How you should think about AI Agents this 2024. (Early Mover Advantage)

Summary

The video script discusses the transformative impact of AI agents on the business landscape in 2023, emphasizing their role as digital helpers that learn and make smarter decisions. It explains the concept of Lang chain, a framework for developing AI agents using programming languages like Python, and how it integrates with tools and data storage. The script provides an example of a Shopify agent, illustrating how AI can personalize customer experiences by accessing specific data. It also touches on the importance of integrating AI with business communication channels and analytical services for enhanced customer interactions. Finally, it suggests two approaches for implementing AI agents: using the Lang chain framework for those familiar with Python, and utilizing open AI assistance APIs with no-code tools for beginners, highlighting the potential for creating and selling AI solutions to businesses.

Takeaways

• 😲 AI agents are revolutionizing the tech landscape, becoming essential in the business world as digital helpers that learn and make smarter decisions over time.
• 🤖 AI agents are smart algorithms that can perform tasks autonomously, improving efficiency and personalizing customer experiences in various industries.
• 🛠 The Lang chain framework is a popular method for developing AI agents, built on top of programming languages like Python to provide infrastructure for deploying and managing tasks.
• 🔍 Lang chain agents are language models with access to user-specific data and tools, allowing them to perform calculations and interact with external services like Shopify.
• 📚 The script discusses the importance of understanding how AI agents work, especially in the context of 2024, emphasizing the integration of AI with business communication channels.
• 💡 AI agents can enhance customer interactions by accessing both raw and processed customer data, combining the capabilities of language models with analytical services.
• 🛑 The use of retrieval augmented generation (RAG) is mentioned as a method to bridge the gap between general knowledge and specific data, improving chatbot performance.
• 🔗 Microservices integration with APIs and computational resources through Lang chain connectors create agents that can handle complex tasks effectively.
• 📈 Analytical services are evolving to serve as tools for language models, helping to create personalized experiences by accessing customer data and analytics.
• 🛑 Two approaches to implementing AI agents are highlighted: using the Lang chain framework with programming languages, and using open AI assistance APIs with no-code tools for easier implementation.
• 💰 The video concludes by emphasizing the business opportunity in creating and selling AI agents to automate workflows and solve business pain points.

Q & A

What is an AI agent according to the script?

An AI agent is a digital helper that is supercharged with smart algorithms, capable of handling tasks autonomously and learning to make smarter decisions over time.

What is the Lang chain framework and its role in developing AI agents?

Lang chain is a framework built on top of an existing programming language like Python, providing infrastructure for building, deploying, and managing AI agents that can perform a variety of tasks more effectively.

What is the significance of user-specific data in the context of Lang chain agents?

User-specific data is crucial for Lang chain agents as it allows the language model to access and utilize this data through tools and APIs, enabling the agent to perform tasks relevant to the user's context.

How does the script describe the integration of AI agents with business communication channels?

The script suggests that AI agents can be integrated with various business communication channels such as paid ads, social media, web pages, chats, emails, and SMS to enhance the online customer experience through personalized text, images, and videos.

What is the role of microservices in making AI agents more effective?

Microservices, through the integration with APIs and computational resources using Lang chain connectors, create agents that fuse a language model with specific tools or resources, enabling the model to tackle specific tasks effectively.

Why is it important for AI agents to access both raw and processed customer data?

Accessing both raw and processed customer data allows AI agents to create truly personalized experiences, leveraging analytical services like churn scores, segmentation models, product recommendations, and customer journey analytics.

What are the two different approaches to implementing AI agents mentioned in the script?

The two approaches are implementing agents using the Lang chain framework and a programming language like Python, and using open AI assistance API with a no-code tool like SpotPress or VoiceFlow for the front end.

How can businesses benefit from using AI agents in their workflows?

Businesses can benefit by automating specific tasks, solving pain points, and enhancing customer interactions, potentially leading to increased efficiency and sales.

What is the potential opportunity for individuals who understand and implement AI agents as described in the script?

