I get asked a lot what my process looks like for building AI agents so right now I am kicking off a super exciting miniseries we're going to walk you through step by step my entire process for building AI agents and as a part of the series together we're going to build a full AI agent that can consume entire GitHub repositories so you can ask questions about all of the code there super helpful if you're not Technical and even if you are because it can save a lot of time getting a grasp on a new Repository now to start things off before we even go and start building an AI agent in this video I want to give you an overview a step by step of my entire process for building AI agents starting from the basics and going all the way to having your agent in production the goal here is to provide a ton of value really really fast for you so that you have an entire framework for how to build agents and then as the series continues we'll actually go through all that and see what it looks like to go through each step and then to continue the series I'm actually going to be doing a live live stream on the 28th at 9:00 central time it is the second official live stream that I've ever done on my channel so I am super stoked and you do not want to miss it together on this stream we're going to build a full prototype for the GitHub agent with n8n and Gemini 2. 0 flash it is going to be a blast and the best part is I'm also going to integrate the agent with the automator live agent Studio that I released recently I'm super excited about and it's going to give us a full front end with chat and conversation history history for our agent and I'll even have the agent published there during my live stream so you can check it out and play along with me as we are learning how to build it now this whole guide for building AI agents is going to be really really helpful for you whether you're using the live Asian Studio or not but I still want to focus on it a little bit here because I've got some big big things coming up for the studio soon including the Christmas present that I already alluded to in another video coming on the 25th for you and then we have the live stream happening on the 28th at 9:00 Central Time and also a lot more agents coming to the platform very very soon and so with that let us do a deep dive into my entire process for building AI agents all right here we are with a bird's eyee view of my entire process and this is what we're going to go over right now and then throughout this miniseries we're going to break this down step by step building an AI agent together following this entire process so don't worry if even any of this seems intimidating to you right now because it is my job to make this crystal clear for you throughout the series so that you can very easily build an agent that is also production ready and so with that we can dive into the first step that we have here planning your agent it's kind of a more obvious step but I really want to emphasize this here because it can save you hours and hours of going down the wrong rabbit holes if you are actually doing some planning up front so ask yourself these questions write out your answers in a document chat with an llm about these things it can help a ton and the questions they can be pretty basic like what are the core functionalities I want for my agent which llm do I want to use like if you want to use local llms you can do some research ahead of time which apis do I need to set up it can help a lot to have part of your environment set up before you get started and then for another good last example question here what does a good V1 look like cuz a lot of times when you're building an agent you have these grandiose ideas of what you wanted to do and it can be very fatigue to try to get there right away so starting with a simpler more like proof of concept type agent can be a really good way for you to have something more easy to shoot for initially now the next step after planning your agent is building a prototype for it using a no or low code tool like n8n flow wise or voice flow all three of these I highly recommend I cover on my channel and actually n8n pairs very well with voice flow and also with flow wise so that's a good idea as well and your goal here is just to build something very very fast that's still pretty powerful so you want to build something that's got POC it's working it's useful you can chat with it it can interact with your tools but don't even focus on the front end or the database yet and this is what we're going to be doing in my live stream on the 28th at 9:00 central time using n8n with Gemini 2. 0 flash as the llm the sponsor of today's video is voice flow a no code AI agent development platform that really stands Above the Rest in my eyes and I mean that genuinely and I actually reached out to to them to sponsor this video which I have never done before and I did it for three reasons first of all they are partnering with NN pretty soon here and they told me that I could tell you all that which is super exciting because I love n8n and n8n and voice flow just pair so well together that I'm just psyched about that I've even made a video on my channel already showing how you can use voice flow with n8n the second reason is obviously this plays really really well into what I was just talking about using these no code tools to prototype agents and with voice flow you can definitely take these agents all the way to production as well which is super useful if you don't want to do any custom coding and then the final reason is voice flow has this incredible API to interact with agents on their platform called the dialogue API and it works really really well with the live Asian Studio that I develop so in the upcoming weeks here I'm going to be building a direct integration between voice flow and the live agent studio so you can build your agents there and they'll instantly be ready for me to put up on the live agent studio for you so that is also super awesome I have had a fantastic experience using voice flow for many reasons but right now I'll just give you three of them the first one is that they have an incredible feature called intense which is basically your way to define your tools and incorporate them within your AI agent to do things like interact with thirdparty services and it's very robust it makes the llm so accurate when it comes to choosing when to use tools and using them correctly the second reason is that voice flow is very very production ready I know entire businesses and consultancies that are using voice flow for a majority of their agents cuz they have incredible production features like logging and monitoring really good documentation and their speeds are insane and the last reason is that a lot of no code agent Builders are very black box you don't understand what's going on and you can't customize it very well but it's not the case with voice blow especially for Rag and working with knowledge bases there's so much customization there that you don't have with other platforms so I'm going to have a link to voice CL in the description of this video a lot more coming soon for them in the live agent Studio as well definitely go ahead and check them out now after you build your initial prototype for your agent the next step is to set up your database because you need one for your chat history for rag if you have a knowledge base and then any other kind of structure that you have to have on the back end to store information and my very honest and simple recommendation is to use super base you can use it for free it uses postgress under the hood it's super powerful you can use it for rag as well and this is what I always use for my agents and also what I use on the automator live agent studio so yeah set up all your tables in your knowledge base and just keep it very very simple after you have your database set up the next optional step is to move your agent to python now I want to emphasize that no/ low code platforms are sometimes actually enough to take your agents all the way to production but a lot of times I feel like I really need to custom code my agents to really get get the customization and power that I'm looking for and that's when I'll move to Python and I want to teach on it I'm going to have that as a part of this series and so that's why I have it here in the process even though you won't always have to do it I would just recommend it a lot of the time and python offers a ton of amazing agent Frameworks like pantic AI that I covered on my channel recently and Lang graph which by the way these two pair very well together I'm going to be putting out some content on that in the future here and then also using AI like wind surf or cursor and those kind of AI idees can make coding agents a lot less daunting so even if you're not a very technical person you can still move your prototype from a no/ l code tool to python quite easily after you have your agent fully built out with python or still within a low no code environment it is time to build a user interface to interact with your agent in a clean way and I have three main recommendations for this first of all you can build a react front end with a tool like bolt.
