In this video, we're going to build a really cool AI agent that will have the ability to pull data from the internet or call in a separate workflow that will have an API attached to it. So we're going to build all of this step by step.

And I'm going to show you how powerful Nandan is when it comes to building customized AI agents and how you can connect several different tools. And obviously, you can attach a large language model like OpenAI in this case, or Anthropic or others as well. I'm

going to explain what Nandan is and why this is such a cool tool to use when it comes to customizing workflows and specifically building customized AI agents because it gives you the ability to connect different tools and therefore make the AI agent extremely powerful. In our scenario in this example that I'm

going to walk through, this AI agent will independently make a decision based on the question that you're asking it, either to call the Wikipedia tool to grab data from the internet or call this separate Nandan workflow that we're going to build that will be connected to a

third-party API app. So this will be a really comprehensive tutorial on how to utilize Nandan's different tools and different customizability options to be able to build a really powerful AI agent. All right, let's jump right in. All right, so a quick introduction for Nandan, um, if

you're not familiar with Nandan, Nandan is a workflow automation tool, but it's extremely powerful in my opinion. It's way better than Make.com, Zapier, or all these other automation tools that are out there in the market, and specifically when it comes to building AI agents, so

the reason why I really like n8n, and again I've been building these automation workflows and agents for a while now, and the reason why I landed on n8n and why I think that this is such a good tool is because it gives you really a lot of options when it comes to

customizing and being able to provide different tools and attach different apps to be able to really customize your AI agent. Another great advantage for n8n is that you can actually self-host this on your local computer. Again, this is going to be more of an introductory

tool. I don't want to go too much deep into that, but on the next videos I'll be able to build a more complex automation tool or more complex AI agents where then I'll be able to host or self-host n8n on my own local computer where then

you basically will have the ability to own all of your data, um, and therefore be at ease when it comes to customizing and uploading your own data. All right, so with that being said, um, so you can create an um free n8n account um, and

again, you can do a cloud account. So that's going to be the free option. You don't have to worry about hosting, self-hosting it for now. We're just going to go ahead and use their cloud account, but you can as soon as you log into your

account, you'll come into um your workflow, and this is basically the starting point. So I'm just going to get rid of this um, so if you're familiar with a tool like make.com, it's kind of similar to that, but it's again like I

said there's more customizability options here all right so first the first node we're going to add is uh a trigger and again a trigger could be something whether uh it's a manual trigger in this case obviously I added a manual trigger but it could be a chat it could be an email so you can have different uh trigger options available and again in the future videos I'll create other AI agents that will have a trigger of an email or a chat or a slack message uh but for this one we're just going to do

a manual trigger when clicking test the workflow so this basically as manual as it gets all right so let's go ahead and click on this add node button right here um so what we're going to do is is actually go to Advanced AI again we'll

come back in other videos and take a look at these other options that you can add obviously there's a lot of um different options when it comes to adding nodes but for this one we're going to click on Advance AI and when you click on Advance AI this gives you

all of the different options you have when it comes to the AI tools that you can attach but we're going to click on AI agent and as soon as you click on AI agent it's going to pull up um the settings and the parameters we don't have

to worry about the settings for now but parameters this is where will be able to um attach actually different tools to this AI agent so for now let's just leave this as it is we need to attach a uh language model to this so I'm going

to attach a uh language model to this so I'm going

to get rid of this node because it automatically pulls that up so the first thing we need to do is add a chat model so if you just click on this plus button or you just drag it so this is all of the language models that are available

for NN and they have this integration so they have obviously the most popular one they have the entropic you know your grock and um Llama or Lama the OpenAI chat model obviously um I I have an account with OpenAI so I'm going to

choose this but it depends on your choice you can definitely um uh choose whatever you want all right so once you do that then it's going to ask you for your credentials so you can connect to your API account again you have to have an OpenAI API account your keys that

you need to grab from there so that way you can add to your credentials so I've already have my account here but if you don't you just click on create new credentials and again you just have to go to your API account and be able to

grab your API keys or you can create a new API key the organization ID is optional but you have to have your API key all right so once you do that you're going to come back and this is going to get um your account is going to get

connected here so the next obviously you got to choose your model again I'm going to use GPT4 and that's pretty much it so now you have connected your uh OpenAI chat model to this and as you can see this this attach is complete so another

