Now is the time to create an AI agent that can manage your Google Calendar.
Send the emails via Gmail and in general, just by the way, plan on all of your meetings with clients
or other people.
Nevertheless, what's very special in this material is that we'll be using deep C, R1 model and deep
chat model.
So we'll be basing on these models.
We'll train how to use them.
And also I will show you how to use deep R1 with the HTTP request.
So we'll be setting entire HTTP request.
And there I will show you how you can do this stuff.
What are the differences between just setting up deep Sea here as a note and as a HTTP request?
Even before we get started, let me show you how it works so I can open the chat, which is the trigger.
And there I can type.
For an example I would like to schedule a meeting with let's say Christian and his email is Christian.
Udemy gmail.com.
Let's say at 5 p.m. tomorrow and the meeting should last.
Let's say two hours.
Yeah.
So I can just type it.
Also I could for an example just just type like I would like to schedule the meeting, send the email
to the Christian asking if he if he wants to get this meeting together with me.
Nevertheless, right now I'm just saying.
All right, schedule a meeting with Christian at this specific hour.
This is his email.
So we know.
Yeah.
And we can send this message.
And what happens?
It goes through our planner, a agent that creates the prompt actually with deep sea air one.
And for this agent that actually, you know, does all of the things.
So it uses deep chat model and then It's, um, it actually schedules for me the meeting inside the
Google calendar and sends an email to the client person.
Actually, we've got the meeting.
Um, but hey.
Alright, the meeting is at this specific hour.
At this specific day.
We are waiting some time because actually we are using deep reasoner model.
So that means it thinks actually it takes more time to process.
And also we've got the prompt we'll cover everything in this video.
So we've got the mission operating scope workflow.
So what this agent does it creates the prompt to our second agent.
And then you know everything works by this way.
Um so let's wait a while.
So the entire workflow works.
And now let's take a look of the prompt our planner agent gave.
So here after we do this, after we run this agent, it gives the prompt for the assistant.
So for example we've got schedule a two hour meeting with Christian there.
The email.
So what it does, it gives the entire prompt.
And then we are using this agent that does everything for us.
So it should schedule a Google calendar, a meeting and then send the email on Gmail.
Um, for this case, let's see.
We've got the meeting right here at the specific hour as I wanted.
And also we've got this entire email to our person that we scheduled the meeting with.
Um, so for sure it works.
We'll build it step by step.
Um, and this video will be a great practice for just connecting your agents with Deepshikha model.
And also I will show you how to set up HTTP request, which is really useful.
And in general, let's dive in.
So when we are inside and then let's click in the right top corner on Create Workflow.
And let's start with creating our entire agent.
And the first step will be our trigger.
So we indicate that we would like to trigger this entire automation whenever we've got chat message.
You could, for example, choose also the form.
However, for that circumstance I would like to process with the chat message.
And then what we can do we can go ahead to our first agent.
So we just search for this agent and then um we just click on define below and we provide the prompt.
And there we click on add option.
And we provide the system message.
So we'll need these two fields and fulfilled.
When you go ahead to our file that will be in the resources of this material.
You will find everything every information inside.
So we've got the prompt we'll be using and the system message that is pretty white.
Also other things.
Um, yeah.
For this entire automation.
Additionally, I will provide for you the JSON file so we can just download it and import inside.
Yeah.
Inside this workflow.
So after you downloaded your file, you go ahead to the right top corner.
You click on the three dots import from file.
And after you click on the deep sea Corona agent, the entire workflow is provided inside Nadine.
But now let's continue with creating that from start to finish.
So we have again our agent and inside we provide a prompt and let's execute previous nodes.
So for example I can type the following sentence schedule for me a meeting with Christian and something
very similar to kind of, you know, um, yeah, train this entire automation and use the data inside
this workflow.
So schedule for me a meeting with Christian.
Um, his email is let's provide some basic information, um, at, let's say 5 p.m., um, on let's
say, I don't know, maybe June 16 or 14, 2025.
All right.
And let's say for one hour now, we can just simply run it, and then we'll be able to use this data
inside our agent.
So let's go over here.
And as you can see our prompt is properly even fulfilled.
So we just drag and drop this one variable.
So chat input.
And by this way we give the information to the model.
Um for the system message um kind of here we'll be using this entire system message.
You can go ahead to the file and just copy it.
So let me show you how it looks like.
Let's provide expression.
And now inside we've got the mission.
So we are an AI tasked with drafting clear practical prompts that help a personal assistant complete
any job at hand.
And we've got the operation scope, so no external tools or references.
Keep every instruction brief.
ordered an unmistakable then jobs can be simple or complex, but your wording must remain straightforward.
So we have the workflow and illustrative scenarios.
Even I provided there some examples.