Individuals can create and sell AI agents for specific use cases to businesses, providing solutions to automate parts of their workflows and address specific needs, which can lead to significant financial opportunities.

How does the script suggest one can learn more about using AI agents with no-code tools?

The script suggests following the channel for upcoming content that will teach how to use the open AI assistance API and no-code tools to create agents and automate workflow aspects.

Outlines

00:00

🚀 Introduction to AI Agents and Lang Chain Framework

The script introduces the transformative impact of AI agents in the business world, particularly in 2023. It explains AI agents as digital helpers with the ability to learn and make decisions autonomously. The video aims to educate viewers on how AI agents work, focusing on the Lang chain framework, a tool built on programming languages like Python. Lang chain enables the development, deployment, and management of AI agents that can perform various tasks efficiently. The script delves into the concept of a Lang chain agent, which is a language model with access to user-specific data and tools, like Python libraries or APIs, to enhance its capabilities. An example of a Shopify agent is provided to illustrate how AI agents can be integrated into e-commerce businesses to offer personalized customer experiences and drive sales.

05:01

🛠 Implementing AI Agents for Business Communication

This paragraph discusses the importance of integrating AI agents with business communication channels to enhance customer experiences. It outlines five primary channels—paid media, social media, web pages, chats, emails, and SMS—and emphasizes the need for language models to access both raw and processed customer data to create personalized experiences. The script mentions analytical services as valuable tools for language models, thanks to Lang chain's ability to bridge the gap between analytics and AI. Two approaches to implementing AI agents are highlighted: one using the Lang chain framework and Python for those familiar with coding, and another using open AI assistance APIs combined with no-code tools like SpotPress or VoiceFlow for a simpler, quicker implementation. The video concludes by encouraging viewers to follow the channel for upcoming content on creating and automating AI agents, hinting at the potential for monetization through solving business pain points or automating workflows.

Keywords

💡AI Agent

AI Agents, as discussed in the video, are digital helpers that are enhanced with smart algorithms capable of performing tasks autonomously and learning from experience to make smarter decisions. They are integral to the theme of the video, illustrating the transformative potential of AI in business operations. The script mentions AI agents as tools that can handle various tasks more effectively, becoming game changers in the business world.

💡Lang Chain

Lang Chain is a framework built on top of programming languages like Python, providing infrastructure for developing, deploying, and managing AI agents. It is central to the video's narrative as it enables AI agents to perform a variety of tasks more effectively. The script explains that Lang Chain allows language models to access user-specific data and tools, which is crucial for creating personalized customer experiences.

💡Language Model

A language model in the context of the video refers to a type of AI that is trained to understand and generate human-like text based on the input data. It is a fundamental component of AI agents, as it allows them to interact with users in a natural language. The script uses the term to describe how Lang Chain agents leverage language models to access and utilize data.

💡Vector Storage

Vector storage, as mentioned in the script, is a method of organizing product data into databases, which allows a language model to access and gain insights into products. It is a key concept in the video as it helps bridge the gap between general AI knowledge and specific product information, enabling more personalized customer interactions.

💡Chatbot

A chatbot is an AI-driven tool used for customer interaction, often in e-commerce or service industries. In the video, chatbots are presented as an example of how AI agents can be utilized to enhance customer experience by providing personalized recommendations and assistance. The script discusses the importance of chatbots having access to specific data about products and customers.

💡Microservices

Microservices in the video refer to the integration with APIs and computational resources using Lang Chain connectors, which create agents. These agents are a fusion of a language model with specific tools or resources, enabling the model to handle complex interactions and tasks effectively. The script uses the term to explain how information is made available to chatbots in real-time.

💡Personalized Customer Experiences

Personalized customer experiences are a key focus of the video, highlighting the importance of tailoring interactions to individual customer needs and preferences. The script explains how AI agents, with access to specific customer data, can provide recommendations and assistance that are customized to each visitor's history and context.

💡API

API, or Application Programming Interface, is a set of rules that allows different software applications to communicate with each other. In the video, APIs are used to connect AI agents to external services, such as Shopify, enabling the agents to perform tasks like calculations based on stored data. The script mentions APIs as part of the tools that AI agents can access.