thing I'm going to add here is a memory. If you just click on this, so you know not to get into too much detail, but this just basically gives NAD the ability to store your um data into your Windows memory buffer so that

way it can refer to your previous chats when you build this AI agent. So we're just going to click on the Windows buffer again. You don't have to do much, you don't have to add any credentials or anything like that. You just got to attach it. All right, so the memory is

done. So this is what makes NADN really powerful, the ability to add these different tools. So they have a lot of tools available here. Uh, so the first thing we're going to add is a calculator. And again, a calculator just gives this AI chat model the ability to uh run

numbers. So if you're asking it a a question um that requires mathematical operations, then it will have uh the ability to do that with this calculator. I mean, again, we don't have to worry about it too much. All right, so for the tools, I'm just going to keep it separate

and you can attach multiple tools here. All right. So another tool we're going to add is the Wikipedia tool. Again, just if you just click on this PL plus button. The Tool uh will open up again, uh and as you can see at the bottom right here, it

says Wikipedia. So what this tool does, it gives your agent the option uh or the ability to to have a conversation with you and have access to the knowledge resource of Wikipedia. Okay. So now that our AI agent has the um knowledge base from Wikipedia, it means that we can cat

with it right so right now as you can see in the bottom here it just says test flow um because right now this is a manual trigger but let's go ahead and get rid of this and we want to be able to add that chat trigger here so I'm

going to go ahead and click on this um add first step and again you can click on that top right corner there as well and as you can see on the uh on the list here you have that trigger manually option on an app event on schedule on a

web hook call uh but as in the bottom you can see here it says on chat message so we're going to click on on that all right so oops so let's grab this over here and bring it here all right and then let's go ahead and connect this there you go

so you just got to do is you just got to drag that and connect it to and you can always delete it from here this connection um all right so now that that's connected um so now again you don't have to do anything here this is

just a trigger and as you can see at the bottom here a little chat box appears which means now you have the ability to chat with this AI tool um so let's go ahead and actually do that first of all another thing is I always have the habit

of saving so I'm just going to click on Save there that way you don't lose your progress all right so now let's go ahead and test this out so I'm going to come here and chat with this so I'm going to say what is the capital of Spain for

example right so it should be able to have access to that data via that Wikipedia tool that we that we that we gave it and there you go the capital of Spain is Madrid and on the bottom you can see the log for this message it means that it uh

the AI agent used the Windows buffer memory again this is just to save so that way you can refer to it um um later on uh and it used the OpenAI chat model and it used the Wikipedia tool as you can see right here to search for that

and if we get out of this you can see now that the AI agent used the following tools by looking at this um with the little green markings here all right all right cool now we're going to add another workflow to this and what you're going to do is click on this plus button

again right here it says called NN workflow tools what this means is that this gives you the ability to call other workflows that you can create in NN and attach it to that AI agent and the way you do that is right here on the workflow ID you can attach the ID of a

separate workflow that you've created so therefore um this AI agent will have access to that so let's go ahead and actually do that so let's go ahead and create a new workflow that grabs weather data using a third-party API okay so let's back out of this Fel and as you

can see right now there's this little error message just because there is no workflow connected to this so in order to create a new workflow again you can just go back to your homepage and just add another uh workflow here so I'm just going to go ahead and do that all right

so now I'm here with my new workflow so what we're going to do is leave this um the test workflow as manual trigger and click on the plus button here and here we're going to grab this data transformation so when you click this gives you the option to manipulate uh

filter or convert different data that's coming in so we're going to click on this and we're going to select this um edit field again this is kind of the popular ones but you can see at the bottom they have all these different uh data transformation tools that you can

attach and again the following videos I'll kind of go through some of these because they're very powerful but for now we just click on this edit field all right so we're going to go ahead and change the name here so I'm going to change this to query if I can type

oops all right so click on rename we're going to leave the mode as manual mapping um so JSON is if you're you know familiar with code you can actually add the mode via JSON as well but for now we're just going to leave this as manual

mapping so for the fields to set so this is where you can um grab uh a field from a previous input and in this case I obviously ours is a manual trigger so if you just execute the previous node it just says when clicking test workflow because we don't have anything added to

this but obviously if you have a previous node that's attached to it and is doing something you'll be able to have access to that as well all right so now what we need to do is just manually add a field okay so the name you can

name this again same thing I'm going to name it Cory you can leave it as string because this is just going to be um a value that we're going to provide to our API endpoint in the next node that we're going to add all right so for the value

