How I’d Learn AI Agent Development in 2024 (if I had to start over)

Summary

In this video, AI expert Arson demystifies the role of an AI agent developer, a highly sought-after job in 2024. He shares his experience running an AI agent framework and an agency, emphasizing the need for developers to understand AI autonomy and the importance of tools in agent performance. Arson provides a roadmap for aspiring developers, highlighting the skills required, including backend development and AI, and offers practical steps from project scoping to monetization. He also stresses the significance of community building for tailoring services and gaining valuable feedback.

Takeaways

• 🌐 AI agents are gaining significant traction worldwide, with tech giants like Google developing their own platforms, indicating a shift from traditional chatbots to more advanced systems.
• 🔍 The role of an AI agent developer is emerging as a highly sought-after job in 2024, yet the field is still in its infancy with many unsure of the skills required or how to become one.
• 👤 The speaker, Arson, introduces himself as an experienced AI agent developer, running a popular AI agent framework on GitHub and an AI agency offering subscription-based services.
• 🤖 AI agents are defined as operating systems that fall between automations and employees, with the ability to access tools and reflect on their actions, contrary to common misconceptions.
• 🛠️ The speaker emphasizes that AI agents should not perform all tasks by themselves but should route requests to appropriate tools, similar to how an operating system works.
• 🔑 The role of an AI agent developer involves ensuring agents have the necessary resources and tools, conducting research, creating tools, iterating based on feedback, and deploying agents within a business environment.
• 💬 Soft skills like communication and eagerness to learn are crucial for AI agent developers, as is the ability to gather requirements and adapt to new advancements in AI.
• 💻 Hard skills needed include light backend development and AI knowledge, with coding being a necessary skill for creating custom tools and managing APIs.
• 📈 The speaker suggests that anyone can become an AI agent developer given the relatively small pool of experienced professionals and the potential for rapid growth in the field.
• 📚 A comprehensive roadmap is provided for aspiring AI agent developers, starting with finding a project, setting up a development environment, learning programming basics, and progressing to more advanced AI development skills.
• 💼 Monetization strategies for AI agent developers include freelancing, cold outreach, and finding full-time positions, with the speaker highlighting the importance of building a community for feedback and service tailoring.

Q & A

What is the significance of AI agents in the current technological landscape?

AI agents are revolutionizing the tech industry by offering advanced systems that surpass traditional chatbots. Tech giants like Google are recognizing their potential, indicating a shift towards more autonomous and capable AI systems.

Why are AI agent developers expected to be in high demand in 2024?

The growing adoption of AI agents across various platforms and the need for specialized skills to develop and maintain these systems make AI agent developers one of the most sought-after professionals in the job market.

What is the role of an AI agent developer?

An AI agent developer ensures that AI agents have access to necessary resources, tools, and knowledge to perform their designated tasks. This involves researching business processes, creating tools, iterating based on feedback, and deploying agents within a business environment.

What is the correct mindset for developing AI agents according to the video?

Developers should not expect AI agents to perform every task by themselves. Instead, agents should route requests to appropriate tools, which do the heavy lifting, similar to how an operating system manages different applications.

What are the soft skills required to become an AI agent developer?

Communication and eagerness to learn are essential soft skills. Communication is needed for gathering requirements and setting expectations, while a strong desire to learn helps developers keep up with the rapidly evolving field of AI.

What are the hard skills necessary for AI agent development?

Hard skills include light backend development combined with AI knowledge. This involves coding, understanding AI models, and being able to integrate various APIs and tools.

Why is coding considered necessary for AI agent development?

Coding is necessary because the most powerful tools require custom code for better control and functionality. Additionally, open-source tools and custom logic often necessitate coding skills, and concerns about data privacy may require deploying agents on private infrastructure.

What is the importance of starting with a project when learning AI agent development?

Starting with a project helps to retain learned information and provides a practical context for understanding AI development. It allows developers to apply their skills to real-world scenarios and find unique use cases by combining AI with their interests.

What are some of the steps involved in setting up a development environment for AI agent development?

Steps include choosing an IDE, installing Python, managing packages with tools like pip and virtual environments, and selecting AI development tools that assist with coding and AI integration.

How does the video suggest learning the basics of programming and AI development?

