Everything I Learned About AI Agents in 2024 in 19 Minutes

imagine having a team of workers that never sleeps handles repetitive tasks with precision and costs a fraction of hiring a human welcome to the world of AI agents this year I discovered their potential and it changed everything for me if you guys don't know me my name is Nate I've been working in automation for a while now but when I started learning about AI agents earlier this year something clicked and I knew I had to throw myself at it completely so I quit my full-time job to throw myself entirely at mastering and building AI agents

because I saw how these tools could cut costs transform workflows and unlock potential in ways that tradition automation never could in this video I'm going to unpack everything that I've learned about building AI agents over the past year into the next 19 minutes we'll cover the foundations challenges I faced and the secrets to building scalable systems that actually work I'm going to move pretty fast through these Concepts so let me know in the comments if I touch on anything that you'd like to see a more detailed video on so stick around because by the end

of this video you'll have the tools and insights to start building your own AI agents today all the AI agents you see right here I built out in NN so if you're interested in NN and building agents check out my channel cuz I've got lots of resources there real quick I just wanted to say thank you guys so much for 10,000 Subs it makes me so happy to see that we were able to hit this Milestone within just under 3 months so I'm super super appreciative for each and every one of you because of this

we're doing a $100 giveaway the winner will be announced January 1st and all you have to do to enter is join the free school community and then comment down below your school username so let's not waste any time and hop into the key components of AI agents real quick I just wanted to break down the difference between an assistant and an agent an AI assistant is a reactive system that performs tasks through direct user interaction like setting reminders or answering questions but an AI agent operates autonomously so the key word here is autonomy they're able

to proactively manage complex multi-step processes and make decisions without constant input so like I said the key difference lies in autonomy and complexity assistance require Direction whereas agents strategize and act independently so breaking down the core components of AI agents first up we have the core agent think of this as the brain of the operation the core agent is the central processing unit that integrates all functionalities every decision every action it all stems from the core without it the AI agent is just a bunch of disconnected tools and data next we have memory this is

really a game Cher because the memory is what allows the agent to store and retrieve information so that it can maintain context and continuity over time which means that the agent doesn't have to start from scratch every time it wants to tackle a new task so it's like imagine you have an assistant that can remember every single detail of your last conversation and previous conversations so that's what the memory is going to do then we have tools these are the external resources or apis that the agent is going to use in order to perform specific

tasks and take action whether it's like sending emails or managing your calendar pulling data from a database tools are what give the agent the arms and legs the more tools that you give your agent the more versatile it becomes but it's all about finding that balance finally and probably most importantly we have the prompt this is where the magic of problem solving really happens The Prompt helps the agent analyze problems devise a strategy and ultimately determine the steps that the agent needs to take to get the job done it's what makes the agent more than

just reactive it makes the agent agent proactive tackling tasks with a clear plan in mind so now that we understand the core components let's talk about the capabilities of AI agents the first one is Advanced problem solving the agent can analyze situations plan tasks and then execute on those plans whether it's generating a detailed project report writing functional code or creating summaries of vast data agents are able to handle repetitive tasks that would take humans hours to complete next we have self- reflection and Improvement this is one of the most impressive capabilities of AI agents

they can analyze their own output identify problems propose Solutions and even communicate with other agents which is super cool this ability to improve through iteration sets them apart from static automation systems then we have tool utilization so obviously tools are what give the agents the power to act and they use them in a lot smarter ways than you may originally think once you hook up different tools to an agent and Define what each tool does they're able to decide which ones they want to use and in what order obviously as the use case gets more

complex you're going to have to you know work in some more structured prompting but these agents are gen genely pretty good at understanding how to conduct themselves when they're given a role and an incoming query finally we have collaborative multi-agent Frameworks like I touched on earlier agents can speak to each other which is super cool one might handle planning one might handle critiques one might offer feedback and this iterative Loop where agents can collaborate and talk to each other dramatically enhances their performance all right so moving on to something that I've realized over the past