💡Business Communication Channels

Business communication channels are the various ways businesses interact with customers, including paid media, social media, web pages, chats, emails, and SMS. The video discusses how these channels shape the online customer experience and how AI can be integrated into them to enhance text, image, and video content.

💡No-Code Tools

No-code tools, as mentioned in the script, are platforms that allow users to create applications without writing code, such as Spotpress or Voiceflow. The video suggests these tools as an alternative for implementing AI agents, offering a low barrier to entry and simplifying the process of creating and managing chatbots and other AI interactions.

💡AI-Driven Solutions

AI-driven solutions refer to the use of artificial intelligence to solve problems or perform tasks, such as generating images or videos. The video mentions 'mid journey' and 'stable diffusion' as examples of AI solutions that generate images and videos, although it notes that they are not yet fully integrated into business communication channels.

Highlights

AI agents are becoming major game changers in the business world, acting as smart, self-learning digital helpers.

Lang chain is a framework built on top of programming languages like Python to develop, deploy, and manage AI agents.

Lang chain agents are language models with access to user-specific data and tools for performing tasks more effectively.

AI agents can connect to external services like Shopify via APIs to enhance functionality.

Chatbots powered by GPT models lack familiarity with specific business data, requiring integration with databases for personalized customer experiences.

Retrieval Augmented Generation (RAG) was discussed in a previous video as a method to bridge the gap between general knowledge and specific data.

Microservices and L chain connectors allow AI agents to access customer-specific information in real-time for personalized assistance.

Agents fuse language models with specific tools or resources, empowering them to handle complex interactions and tasks.

Business communication channels include paid media, social media, web pages, chats, emails, and SMS, primarily utilizing text, images, and video.

AI-driven solutions like Mid Journey and Stable Diffusion are being explored for generating images and videos in business communication.

Language models need access to raw and processed customer data from analytical services to create personalized experiences.

Lang chain bridges the gap between traditional analytics and the new frontier of AI, enhancing the capabilities of language models.

Two approaches to implementing AI agents are discussed: using the Lang chain framework with Python or utilizing open AI assistance APIs with no-code tools.

No-code tools like VoiceFlow and BotPress offer a simple and quick way to create and manage AI agents without extensive coding knowledge.

Creating AI agents for specific use cases can provide businesses with solutions to pain points and automate workflows, presenting a lucrative opportunity.

The video promises to deliver valuable content on using open AI assistance APIs and no-code tools to create agents and automate workflows in future uploads.