The video suggests using online resources, books, and practical exercises like cloning and modifying GitHub repositories. It also recommends using AI tools to assist with learning and coding, such as GitHub Copilot and AI-first IDEs.

What is the significance of deploying AI agents in production?

Deploying AI agents in production is crucial for real-world testing and integration. It allows developers to understand the scalability, performance, and practical application of their agents, ensuring they work seamlessly in a business environment.

What are some ways to monetize AI agent development skills?

Monetization can be achieved through freelancing on platforms, cold outreach to potential clients, or finding full-time employment in companies seeking AI agent developers. Building a community can also provide valuable feedback and tailor services to specific needs.

What is the role of function calling in AI agent development?

Function calling allows AI agents to interact with the external world by calling external functions. It is a critical feature for agents to perform actions based on their decisions and is key to measuring the effectiveness of AI systems.

Why is learning to use LLM APIs an important step for AI agent developers?

Learning to use LLM APIs is essential because it allows developers to create agents from scratch, equip them with tools, and fine-tune them on tasks. It provides a deeper understanding of how AI agents work internally and is a fundamental skill in AI agent development.

What are multi-agent frameworks and how do they assist in AI development?

Multi-agent frameworks like Autogen, Crew, and Agency Form handle underlying details for running agent-based systems, such as communication, function execution, and state management. They provide a structured approach to developing complex AI systems.

What is the importance of learning about data privacy in the context of AI agent development?

Data privacy is crucial as many clients are concerned about it and prefer deploying agents on their own infrastructure. Understanding data privacy allows developers to create solutions that meet client needs and comply with regulations.

How does the video suggest finding the first client for AI agent development services?

The video suggests using freelance platforms for quick client acquisition, cold outreach for higher-paying clients, and seeking full-time employment in startups or companies that have recently received funding and are looking for AI agent developers.

Outlines

00:00

🌐 The Rise of AI Agents and Developer Demand

The script introduces the burgeoning field of AI agents, highlighting their growing prevalence in 2024. Tech giants like Google are developing platforms for these autonomous systems, and traditional chatbots are being replaced by more sophisticated agent-based models. The role of an AI agent developer is emerging as highly sought after, despite the lack of clarity on the skills and knowledge required for this position. The speaker, Arson, establishes credibility by mentioning his involvement with a popular AI agent framework on GitHub and his own AI agency. He outlines the intention to provide a roadmap for becoming an AI agent developer and offers a brief overview of what AI agents are, emphasizing their autonomy and how they differ from traditional automations and human employees.

05:03

🛠️ Skills and Tools for AI Agent Development

This paragraph delves into the skills necessary for AI agent development, emphasizing the importance of both soft skills like communication and eagerness to learn, and hard skills such as light backend development and AI knowledge. Arson argues that anyone can become an AI agent developer given the relatively equal starting point for most people in the field. He also discusses the necessity of coding, the advantages of open-source tools, and the importance of understanding AI coding tools, which are simplifying the learning process. The paragraph concludes with advice on finding a good project that aligns with personal interests and how to scope such a project effectively.

10:03

💻 Setting Up the Development Environment

The speaker provides a guide on setting up the development environment for AI agent development, starting with choosing an Integrated Development Environment (IDE) and installing Python, which is ubiquitous in the AI industry. He advises on best practices for package management, such as using virtual environments and tools like pip or venv. Arson also recommends AI development tools that leverage AI to assist in coding, such as GitHub Copilot and Cursor, which are designed to streamline the coding process by combining AI with traditional development practices.

15:05

📚 Learning the Fundamentals of AI Development

The paragraph focuses on the initial learning phase for AI agent development, suggesting resources for learning Python and the importance of understanding programming at a high level. It encourages hands-on learning by creating an agent or modifying an existing repository with the help of AI. The speaker also emphasizes the importance of learning Git for version control and collaboration, recommending resources and tools to master it. The paragraph progresses to the necessity of understanding Large Language Model (LLM) APIs, particularly from leading providers like OpenAI, and the process of creating an agent from scratch for a deeper comprehension of AI agents' internal workings.