So for an example user requests book a doctor's appointment for next week and prompt to produce.
Find open slots with the doctor next week.
Pick a time that doesn't clash with existing events.
Book the slot for phone or online.
Confirm and add the appointment to the calendar.
So what do we do with this agent?
We actually create a prompt for the next agent.
Obviously, if you want to read this entire description system message and you can stop the video,
read it, it's your choice.
However, you know, I don't want to waste too much, too much time on explaining all of the things
we have inside this message because again, it's pretty white.
And also we've got something like the Operation Operating checklist and we've got closing reminders
and also what's very important, we provide a variable for the date.
So current date and time.
Nevertheless what we can do for now we need to add the chat model, chat model and their um memory.
For the chat model we'll be using Deepsea.
Deepsea chat model.
And there in general we need to create our new credential.
So for this case let me show you how to do this.
Just simply type Deepsea API.
Click on Deepsea API docs.
And there in the right top corner click on Deepsea platform.
When you are here uh you should actually provide some balance.
So there I've got for an example $2 and Deepsea.
Why we are even covering Deepsea instead for an example of um, let's say ChatGPT and models.
Deepsea is one of the cheapest models available right now on the internet.
So if you actually consider using some pretty nice models, you can use Deepsee because again, it's
very efficient and very affordable.
However, for now we can click on API keys, create new API key, and for an example type test.
And then and then just copy this API key.
Go ahead to your workflow and just paste it right there and click on save.
I've got my API key so I can continue.
I can close it.
And for this case I will be using Deepsee Reasoner.
So the model that actually thinks that, you know um, yeah, uses our thinking and reasoning and capabilities.
Um, so I've got my Deepsee care1.
And additionally what I can provide is the simple memory.
This extension allows our agent to remember actually past interactions.
Um, we've got inside our automation, inside our chart.
So how many past interactions the model receives as context?
It's cool because, uh, yeah, I like the model.
The model to know what I'm typing.
Um, and just it's very useful.
Note for now, I can what I can do, I can click on test the step.
So just use our model to create a prompt.
Bear in mind that it takes some time because we are using Deepsee R1 instead of deepsee chat.
So this reasoning model needs to have some space to work.
Um, so it can take up, let's say up to three minutes or so.
However, we've got our response from our model.
So entire prompt we can use for now.
Um it's perfect.
Let's process with the next agent.
And now what we can do.
We can click on this agent and just hold Ctrl button.
And then just click on other nodes and click on Ctrl D.
So we just copy this a agent?
For now, what we can do and we can connect them.
And in general let's rename them.
So let's go over here and click on rename.
So we've got our um planner agent.
And this will be um executive agent.
So we've got executive um, so simply now we can see that this agent is for planning and this for just,
just making and executing tasks.
Uh, for this case, we can use the Leipzig chart.
And then what we can do, we can just delete this memory option.
It's not necessary for now.
And we can add our tools.
The first tool will be our Gmail node.
All right.
We would like to use the Gmail to send the message.
And for this case um we've got the resource which is message operation sent.
We've got a tool so we can just simply open, and here we can just simply open two curly brackets.
And now we'll be providing the specific variable which is dollar sign.
And we will be using from I.
So this entire code allows us to ask I for getting for us the email of the person from our chat.
Because you need to remember actually here we don't have any variable for the email.
The email is somewhere there.
So we've got the email.
However, still we can just provide this entire output because we need to have clear value right there.
So we can just type from I and there inside type something like this.
So let's say we've got email address okay.
And I should guess what we have inside.
Like what should be inside okay.
And we can just copy it again.
However now inside this the the, um, yeah.
Inside the brackets, we can type the subject and they're inside the message.
We can go ahead and type actually our message.
So it's very cool.
It's not that easy to set up because if you have never used like, uh, yeah.
Um, this, this entire comment, um, you may have some problems.
However, you will see, it's not that complicated.
Um, let's go back to canvas and the similar thing we'll be doing with Google Calendar so we can pick
Google Calendar.
Where do we have the Google Calendar right here and now.
What we can do.
We need to do the same stuff with start and end date.
Additionally, we can firstly pick the calendar.
So this is my email hover for now.
Again what we do we provide two curly brackets.
Let's delete it.
Firstly two curly brackets.
We provide our dollar sign and we type from I.
We can just simply choose the method and then just go over with Start date and just paste it.
Just copy it paste it here and just type end date.
So we'll see if that works.
But for now the last step is to customize our agent because we need to remember we duplicated like this
agent.
And we've got the instructions from the previous one.
So let's delete it.
We don't need to have like system message.
Um so in general we can also type connected chart trigger note without um the system message.
Um and for this case like let's delete it.
For this case we can test this step.
So let's test the step.
Obviously we need to provide the variable.