now we're going to add um a name of the city again this is just so we can test our workflow so for now I'm just going to say San Francisco that's my city so I'm just going to put that here all right and

that's pretty much it so now let's go ahead and test this step and by testing this step you you want to make sure that uh the output is what you're expecting and in this case we want the output to be just be San Francisco and as you can

see right here the key it's the name of the field and it's outputting San Francisco based on what we put in the bottom here all right so let's back out of that and as you can see it's successful uh if there was any kind of

error this green box won't appear it it's just going to show the error there okay so now we need to add a node that will have access to weather data and we're going to do this by grabbing the weather API from a third party so the

way we do that is just again same thing add a node I'm going to search weather here and I'm going to use this open weather map uh tool that they have and again this is uh from a third party so it just you gives you the option as far

as actions you can return current data or return weather data for the next 5 days so let's just click on the return current weather data because we want to be able to have you know the current data from our Cy which is San Francisco all right so once you click on that this

will open up your credentials again to connect with so it's the same kind of concept you have to go to your open weather map um account on their website and if you click on docs here this also opens up um kind of a guide on how to

grab your credentials so go ahead and do that once you do that again same thing you're just going to come here and uh add your access token which is your API key then at the bottom here you have your operation and you're just going to say current weather 5-day forecast

I'm just going to leave it with current weather uh the format you can choose as metric or imperial you know obviously Imperial is the Fahrenheit metric Celsius I'm just going to leave it as it is for now um location setting so this is going to be the city name that's

going to going to be coming in so the city this is where um we'll be able to grab the city and in this particular this field could be or this parameter could be coming in from our previous node and in our case in our previous node we added San Francisco as our query

so that's exactly what we're going to do and then as you can see on the left hand side on input you have a query that's coming in and it shows San Francisco right so there's the schema there's a table view and there's a Json view right so if

you're you know familiar with code you can uh work with JSON and that also is very helpful um because it gives you exactly the code version of it but you can just stay in schema because this if you're not familiar with code or you're

not good at coding you just grab basically uh this schema from here so what I'm going to do is just basically grab this and bring it over here to the city and as you can see this shows this uh converts it into um a JSON field

which again it's a JavaScript field so all right so that that's pretty much we're done with this point we're just going to leave the language is English so let's go ahead and test this step out and again I'm going to click on test

step and there you go so because my um API is connected one thing I want to point out that when you create your open weather map API account it does take about 5 to 6 minutes for your API key to get operational so just once you create

that account just be on the lookout there because if you click on this it's just going to give you an error so once you create your account your API key just wait a couple of minutes before you test this step out all right so now let's

take a look at the output here as you can see it gives us the longitude latitude and again all this data is coming into to us via our open weather map account or our open Weather API there uh it gives you the temperature what it feels like pressure humidity all

of this right in a in a table format because you output you also have the option to have the output in table format in JSON or in a schema view but the table view this also gives you the data in a really well structured way and as you can see the ID and the name is

the San Francisco because this is coming in from our query here all right so that looks good so now let's go ahead and add another field to this where we want to convert this data that's coming in from this API in this format and convert it

into simple text so that way we can attach it to our um to our AI agent here so that way it can absorb that in plain text so in order to do that we're going to go ahead and add another uh note to this and that is going to be again our

open um open AI chat model so again we're going to use our large language model to be able to um convert this table format output that we're getting from our open weather map uh API into plain text so I'm going to go ahead and search for open AI click on open AI and

you have all these different options you have assistant actions you have text actions image actions audio actions so it gives you a lot more comprehensive ways to use this um open AI tool but I'm going to go ahead and click on message a model all right so same thing your

parameters the credentials is going to be your account um the resource we're going to leave it as text operation but but you do have option uh to um have image audio file and Etc all right the operation we're going to leave as message a model um you can have custom