months is so so important the foundation which is data and context we've all seen what they're capable of but we need to talk about the fuel that powers these capab abilities which is data and context without high quality data up-to-date data even the most advanced AI agents it's like having a car without gas so data is key we've all heard the phrase data is the new oil and when it comes to AI agents it couldn't be more true an agent is only going to be as good as the data that it has access to if

the data is outdated or incomplete the agent will make poor decisions and ultimately give you inaccurate results it's not just about the data though the agent also needs context context gives the data meaning without context even accurate data will lead to poor results agents need to understand the situation that they're working in in order to make the right decisions for you the question then becomes how do we give agents the data they need while also maintaining context this is where Vector databases come into play these tools allow us to store and retrieve data in a

way that's both fast and contextually aware systems like pine cone for example let agents search vast amounts of information and find exactly what's relevant to their task a vector database basically stores information in a way that captures meaning and context of data and not just exact matches it represents data as numerical vectors which allows the AI agent to quickly search through and retrieve the most relevant data based on similarity even if the exact words or phrases don't match sort of like when you do a Google Search and so diving into the agent space you're going

to hear a lot about Vector databases and you're going to also hear a lot about rag which is retrieval augmented generation and as you can see based on this image it's just the process of the agent going out to retrieve information and then getting it back and generating an answer as I alluded to earlier another key factor here is keeping the data up to date business data changes constantly New Leads come in deals close projects evolve if your agent isn't working with the latest information its performance is going to suffer so how do we have

the right mindset when we're approaching building out an AI agent or an agent framework when I approach building an AI agent I don't jump straight into NN which is what I typically use I don't jump straight in there and start dragging around nodes and connecting things and writing prompts the first step before you build anything is setting up the data Foundation this means asking yourself how can I most optimally take all this information that I have have over here and organize it and structure it in a way that the agent can accurately retrieve and use

so Step One is building a robust data framework it's going to be a big big chunk of the process getting all your information into a database like a vector database in a way that's clean organized and optimized for the agent to use efficiently this is the foundation if the agent doesn't have access to Accurate well structured data like we talked about it's not going to perform well then another huge chunk is Road mapping your goals once your data Foundation is in place you need to understand what are you trying to accomplish what specific tasks do

you want the agent to Handle by breaking down your objectives into smaller actionable tasks you can build out an agent framework with Clarity and purpose what we're trying to avoid here is building out an agent that initially works but then 3 months later having to rebuild that agent then we move into the build phase which won't take as long if you have all your data in place and you have goals so this is where you can actually get into nadn start dragging and dropping Tools around plugging in workflows assigning tools and testing your agent functionality

but even here testing an itation IS key which moves into the fourth step testing and refining this is the final piece of the puzzle it isn't just about seeing if your agent works it's about exposing it to different scenarios identifying gaps in your logic because trust me there will be gaps and then testing helps you uncover these edge cases to refine your database and your workflows further so by starting with a strong Foundation you set yourself up for long-term success a robust framework not only makes the initial build smoother but also makes it easier to

update and scale your agent over time this mindset is going to help you prioritize building up a good foundation which is what separates a functional agent from one that's constantly breaking or requiring fixes down the line this leads gr into the point of architecture and why architecture matters it matters because we want to build out agents that are scalable and that we won't have to rebuild later by architecting these agents with the goal of continuing to add on top of them we can create some really cool systems that don't require the infrastructure to be reset

if we want to make changes later on so here are the things that you need to think about first thing is to think about your inputs and your outputs when you're planning out the I agent you need to think about what is the agent going to receive and then what do we want the agent to give us the simple step is really important in understanding the tasks that the agent needs to perform so let's say You're Building some sort of email agent the input is reading an email and the output is responding to an email

some tasks within that process between the input and output are going to happen every time but others are going to be situational like let's say we need to scrape availability or let's say we need to generate some sort of quote or let's say we need to check a knowledge base those things are going to be situational based on the input so thinking through these scenarios will help you decide what tools the agent should have access to and which ones should be baked into the logic of the workflow that are going to happen every time for