20:06

🔧 Advanced Skills for AI Agent Deployment and Monetization

This section covers advanced skills required for deploying AI agents, such as function calling which allows agents to interact with the external world, and the importance of using the Instructor Library for input validation. It also touches on multi-agent frameworks and the balance between control and ease of use. The speaker stresses the importance of actually deploying agents in production and learning light backend API development for scalability. The paragraph concludes with advice on monetizing AI agent projects, finding clients, and the benefits of building a community around one's services for tailored solutions and valuable feedback.

🚀 Conclusion and Call to Action

In the final paragraph, the speaker wraps up the video with a bonus tip on building a community to support and grow with one's services. He shares his personal experience of gaining a client through his YouTube channel early in its development. The speaker invites viewers to subscribe for more information and provides a notion template for scoping AI agent projects, teasing an upcoming course with more in-depth content on AI agent development.

Keywords

💡AI agents

AI agents, or artificial intelligence agents, are autonomous systems that can perform tasks, make decisions, and interact with their environment. In the context of the video, AI agents are revolutionizing industries by taking on roles traditionally managed by chatbots, with tech giants like Google developing platforms to support these advanced systems. The script mentions that AI agents are more autonomous than automations but less than human employees, highlighting their unique position in the tech landscape.

💡Autonomy

Autonomy in the video refers to the degree of independence that AI agents have in performing tasks and making decisions. It is contrasted with the limited autonomy of traditional automations and the full autonomy of human employees. The script explains that AI agents have more autonomy than automations because they can access tools and reflect on their actions, but they are still directed by users' instructions, unlike human employees who can act on their own initiative.

💡AI agent developer

An AI agent developer is a professional who designs, builds, and fine-tunes AI agents for specific tasks and business processes. The video emphasizes that this role will be highly sought after in 2024 due to the growing adoption of AI agents. The script describes the responsibilities of an AI agent developer, which include researching business needs, creating necessary tools, iterating based on feedback, and deploying agents into clients' businesses.

💡Agencies as a service

Agencies as a service refers to a business model where AI services are provided on a subscription basis, rather than as a one-time product. The script mentions that the speaker runs an AI agency that has launched such a service, indicating a shift towards ongoing, flexible AI solutions that can be tailored to clients' evolving needs.

💡AI frameworks

AI frameworks are tools and libraries that facilitate the development of AI applications. In the video, the speaker mentions running one of the most popular AI agent frameworks on GitHub, which allows for the creation of AI agents. These frameworks are essential for developers to build and customize AI agents efficiently.

💡Operating systems

The video script compares AI agents to operating systems, suggesting that they fall between the functionality of human employees and traditional automations. The analogy is used to illustrate that AI agents, like operating systems, have memory, access to tools, and the ability to perform tasks based on user requests, but they require the heavy lifting to be done by specialized tools, just as an operating system uses various applications for different purposes.

💡Function calls

Function calls are a programming concept where a function is invoked to perform a specific task. In the context of AI agents, function calls are crucial for agents to interact with the external world, such as executing commands or accessing data. The script emphasizes the importance of function calls in measuring the effectiveness of AI agents, as they are the actions that agents take based on their programming.

💡Multi-agent Frameworks

Multi-agent frameworks are systems designed to manage and coordinate multiple AI agents. The video mentions several popular frameworks, including Autogen, Crew, and the speaker's own framework, Agency Form. These frameworks handle communication between agents, function execution, and state management, providing a structured environment for developing complex AI systems.

💡Backend API development

Backend API development involves creating and managing the server-side components that power applications. The script suggests that learning light backend API development is essential for deploying AI agents as APIs, which can be done using serverless functions or by creating custom servers with frameworks like Flask or FastAPI. This skill is crucial for making AI agents accessible and scalable.

💡Monetization

Monetization in the video refers to the process of turning a service or product into a source of income. The speaker advises on productizing AI agent development services, reusing code, and finding clients through various means, including freelance platforms, cold outreach, and full-time job opportunities. Monetization is presented as a learning opportunity that also provides financial rewards.

💡Community building

Community building involves creating and nurturing a group of people with shared interests or goals. The video suggests that building a community around AI agent development can provide valuable feedback and help tailor services to specific needs. The speaker shares personal experience of gaining a client through a YouTube channel, highlighting the long-term benefits of community engagement.

Highlights

AI agents are revolutionizing the tech industry, with major players like Google developing their own platforms.

Traditional chatbots are being replaced by advanced agent-based systems.