API call or you can classify the text violation all right so now for the model same thing we're going to choose from the list I'm going to use GPT-4 all right so for the message here let me give myself some more room here so this is where we're going to put our

prompt right so let me go ahead and put my prompt here so you don't have to see me typing okay so I just basically said uh please convert the input from OpenWeatherMap into plain English with a friendly tone to make it prettier use relevant emojis again you can work with

the prompt and try to change it based on the result you get to make sure that it's exactly the way you want it but for now this is good for this example right and the data so I'm going to say here is the data and I'm just going to again

grab the data that's going to be coming in from here so you have several options you can uh grab individual nodes so for example I could just grab this and if I bring it here on the bottom as you can see it just says uh json.weather so

this is going to give you the weather there but if you want to again grab everything and not worry too much about it you're just going to you come click on that JSON field here and literally just grab everything right so I'm just going to go ahead and like select everything and

come here and paste all right so that's good let's go ahead and test this out so on the bottom of the role you just leave it as user for now so let's go ahead and test it out let's see if this works and again all this data here it's coming in

from our previous note which is our um OpenWeatherMap API that we called uh to grab the data for San Francisco and bam there you go as you can see on the right hand side here really cool way to put all of that data and convert it into

a map image and again this is coming in because of our prompt here right um so same thing play around here if you see that hey you know what you don't like some of this or if you don't like the emojis just change the um change the

prompt to to your liking but for now this is good to go again this is a table format but the JSON format gives you a more uh structured way to see this this data all right cool so we're pretty much done from here so now what we're going

to do is add another query to this so that way we can convert this data because again like I said this is still in a format that has the role in it the content in it this refusal all the stuff in there but what I want to do is just

make this really nice and simple so that way we can have access to it with our AI agent so what I'm going to do is again same thing add another query here so I'm just going to go ahead and go to data transformation edit fields and this time

I'm going to change this to response change the name to response cool and then the same thing you just just leave the mode as manual mapping um for add field you click and you can name this so I'm going to same thing name this response keep it as string because we're

going to grab the text here right and for the value now all I have to do is just grab this uh this content here right so all I have to do is just if I go to schema I can whoops I can just grab the content perfect okay so that's cool so

we're done there and now let's go ahead and test this out again so I'm going to click on test app and there you go you got uh the nice response here this is an adjacent on from let's go ahead and table and this gives you um you know the

better view that hey like this now the me this message is only coming in from this the execution of this workflow right and this message is under the response field that we have set so now we're going to do is grab this response field and add it to our AI agent so that

way it can have access to this message only when we're chatting with it so let's go ahead at this point we're done with this as you can see this is successful uh so now we're going to switch to our previous workflow our a uh

AI agent workflow and we're going to now connect this workflow tool or sorry connect this workflow to this AI agent via this n8n workflow tool so this is what as I mentioned before makes n8n really uh different from other tools because you can add separate

workflows to it all right so you can again rename it but I'm just going to leave it as it is for now uh parameters so let's go ahead and name this parameter so I'm just going to say weather tool right because again this is

uh um our weather workflow that's coming in from that separate tool there so I'm just going to name it weather tool you can name it whatever you want all right so for the description oh give me a oh yeah you're not allowed to have a space so

for the description this is where where you want to instruct this workflow you know to what to do so again gives you that uh placeholder call this tool to get a random color the input should be a string with comma separated names so this is just a um place order but it just

gives you the format of how to write a description so what we're going to say is use this tool to get the current weather data for a specific city and we're going to say input should be the name of the city all right perfect that is good all right so now

here's kind of the magic here's how you attach um your other workflows so you come here and select Source database um so if you Source this defined below this just gives you the option to just attach or copy and paste the Json uh so if you're familiar with code again same

thing you can just bring the Json from your other workflows and just paste it here or the easiest way to do is just click on database and you can grab the workflow ID the workflow flow ID is always if you just look at the URL on top here so let's go back here if I

click right here as you can see the workflow ID is this ID at the end of it so you know it says AI workshop. app. nn. Cloud slworking2 which means that your AI agent has um access to this workflow over here because we added this tool all

right perfect so now we're done there you go now that error disappeared because this workflow tool is now attached to this one because of the fact that we added that ID there right okay so now let's go ahead and test this thing out right so I'm going to go ahead

and click on save so now if you're asking a weather related question this AI agent will automatically decide to grab that data from this workflow tool but if you're asking it anything nonrelated to uh weather it's going to grab that data from Wikipedia and we're going to see

that in uh real life so let's go ahead and start a chat here so earlier we asked it what is the capital of Spain and it answered the capital of Spain is Madrid and it used the Wikipedia tool here so now let's go ahead and ask it

what's the weather in San Francisco all right perfect there you go now as you can see it's using it's giving us the weather in San Francisco that is 23.4 6 Celsius varying between low blah blah blah and on the right hand side as you

Step-by-Step Tutorial: Build A.I. Agents with n8n (NO CODE!!)