example if you want every single conversation to be stored in some sort of database you wouldn't want to give the agent a tool that can do that if it happens every time you want to bake it into the logic of the output or following the output so breaking things down it's super important to do this for every project every workflow within the project and every task within every workflow so that we can have these tasks that we can connect like Legos each Lego has a clearly defined job and together they perform complete system this modular

approach ensures that your agents are focused reliable and easy to update the best approach here is to create job function-based agents each agent specializes in a particular workflow like email management or scheduling or lead qualification the specialization makes them more efficient and easier to debug ultimately this is going to make your systems more modular and modular design is key to scaling your systems like we talked about breaking your agents down into smaller independent modules you can easily remove or add or insert or update specific functions without having to redo the entire infrastructure because you can

just sort of plug and play these things in and then when you have all these different agents that have different specific functions you're able to connect them to different tools so let's say you build an agent workflow that sends emails you can now have this tool be called by multiple other workflows whenever you need that function of sending an email okay so now this sort of relates back to thinking about your inputs and your outputs sequential versus parent chaining on the left we have the sequential and on the right we have parent chaining so what

is sequential chaining it's exactly what it sounds like one agent performs its task passes the output directly to a next agent which takes that input from the agent and then performs a task and then passes the output to another agent this linear approach is straightforward and it works well for processes where each step depends on the previous one the biggest Advantage here is its Simplicity it's easy to set up and debug because each step follows logically from the last however it can become a bottleneck if one agent takes too long to complete its task or

just fails entirely then we have parent chaining which involves a central parent agent that coordinates multiple child agents instead of a linear flow the parent agent evaluates the situation and then it's able to delegate tasks to different child agents appropriately the parent collects their outputs and then integrates them into one cohesive result which is the output this system is going to be a lot more flexible and resilient compared to sequential chaining because if one child fails the parent can reroute the task to another agent or have the child agent try again and handle the error

more gracefully however it does require more planning and a little bit more complexity in terms of the architecture so how do you choose between these two systems it really depends on the workflow sequential training is best for straightforward processes with a clear order of tasks and parent training is ideal for complex or more Dynamic scenarios where multiple tasks can run in parallel and where some of the tools are situational understanding these methods is crucial for Designing effective AI agent workflows whether you go with sequential or parent or even like a hybrid approach choosing the right

method will determine how well your system performs all right so now moving on to like I said what is probably the most critical skill for making agents that work as expected which is prompt engineering the quality quality of your prompts determines the quality of your agent's output a well-crafted prompt guides the agent's reasoning ensures it understands the tasks and reduces errors so it's super super important so there are a few essential components to every strong prompt and breaking these down will help you engineer them effectively starting off with the objective you need to define the

agent's overall goal this gives the agent a clear sense of purpose then we have context this provides the agent with all of the relevant background information and it helps it understand the environment that it's working in obviously we have tools you want to outline each tool the agent can access what each tool does and when to use each one then we have instructions you want to be explicit about what you want the agent to do and how you want it to take action output requirements specify exactly what the output should look like and finally we

have examples these are really the Lyn pin of a good prompt they show the agent what a successful flow and what a successful output looks like reducing ambiguity if you want a deeper dive on prompting for AI agents specifically check out this video that I'll tag right here where I sort of dive into this topic obviously this process of prompt engineering is not a one andone you're going to need to test refine and retest your prompts to get consistent results start simple observe where the agent struggles and adjust accordingly like I mentioned earlier exposing the

agent to different scenarios to see where it has gaps is an essential part of making sure that your agents can be trusted and testing and refining the prompts is a huge chunk of this now I want to talk about challenges that I face when I'm building a agents it's a very fun and exciting process but trust me it's not without its challenges because you're going to run into a lot of issues and there's going to be a lot of headaches so let's dive into some some common hurdles that I face and how you they can

derail your process but also what you can do to overcome them so the first thing is data quality it's easy to overlook how your data is being prepared and fed into the database especially when everything is initially working because you think you're good to go but trust me you want to get it right the first time I've gotten to the end of a build only to realize that the information in my Vector database was unoptimized missing metadata and even had some nonsensical vectors so I knew this would only cause the agent to get confused as