AI agent developers are becoming highly sought-after professionals in 2024.

The role of an AI agent is not just automation but an operating system with autonomy and access to tools.

AI agents should route requests to tools rather than performing all tasks themselves.

AI agent developers need to ensure agents have necessary resources and tools for designated tasks.

Soft skills like communication and eagerness to learn are crucial for AI agent developers.

Hard skills required include light backend development and AI knowledge.

Coding is essential for AI agent development, despite the rise of no-code platforms.

Open-source tools and custom coding provide more control and adaptability.

AI coding tools are simplifying the learning curve for coding.

Finding a good project is the first step to becoming an AI agent developer.

AI agents should be integrated into a client's business seamlessly.

Environment setup is fundamental for AI agent development.

Learning to use LLM APIs is a key skill for AI agent developers.

Understanding function calling is vital for agents to interact with the outside world.

Multi-agent frameworks like Autogen, Crew, and Agency Form are essential for managing agent systems.

Deploying agents in production requires learning light backend API development.

Monetizing AI agent projects involves productizing services and finding clients.

Building a community can provide valuable feedback and tailor services for specific needs.

Transcripts

AI agents are taken the World by storm

in 2024 but AI agents cannot build

themselves at least not yet even Tech

giants like Google are recognizing the

potential here with their own agentic

platforms and it's not just Google a

wave of stas products are now ditching

traditional chatbots in favor of more

advanced agent based systems and

although some of these platforms haven't

changed a thing beyond the copy on their

lending Pages it's evident that the role

of an AI agent developer will be one of

the most sought after jobs of 2024

However unfortunately because it's still

so early no one really understands what

exactly to look for leave alone how to

become one so in this video I will

finally demystify who an AI agent

developer is what skills you'll need to

become one and most importantly provide

you with a complete road map that you

can follow by yourself right now let's

Dive In

all right before we jump in I'm sure you

might be asking who am I and why do you

have to listen to me and what I have to

say about AI agents so allow me to

introduce myself my name is arson and

I'm currently running one of the most

popular AI agent Frameworks on GitHub

that allows you to create agencies with

AI called agencies form but that's not

all I'm also running my own AI agency

where we recently launched the first of

its kind agents as a service

subscription so unlike other influen

answers I'm not making my living selling

you ads I actually make all of my income

delivering real world AI Solutions in

fact just in the past month I've hired

two AI agent developers myself so

hopefully this now gives you an idea

that I've got a pretty good

understanding of what to look for okay

with that out of the way allow me to

break down what AI agents are I've

previously talked about this on my

channel so if you want to dive into more

details please make sure to watch this

video after here I'm just going to give

you a brief overview because

understanding how to think about AI

agents will significantly impact your

approach to building them from what I've

heard so far typically most of AI

influencers either Define them as

automations or as your own employees in

my opinion both of these definitions are

extremely misleading on the one hand AI

agents have a lot more autonomy than

automations AI automations run on your

back end no matter what even if the

whole world Burns RS down EI automation

is just going to keep running like

nothing happened on the other hand eii

agents have less autonomy than your own

employees because your employees can

defy you you can provide them with as

many instructions as you want but they

can still consider things on their own

and do them in their own way so the

closest definition to AI agents that

I've ever heard was made by Andre

karpathy who said that AI agents are

essentially operating systems yeahi

agents fall in between employees and out

automations they have Ram which is

memory they have access to tools and

they can use other llms and reflect upon

their own actions why am I telling you

this well because often times I've

noticed that some of you guys are trying

to make your agents perform literally

everything by themselves this in my

opinion is wrong you shouldn't just tell

your agent what you want and then hope

it's going to do it all for you your

agent should only route the requests

from the user to the tools all of the

heavy lifting must be performed by the

tools not by your agents think of it as

your own OS like Windows or Mac where we

have different apps for specific

purposes we don't even consider how

these apps function we simply control

them and the same should apply to your

agents you can't just tell them what you

want and then sit back and wait until

you become a millionaire unfortunately

this does not work out yet and at this

point the most significant challenge

that prevents Mass adoption of EI agents

is their real ability currently to make

agents reliable you need to carefully

fine-tune them on a specific business

process and this is exactly where AI

agent developers come in as an AI agent

developer Your Role is to ensure that

your agents have access to all the

necessary resources tools and knowledge

to perform their designated tasks

essentially what it entails is first

conducting thorough research about the