DIY or bolt. new to interact with your agent and I also know that lovable is a good alternative that a lot of people have had success with too and these tools they can help you hook up everything you need to to actually work with your agent also you can build a streamlit app so streamlet is a fantastic and really easy to use Python UI library and so you can't use bolt cuz it's python but you can use something like winds surf or cursor to help you build the streamlet app super simple to do so and it's made for interacting with llms with a lot of the features they have as well and then the last recommendation is to build an agent for the live agent Studio what which I released very recently super excited about and I have extensive documentation for how you can build an agent for the studio and a lot more content coming on it soon as well and if you integrate it with the studio you instantly have a full front end with chat and conversation history super robust so I'd highly recommend doing that as well another great option after you've got the UI it is time for everyone's favorite part and that is testing your AI agent and I mean that sarcastically cuz a lot of times this is not what people enjoy doing but when you use a tool like winds Sur for cursor it can actually help a lot with writing your unit and integration tests and that is so so important you do not want to skimp out on testing your agents extensively because this is the way that you make sure you cover all your edge cases you make sure that your agent is secure enough that it's giving you accurate information all these things are so important before you start trusting it to do things for you or you give it to other users to use so make sure you test your agents now after you test your agent at least for the first time you now have an agent that's fully built out it's got a user interface and you're pretty confident it's working well and so now the next step is to finally start to take your agent into production and deploy it and my simple recommendation here is to containerize your agent with Docker now obviously this only applies if you're custom coding your agent if you're using a platform like NN or voice flow they have their own way of deploying things to production but yeah if you're building your agent with python like I'm going to show in this series containerize it and then host in runpod that's my recommendation for the cloud platform if you're using local llms and then host in digital ocean if you are not and my simple reason for this recommendation is runpod has the best pricing for GPU instances that I've seen in general there's also a lot of other good platforms like novita Ai and then digital ocean has the best pricing for General instances that aren't GPU instances their GPU instances are very very expensive but their other ones are very affordable and I love them in general and then also generally you're going to want your agent to be behind an API built with something like Fast API if you're using python that's also what I did for the live agent studio and all the agents that I have hosted there built with python CU in general you need an API because you're going to have your front end built somewhere else hosted somewhere else that needs to interact with your agent and get the response from them now the next few steps are pretty interchangeable so the first seven here you definitely want to do it in that order but now what we have next here is setting up monitoring for your AI agent and honestly you could even do this before deploying as well but usually I like to have my production environment up and running making sure that's good and then I'll Implement monitoring because it's really important to watch your agents for failures and performance issues and there's a lot of really great tools some of them free even in open source to make that possible so we got Lang Smith which is super good if you're using Lang graph um or anything from Lang chain Lang fuse which is an open source LM observability platform that's super awesome and then also if you're using pantic AI like I'm going to be using in the series you have log fire which is again open source absolutely fantastic for monitoring one of the more advanced concepts that can honestly fit into a lot of different parts of this process is Agent evaluation it's one of the more hard things to do with agents especially because there's not a lot of tools out there to make it possible but it's very different than testing and it's super important testing is making sure your agent doesn't encounter errors like the application crashing the llm just straight up not able to process the user's request that kind of thing you have to test for but evaluation is ensuring the agent actually gives correct responses and takes correct action so you give it certain inputs and you say is it doing the right thing is it giving me an acceptable answer back that is evaluation also very very important to do now last up we have some Advanced topics that I want to mention really quickly here these are the things that I'm not going to go into great detail for in this miniseries because I want to keep it concise and straightforward but I still want to point these out just to show how much can really go into building an AI agent that is Enterprise level and production grade and there is just so much that can go into it you have things like cost optimization like prompt caching and managing the token window and batching your requests load balancing best practices for security and data privacy and rate limiting and input sanitization this list can go on and on and on and a lot of these things I want to keep covering and maybe having dedicated videos for on my channel but just yeah know that there is a lot that you can do once you have your agent built to continuously improve it and make it more and more Enterprise grade and so with that that is the entire road map that I've got here for my process I'm super excited to just dive into all this throughout the miniseries with you helping you build awesome AI agents and with that you now know my entire process for building production ready and Powerful AI agents and I'm sure you're left with a lot of questions as well but that is what the rest of this miniseries is for cuz I'm going to do a deep dive into every single step as we build out our GitHub agent so come to my live stream this Saturday the 28th at 9:00 central time because we're going to build a full prototype for our agent using n8n and Gemini 2.