Transcripts

you were able to witness in this last

2023 the way chat GPT changed the way

people worked forever the tech landscape

is changing rapidly and now ai agents

are becoming major game changers in the

business world but what is an AI agent

think of AI agents as your digital

helpers but supercharged they are smart

algorithms that can handle tasks on

their own learning as they go to make

smarter decisions as these agents become

more sophisticated businesses are using

them to make things run smoother

personalized customer experiences and

come up with fresh ideas before we move

on to the rest of the video and explain

the potential opportunities of AI agents

in this 2024 it's very important that

you understand how AI agents work in the

background so the most popular method

people are developing AI agents is by

using Lang chain so Lang chain is

basically a framework that is built on

top of an existing programming language

such as Python and provides an

infrastructure for building deploying

and managing AI agents that can perform

a variety of tasks more effectively so

if we get specific about the Lang chain

framework what a lang chain agent is

it's basically a language model that has

access to user specific data which we

will store in some form of storage but

also it has this access to tools and

tools could be a python library that

allows the language model to perform

calculations based on the data we gave

it which is stored somewhere with that

data that is stored it can also perform

other functions with tools such as an

API which can connect to an external

service such as Shopify so let's get in

a little bit more depth by me going

through an example use case which in

this case will be a Shopify agent so

coming back to what I said before so

you're working on a chatbot for your

e-commerce business right whether you're

in retail services or any other b2c

industry this example will probably

resonate many businesses worldwide are

already tapping into the power of GPT

3.5 in in GPT 4 to build their chat Bots

but here's the catch while these models

have vast general knowledge they lack

familiarity with your specific data so

your chat boo needs to know about your

products your customers and the context

of their interactions to provide a great

customer experience in a previous video

I've discussed using Lang chain and

Vector storage to address this Gap so

previous video I mentioned about the

concept of retrieval augmented

generation or rack so I'll drop a link

to the video if you want to see it by

organizing your product data into

databases like pine cone or chroma and

allowing the language model to access

this data your chatbot gains valuable

insight into your

products but that's just the first step

to truly enhance customer interactions

and drive sales your chatbot also needs

to access customer specific information

so for example is the visitor new or

returning what's what's their browsing

or purchase history so this contextual

data enables your chatbot to provide

personalized recommendations and

tailored assistance so how do we make

this information available to the

chatbot at runtime or in real time well

you

enter the realm of microservices so by

integrating with apis and computational

resources using L chain connectors we

create what we call

agents and agents are the fusion of a

language model with specific tools or

resources enabling the model to tackle

specific tasks effectively just like how

a person uses tools like python or Excel

to solve a problem agents empower the

language model to handle complex

interactions and tasks so hopefully that

was clear if not you can always go back

and rewatch the video at any point and

if that didn't work drop a comment down

below and I'll be happy to answer but

yeah now I want to take a step back back

on to of a more onto a more General

approach and I want you to understand

from another perspective why Lang chain

is such a big game Cher because you

really need to understand this in this

2024 so let's talk about business

communication

channels so when it comes to business

communication there are five main

channels through which businesses

interact with

customers paid in social media web pages

chats emails and SMS

these channels shape the online customer

experience primarily through text images

and video and while businesses are

already leveraging language models like

chat GPT for generating text content

there's more to consider images and

videos are also crucial elements and

companies are exploring AI driven

Solutions like mid journey and stable

diffusion although they are out there

and they do a pretty good job at

generating images and video they're now

to a point where they can be be

integrated within business communication

channels effectively right you can still

tell the difference in a AI generated

image and an AI Genera re for now they

will soon but for now we won't consider

them for this example and so language

models alone don't possess enough

knowledge about customers to create

truly personalized

experiences to deliver exceptional

customer experiences language models

need to access both raw and process

customer data from analytical

services and this includes churn anden

scores segmentation models product

recommendations and customer Journey

analytics and fortunately analytical

Services have been evolving for years

and now they can serve as valuable tools

for language models this integration

Bridges the gap between the traditional

world of analytics and the New Frontier

of AI thanks to Lang chain but now on

top of language models we have

agents so before ending the video I want

to highlight two different approaches to

implementing agents so the first way I

mentioned in this in this video which

would be implementing agents using the

Lang chain framework and language such

as python in your preferred environment

such as visual

studio and this provides a powerful and

and flexible solution but yeah this is

only suitable for people that are

already familiar with python if you

don't fall into that category don't

worry at all because there's a really

good alternative that offers the lowest

barrier to entry that I've ever seen and

this is using openi assistance API as

well as having a little bit of code on

the back end and having a no code tool

such as spot press or voice flow as the

front end this is the simplest and

quickest way to do it because the front

end where all the conversations would

happen between the agent and the user

will be handled by these no code tools

such as voice flow and Bot press instead

of using the first method where you

would have to program the conversational

flows as well as the front end by

yourself and so if you want to learn how

to do it with the second method I really

suggest that you follow my channel

because in the next following weeks I

will be putting out a lot of valuable

content that will teach you how to use

assistants API as well as no code tools

in order to create agents as well as

automating aspects of your workflow

because if you're able to create agents

for specific use cases you will be able

to sell these to businesses to Sol some

of their pain points or automate part of

their workflows and therefore there's a

serious opportunity for you to make a

lot of cash if you are able to

understand this and implement this

that's the end of this video If you

enjoyed the video and you found it

useful give it a thumbs up and if you

didn't let me know why in the comments

below and yeah see you in the next

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