business and determining which of those

resources the agent will need then you

need to create the tools as as I said

tools are the most important while

building your agency you have to

constantly iterate in it gather feedback

from the stakeholders and adjust it in

the loop until it performs consistently

from time to time and finally you must

actually deploy your agents which is a

step that a lot of people Skip and then

integrate them into your client's

business you have to make sure that

these agents work seamlessly alongside

their own employees now what skills

required to become an AI agent developer

well as for the soft skills definitely

communication and eagerness to learn

communication because you'll often find

yourself Gathering requirements from the

stakeholders and setting clear

expectations which is not an easy task

and eagerness to learn because we're

still in a very early stages with

advancements in AI emerging all the time

so you have to be able to adapt very

quickly and as for the hard skills

primarily it will be light backend

development combined with AI so now can

anyone become an AI agent developer in

2024 in my opinion yes anyone can do

this because imagine if you were trying

to excel in a subject like math and

you'd be up against people who' spend

their entire lives mastering it however

when it comes to AI agent development

you'll only be competing with people who

have at least a few months of experience

so in my opinion the timing is just

right even if you're a beginner now I'm

sure that you guys might be wondering if

coding will be necessary or if some no

code platforms will suffice in the

future well the short answer is yes

coding will be necessary here's why

first of all the most powerful tools

that your agent will use will always

require some coding experience sure you

can connect your agents to off the

shelf's apis but typically in real world

scenarios we always create those proxy

end points even if we connect to Off the

Shelf API this gives us a lot more

control over the behavior of the tool

for example we can process the input

data and output data as we see fit

moreover a lot of those tools do require

multiple apis together or some custom

logic inside secondly open source will

always lead the way not in a sense of

developing the most powerful models but

in a sense of actually using them in

Creative projects SAS platforms often

require thorough testing and a lot more

time to release new features open source

reers like my agency's War framework can

adapt to new advancements in AI much

faster and lastly man of my clients are

extremely concerned about data privacy

for them SAS platforms are not even an

option they want to deploy agents on

their own existing infrastructures so

while SAS platforms will definitely

evolve over time and they might satisfy

some of our client needs in the future I

believe that learning to code will still

be a non-negotiable moreover with the

Advent of AI coding tools coding is

literally becoming Ain to using another

SAS platform because you can already

code using only n natural language so

the learning curve is nowadays much

smoother and there is no longer a need

to learn any complex data structures or

algorithms you just have to know them on

a high level so you know what questions

to ask the rest AI can already do for

you my road map factors in all of those

AI coding tools so let's now take the

first step which is to find a good

project yes guys I can't emphasize this

enough you can start without a project

if you don't use it you lose it

everything that you learn without a

project you will simply not retain on

the other hand having a real world

project from the start will give you

your own lens through which you can

interpret all of the following

information and the best way to find a

good project is to think about your own

interests and how they could intersect

with AI your Niche is essentially where

AI combines with your talents all of the

obvious applications for L language

models have already been capitalized

upon if you were to create another EA

agent for research for example you'd be

up against tens of Open Source and

Commercial projects providing this exact

service however the real opportunity

lies in merging AI with your own unique

interests this is how you can find those

untapped use cases that make Captivate

other people just like you for example I

used to be into EDM so I could create an

AI agent that would send me the latest

hits create media files with a custom

coded tool generate certain sounds with

11 laps API or even entire song when a

company like yudo releases their own

API so start by scoping an initial

agentic project include how many agents

you want to create what each agent will

do and what tools they will use

additionally it's helpful to determine

the overall goals and the desired

outcomes of the system if you want to

download my notion template for scoping

an agentic project that we use in our

agency almost every day please feel free

to use the link below it's going to ask

you for your email I'll explain why at

the end of this video once your project

is fully scoped you're ready to move to

the next step which is the environment

setup your development environment is

where you'll be spending most of your

time so it's important to nail it down

from the very start first you'll need an

IDE this is the software you'll use to

write and test your code there are many

idees to choose from but some of the

most popular ones are visual studio code

jet brains and X code I personally

prefer jet brains but it's not free

although you can get it for free if

you're currently a student next you'll

need to install python python is the

most commonly used language for AI

development literally the entire AI