we continue to and more data so it forced me to go back and fix the entire data pipeline the takeaway here is to pay close attention to how your data is being chunked what the metadata looks like and how it's stored in your vector database try to automate the data ingestion process to ensure everything is consistent and always test the database early and often during the build to catch issues before they snowball if you want to understand better how your data is being chunked and split and ultimately fed into a vector database then go ahead

and watch this video I'll tag it right up here where I sort of show a lot of examples of how that works next we have poor planning another challenge I faced a ton is building a system that works initially but ultimately it's not scalable you know early on I start building an agent with a specific workflow in mind it works well but then when I try to add new features I realized that the architecture couldn't handle it so I ended up having to start over from scratch the takeaway here is planning out your builds is

crucial before you jump into NN or any other agent building platform you want to map out your goals your workflows your tasks that your agent needs to handle not just now but in the future because you're probably going to end up adding on more capabilities like we talked about you want to adopt a modular design breaking tasks into reusable components or tools that multiple agents will be able to call on later this not only makes your builds more scalable but also saves you time when you need to Pivot or add features later on third we

have the balance between Simplicity and flexibility there's the challenge of balancing these things because if your workflows are too rigid adding new features becomes difficult but if they're too overly complex it can be harder to manage and debug you want to start simple and focus on making workflows modular as your agent capabilities grow you can layer lexity without breaking the system finally just wanted to talk about the mindset of having realistic expectations agents are not perfect and they will break and they will fail but whether that's due to something you did or changes in the

data or um shifts in the platform and the tools that they rely on breakdowns are inevitable and it's okay so the takeaway here is that you can't expect Perfection but you want to prepare yourself for troubleshooting and learning from those failures because every time an agent breaks you're gaining insights into how it operates and how to make it better so adopt a mindset of curiosity adaptability view every issue as an opportunity to understand your agent better and refine it over time you're going to develop a troubleshooting process that not only solves your problems but makes

your future builds stronger okay so the future of AI agents starting to wrap up here but let's zoom out and think about the bigger picture what does the future hold for AI agents and why should you start building them and understanding them today this isn't just a passing Trend this is the future of work and automation as these systems become more advanced they're poised to replace repetitive low value tasks freeing up humans to focus on creativity strategy and innovation in the past businesses scaled by hiring more people in the future scaling will mean building smarter

AI agents to handle complex workflows with minimal human input and so there's some concern around this right but the future isn't about AI replacing humans it's about how can humans and AI work together how can humans leverage AI to act as teammates not just tools collaborating with us to achieve goals faster and more efficiently why is right now the time to start because the technology is still evolving we're still pretty early in this paradigm shift but that's exactly why right now you want to get involved by starting early you can sort of position yourself as

a leader in the space gaining experience and insights that you need to stay ahead of the curve so here are a few trends that I think we need to keep our eyes on within the next few years the first one is increase autonomy agents are obviously going to become more self-reliant and smarter and there will be a day when agents can build other agents which will will be pretty cool but also maybe a little scary um anyways number two enhanced collaboration multi-agent systems will become more common with agents working together to handle these more complex

workflows we're going to have broader accessibility so no code and local platforms are already super accessible but these will be even better for more you know non-developers non-technical users and then finally more integration into everyday tools so this is already happening but agents are going to be embedded more often in tools we use every day like our crms our email platforms and project management systems stuff like that it's just going to become super super common so the takeaway here is that AI agents are not just the future they're the present so the sooner you start

learning experimenting and building the better you're positioning yourself to take advantage of this paradigm shift in the future so if you haven't joined my free school Community for the giveaway already I would encourage you to do so it's a great Community you can get your questions answered it's a committed group of people that are you know dedicated towards building out AI agents and learning how they can help businesses and also any videos that I make in the future all the resources will be on there for free then I also have a paid community that you

can check out in the description if you want to take your skills a little bit further if you want access to five weekly calls real project insights and stuff like that then my last call to action is if you're looking to have this sort of stuff built out for you or for your business or you're looking for AI consultancy Services then book a call using the link to my website in the description but that's all really appreciate you guys making it to the end really appreciate all the support so far and I'll see you guys

in the next one thanks