AI Workshop

인터넷이나 API에서 데이터를 가져오는 AI 에이전트를 구축합니다.

다양한 툴과 LLM(OpenAI, Anthropic)을 연결하는 Nandan의 강력한 성능을 보여줍니다.

AI 에이전트는 Wikipedia 또는 사용자 지정 Nandan 워크플로우(타사 API에 연결)를 사용할지 여부를 독립적으로 결정합니다.

난단의 도구 및 사용자 지정 옵션 사용에 대한 종합적인 튜토리얼입니다.

난단은 강력한 워크플로 자동화 도구로, AI 에이전트 구축에 있어 Make.com이나 Zapier보다 뛰어납니다.

다양한 도구와 앱을 연결할 수 있는 광범위한 사용자 지정 옵션을 제공합니다.

데이터 소유권을 위해 로컬 컴퓨터에서 자체 호스팅할 수 있습니다.

무료 클라우드 계정 사용 가능, 향후 동영상에서 셀프 호스팅에 대해 논의할 예정입니다.

워크플로 시작점 설명; Make.com과 비슷하지만 더 많은 사용자 정의 기능이 있습니다.

난단에서 AI 에이전트 워크플로 구축하기

수동 트리거 노드 추가하기(향후 동영상에서 채팅이나 이메일과 같은 다른 트리거).

'AI 에이전트' 노드를 선택하고 언어 모델을 첨부합니다(이 예제에서는 OpenAI의 GPT-4를 사용함).

API 키를 통해 OpenAI 계정을 연결하고 언어 모델을 선택합니다.

나중에 참조할 수 있도록 채팅 데이터를 저장하는 메모리 노드(Windows 버퍼)를 추가합니다.

도구 추가: 수학 연산을 위한 계산기 및 지식 액세스를 위한 위키백과.

채팅 트리거를 추가하여 AI 상담원과 실시간으로 상호작용할 수 있도록 합니다.

AI 에이전트 테스트: 스페인의 수도에 대해 질문하기(위키피디아 도구 사용 시연).

날씨 데이터를 위한 별도의 워크플로 통합

다른 워크플로를 호출하기 위해 n8n 워크플로 도구를 추가합니다.

타사 API(OpenWeatherMap)를 사용하여 날씨 데이터를 검색하는 새로운 워크플로우 만들기.

데이터 변환을 사용하여 데이터 조작하기, 도시 이름(샌프란시스코)으로 쿼리 필드 설정하기.

API 키를 사용하여 OpenWeatherMap API를 연결하고 현재 날씨 데이터를 검색합니다.

OpenAI를 사용하여 날씨 데이터를 표 형식에서 이모티콘이 포함된 일반 텍스트로 변환합니다.

다른 쿼리를 추가하여 응답 텍스트를 세분화하여 보다 깔끔한 출력을 얻을 수 있습니다.

날씨 워크플로우를 AI 에이전트에 연결하기

AI 상담원 워크플로로 다시 전환하고 새로 만든 날씨 워크플로를 연결합니다.

n8n 워크플로 도구를 추가하고 매개변수 '날씨 도구'를 지정합니다.

URL의 워크플로 ID를 사용하여 날씨 워크플로를 첨부합니다.

통합 AI 에이전트 테스트: 샌프란시스코 날씨 질문하기(워크플로 선택 시연).

질문에 따라 Wikipedia와 n8n 워크플로우 중 하나를 선택하는 AI 에이전트의 능력을 보여줍니다.

튜토리얼 요약과 그 가치에 대해 알아보세요.

향후 고급 AI 에이전트에 대한 동영상과 다른 자동화 도구와의 비교를 발표할 예정입니다.

n8n을 사용하여 코딩 없이 AI 에이전트를 구축하는 방법을 설명하며, 이 에이전트는 인터넷에서 데이터를 가져오거나 API에 연결된 워크플로우를 호출할 수 있습니다.

n8n은 다양한 도구를 연결하여 AI 에이전트를 강력하게 만들 수 있으며, OpenAI와 같은 대규모 언어 모델을 사용할 수 있습니다.

AI 에이전트는 질문에 따라 Wikipedia에서 데이터를 가져오거나, 날씨 API를 사용하여 특정 도시의 날씨 정보를 얻을 수 있도록 구축되었습니다.