industry runs on python it's also a

great language for beginners because

it's extremely easy to read just follow

all the installation steps on their main

website and if you run into any issues

just send them to chpt please don't send

them into comments after that you'll

need to figure out package management

this is how you'll install and manage

the various libraries and tools you'll

use in your project messing up package

management is the single most common

regret for all python developers

remember that you should never install

packages globally for a specific project

and always create separate virtual

environments I recommend using a tool

like K however python also has a

built-in package manager called vnf

finally you'll need to find your AI

development tools as I said these are

crucial in 2024 honestly I personally

barely even C code myself I just check

code provided by AI some of the most

popular ones are GitHub copilot cursor

chpt Amazon code Whisperer and even your

own agent like David I recommend cursor

at the start which is an AI first IDE

Fork from vs code it's one of those rare

tools that strike the right balance

between coding and using AI they are

also backed by open AI once your

environment is ready to go it's time to

start coding so the next step is to

learn the basics at this stage I do not

recommend diving into too much detail

you just need to learn programming at a

relatively high level there are many

resources available online to Learn

Python quickly you can find tutorials on

YouTube or you can even read books like

learning python which is what I did in

my early days however what you can do

now is also create your own agent or a

custom GPT upload this entire book all

of those YouTube videos and use it to

assist you with your learning process

another great way is to clone a

repository on GitHub that you really

admire and go through it with AI ask

questions about specific lines of code

that you don't understand then try to

modify this repo or add some new

features feel free to try this out with

my own framework and submit a PR if you

create something cool next you'll need

to learn git git is a version control

system that lets you track code changes

and collaborate with others this is

important because sometimes as an agent

developer you might have to collaborate

with your client development team again

there are many resources available to

learn git you can find tutorials on

YouTube or you can use tools like GitHub

copilot CLI a new addition to GitHub

copilot that explains or even creates

any terminal commands for you fireship

also has a great course on git that I

personally recommend once you've got the

handle on the basics the next step is to

learn the modern AI development text tag

the first skill you absolutely need to

master is an AI agent developer after

you learn the basics is the use of llm

apis currently the leading players in

the field are open AI anthropic and

Google Gemini in my opinion open AI is

at least a few months ahead of everyone

although they were extremely slow with

releases recently so I definitely

recommend starting with open AI also in

my opinion although the gap between open

stores and commercial models will shrink

over time commercial will always be

ahead feel free to disagree with me in

the comments so explore open a API and

try to create an agent from scratch

don't use any Frameworks or libraries

equip it with tools using only custom

code and try to fine-tune it on a simple

task this will give you a much better

understanding of how e agents work

internally however don't stop on just

commercial model apis sometimes there is

a need to deploy or fine-tune an open

source model because of data privacy I

am also currently working on adding a

new way to use my framework with open

source models and if this proves

successful I will let you know so it's

important to know how to reuse existing

AI models in 2024 but I would not

recommend learning to train your own AI

from scratch as I said in one of my

previous videos AI is just becoming way

too complex secretive and expensive

there is absolutely no way you can train

any breakthrough model by yourself

instead try fine-tuning or deploying

existing models on a platform like

huging phas they offer convenient

deployment options and a convenient way

to fine-tune those models so I've

actually released videos on how to do

both on my channel as well and finally

you'll need to learn function calling

function calling is a feature that

allows llms to call external functions

this is how your agents interact with

the outer world and this is why it's the

most important part of the entire system

in our agency we don't measure results

of our systems by analyzing the outputs

of the messages instead we measure our

results by analyzing the actions that

those agents take I recommend exploring

the instructor Library which is a

project developed by Jason Leo that

conveniently combines penic with

function calls essentially it allows you

to validate all the inputs provided by

your agents before executing any logic

inside those function calls so my

framework depends on instructor for all

custom tools and Json also has a

detailed documentation with many great

examples after that the next step is to

learn multi-agent Frameworks the most

popular ones are currently autogen crew

eii and my own framework agency form

I've previously discussed all of them on

my channel and I also released many

tutorials on how to use my own framework

so I won't go into too much details here

if you prefer another framework there

are also plenty of tutorials on YouTube

essentially all these Frameworks do is

just handle some of the underlying

details for running your agent-based

systems such as multi-agent

