.
They not only use an LLM as their brain, but also have tools they can call interact with other systems
and perform complex tasks independently, autonomously, like responding to messages, searching the
web, managing emails, etc..
And in the next lesson, I'm going to dive deep into agents and explain the difference between a agent's
assistance, multi-agent systems and rack.