🤖 n8n을 이용한 AI 에이전트 구축 소개

이 비디오에서는 **n8n을 사용하여 인터넷에서 데이터를 가져오거나 API에 연결된 워크플로우를 호출할 수 있는 AI 에이전트를 구축하는 방법**을 단계별로 안내합니다. n8n은 다양한 도구를 연결하여 AI 에이전트를 강력하게 만들 수 있는 워크플로우 자동화 도구입니다.

n8n은 **워크플로우를 사용자 정의하고 다양한 도구를 연결할 수 있는 강력한 기능**을 제공합니다. OpenAI, Anthropic 등 다양한 대규모 언어 모델(LLM)을 연결할 수 있으며, 필요에 따라 위키피디아 도구를 호출하여 인터넷에서 데이터를 가져오거나, 별도의 워크플로우를 호출하여 외부 API 앱에 연결할 수 있습니다.

n8n은 Make.com이나 Zapier와 같은 다른 자동화 도구보다 **뛰어난 사용자 정의 옵션**을 제공합니다. 또한, 로컬 컴퓨터에 자체 호스팅하여 데이터를 완벽하게 제어하고 개인 정보를 보호할 수 있습니다.

🛠️ AI 에이전트 구축 과정: 노드 추가 및 설정

n8n 워크플로우에서 **첫 번째로 추가할 노드는 트리거**입니다. 수동 트리거 외에도 채팅, 이메일 등 다양한 트리거 옵션을 사용할 수 있습니다. AI 에이전트에 AI 도구를 연결하려면, 고급 AI 노드를 선택하고 AI 에이전트를 클릭합니다.

AI 에이전트에 **언어 모델을 연결하려면 챗 모델 노드를 추가**해야 합니다. n8n은 OpenAI, Anthropic, Grok, Llama 등 다양한 언어 모델을 지원합니다. OpenAI API 계정을 연결하여 GPT-4와 같은 모델을 선택할 수 있습니다.

**메모리 노드를 추가**하면 AI 에이전트가 이전 채팅 내용을 기억하고 참조할 수 있습니다. 또한, 계산기 도구를 추가하여 수학적 연산이 필요한 질문에 답변할 수 있도록 할 수 있습니다.

위키피디아 도구를 추가하면 **AI 에이전트가 위키피디아의 지식 리소스에 접근**하여 대화할 수 있습니다.

💬 채팅 트리거 설정 및 테스트

수동 트리거 대신 **채팅 트리거를 추가**하면 AI 에이전트와 직접 대화할 수 있습니다. "스페인의 수도는 어디인가?"와 같은 질문을 통해 위키피디아 도구가 제대로 작동하는지 테스트할 수 있습니다.

🌐 외부 API 연동: 날씨 데이터 가져오기

**n8n 워크플로우 도구를 사용하여 다른 워크플로우를 호출**할 수 있습니다. 이를 통해 AI 에이전트에 외부 API를 연결하여 날씨 데이터를 가져오는 기능을 추가할 수 있습니다. 새로운 워크플로우를 생성하고, OpenWeatherMap API를 사용하여 특정 도시의 날씨 데이터를 가져오는 방법을 설명합니다.

날씨 데이터를 가져오는 워크플로우에서 **데이터 변환 노드를 사용하여 API 응답을 처리**하고, OpenAI 챗 모델을 사용하여 데이터를 일반 텍스트로 변환합니다. 이렇게 변환된 텍스트는 AI 에이전트가 이해하고 활용할 수 있습니다.

🔗 워크플로우 연결 및 AI 에이전트 테스트

날씨 데이터를 가져오는 워크플로우를 **AI 에이전트 워크플로우에 연결**합니다. 이제 AI 에이전트는 질문에 따라 위키피디아 또는 날씨 데이터 워크플로우를 선택하여 답변할 수 있습니다.

"샌프란시스코의 날씨는 어떤가?"와 같은 질문을 통해 **AI 에이전트가 날씨 데이터 워크플로우를 사용하여 답변하는지 확인**할 수 있습니다. AI 에이전트는 질문의 내용에 따라 적절한 도구를 자동으로 선택하여 답변을 제공합니다.

향후 비디오에서는 **n8n을 사용하여 더욱 강력한 AI 에이전트를 구축하는 방법**과 다른 자동화 도구와 비교하여 n8n의 장점을 소개할 예정입니다. n8n을 통해 다양한 AI 도구를 활용하여 강력한 워크플로우를 구축할 수 있습니다.