So instead of connected chart trigger note will have defined below.
And we need to take our um.
Here.
We've got the output from planner.
We don't want to have an output that we had with the connected chart trigger node, because we are taking
just chart input.
Nevertheless, for here we want to have the output from the previous agent.
So again let's click on the step.
And now let's wait until it finishes its job.
So it's done.
And now let's see how it performed.
Let's go ahead to the Google Calendar.
And we've got there our meeting from 5:00 PM to 6:00 pm.
Uh, when we go ahead to our automation, you can see at 5:00 pm on June 14th, 2025, it's nice.
However, the point is, let's see what happens right there.
So it didn't actually, uh, yeah.
It didn't, um, provided the message inside our Gmail.
However, it scheduled the meeting inside the Google Calendar.
So now let's see what's wrong.
So actually I forgot to change the email type to the text.
So everything else is set up correctly.
So let's again what we can do.
Again, we can run the executive agent and see now what happens.
So it should work without a doubt.
However, let's wait again.
It should actually send a message via our Gmail.
So for now actually see in the message I don't have the actually text for sending the email to the person
we are scheduling the meeting with.
So in general to test out if that works, if we don't have any problems in the Gmail node, let's type
the same prompt.
However, let's change for an example the time.
So 7:00 pm and there send the email confirmation to that person.
So we simply test if the entire workflow works.
So after testing our workflow once again we can see it works.
So it sent our email to the customer.
So you need to remember also to be specific when you're typing the message.
You know, to your entire agent.
However, for now it's cool.
And now we can process with the HTTP request because I will be showing you how to exactly how to connect
Dpsk with HTTP request.
And there we've got some differences between connecting IPsec with our agent and HTTP request.
So for now what we can do, we can click right click on the workflow and add the node.
We'll be using HTTP request.
So let's go ahead here and provide the method which is post.
And inside our document I provided for you all of the important information also for them.
For an example, for the Gmail note and calendar note, you've got like these code snippets.
However, for Deep Sea Urban Planner HTTP request, I provided for you the data because we'll be using
URL, which is this one.
We can just copy it.
Let's go over here.
Provide it there.
And now let's see what we need to do.
We need to enable the headers provide the authorization.
So our API key.
Send the body and specify the JSON body to connect our HTTP request.
And then you can find this information inside the Deepsee documentation.
So you've got everything here.
The exact URL you need to use um model you can use actually you know, all of the variables.
Um, so, you know, you can go over here and just look.
Also we'll be using this JSON code.
So this is just a disclaimer that remember actually to every time, whenever you've got some problems
to go to the documentation, documentation because you've got all of the data inside there.
For the simplicity, I provided, um, information inside the file.
So we don't need to search it, search for it manually, or just rewrite from the video.
You can just copy and paste it.
So let's for now um, yeah.
Send the headers There in the name type, authorization and authorization, authorization and in the
value.
Let's type the bearer.
And here we need to provide our API key that we can copy from our Deepsee platform.
So remember from here um, let me for an example copy this API key.
All right.
We need to set the body using JSON.
And in general let's provide uh where do we have that okay.
Here.
Let's provide our JSON code.
I won't go more in depth when it comes to explaining what happens here.
However, what we've got, um, inside, um, we've got our message.
So here we have the content.
Um, you're a helpful assistant who is tasked with creating a step by step plan of action for a personal
assistant.
So in general, what we have inside, we've got a content for the system.
So this is the system message there the content for the user.
So this is the prompt.
Okay.
And there we've got some specific fields and variables.
And what are the differences between connecting deep skiff node as we as we've done here and HTTP request
with HTTP request.
You have more control over your model.
So inside for example you can see you've got frequency penalty.
You've got max tokens you want to use.
You can enable actually and change the temperature.
So it allows your model to be more predictable or less predictable.
Um you've got top P you know.
Um yeah.
Metric.
You've got the tools, you've got the tool choice.
So everything you have explained inside the documentation and the and the HTTP request is more for developers,
the people that are that want to have, you know, like let's say temperature at, I don't know, at
higher value and just just experiment with different fields for 95% of cases, you want to use this
node.
So yeah, with our agent it's easy to set up.
It's really effective.
Nevertheless, in this video I wanted to show you how you can exactly set up the HTTP request.
So for now, what we can do, um, in general we can go ahead and we can, um, delete this node.
Let's connect it right here, open the chart and now provide some message.
So for example this one very simply.
So we just want to make sure that our HTTP request works.
Um we are waiting for now.
So yeah let's wait a while.
And after some time we can see uh yeah it works.
We've got our content and we've got all of the information.
So we successfully connected HTTP request.
And for this video I can say this is pretty everything.
Thank you for watching this material.
I hope you enjoyed it and I will see you in the next lesson.