communication function execution and

State Management there is an intricate

balance however between between having

control over your systems and being able

to get started fast my framework is

definitely heavily skewed towards

control and customization rather than

the ease of use it provides you with

full access over your agents without any

hardcoded prompts or any hardcoded

agents even like in other Frameworks so

other Frameworks might definitely be

easier to get started with because you

don't have to create all of those Proms

and agents yourself but they might be

too restrictive if you're building

anything more complex than a trip

planner now there is one crucial step

that a lot of people skip which is

actually deploying your agents in

production this is why next you do have

to learn some light backend API

development you can start by deploying a

simple model via API there are many ways

to do this for example you can use

Firebase functions AWS Lambda or even

create your own server with flask or

fast API I recommend using serverless

because the number of requests your

agent receives can vary significantly

from time to time therefore you need a

scalable solution to learn quickly the

best way is to chat with chat GPT it

knows surprisingly well all of those

infrastructure providers like Google

Cloud AWS and Azure Google Cloud also

has recently introduced their own

assistant powered by Gemini which can be

used directly from their console you can

simply ask how to create a serverless

function and it will provide you with an

exact step-by-step process to follow and

if you encounter any issues of course

you can send them back and it will help

you to resolve it once you've deployed

your agent as an API try integrating it

into a channel like slack or Discord or

you could even go farther and try

creating your own chat user interface

using a platform like verel VZ front-end

development could be helpful sometimes

however it's not a requirement once

you've shared your project with the

world the final step is to monetize it

making money is hands down the best way

to learn because you also get paid for

it and to do this effectively you must

productize your service you must reuse

as much of the previous code as possible

you should create templates for each

step of the process like deploying

agents creating tools or even creating

agent templates sometimes that could be

helpful although I do not recommend

using it all the time after that you

need to find your first client there are

three means you can find your first

client number one is freelance platforms

this is the easiest and the fastest way

to find a client without making

significant time and money Investments I

think AI agent development fits quite

well with freelancing because the

projects are relatively shortterm

besides There is almost no competition

for a agent deps on these platforms yet

this is how I personally got started I

used to work as an eii freelancer on upw

work which gave me a ton of experience

because I was working with many

different companies on various projects

across all modalities like audio Vision

NLP and so on my tip for you here is to

create your own project offers and let

your clients find you instead of sending

proposals number two cold Outreach cold

Outreach involves sending code emails

and DMS to companies offering your

service this method requires a lot more

effort but it can also lead to higher

paying clients I don't have much

experience doing cold Outreach but my

tip for you is to always schedule a call

instead of trying to sell your solution

via emails and finally you can find a

full-time job you'd be surprised how

many companies are actually looking for

a agent developers right now and the

best way to find such a company is to

look for startups that recently received

funding our team is also currently on

the lookout for agent developers so if

you have already completed all of the

previous steps you can reach out to us

on our Discord remember the goal is not

just to make money but to learn and grow

as you go and to wrap this up I just

wanted to share a bonus tip with you

which is to build your own community

building a community requires a

substantial time investment however if

you have a community and if you

understand the needs and challenges that

your community is currently facing you

can tailor your services specifically

for them this will not only help you

stand out but will also provide you with

extremely valuable feedback so as I said

I personally started as as a freelancer

on upwork yet about a year ago I decided

to start this YouTube channel I thought

that maybe by this time I would get my

first client from YouTube however

believe it or not my first client found

me on my second video when I had only

about 100 subscribers or so so I

definitely recommend considering

platforms like Twitter Youtube Youtube

shorts medium or even your own open

source project with a dedicated Discord

server it will take some time to grow

your audience but if you stick the

investment will totally be worth it so

yeah that's it for this video guys if

you want to grab this whole road map and

my AI agent scoping template for notion

make sure to use the link below it's

going to ask you for your email because

I'm currently working on a complete

course that will include a lot more

information that I couldn't fit into a

single video here we are actually one of

the very few companies in the world that

productized AI agent development at

scale so we have a lot more to share

however before we do that I want to make

sure that this course is as good as it

can be so it will probably take me a

couple months or so to make it thank you

guys for watching and don't forget to

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