How you should think about AI Agents this 2024. (Early Mover Advantage)
Summary
TLDRThe 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
🚀 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.
🛠 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.
Mindmap
Keywords
💡AI Agent
💡Lang Chain
💡Language Model
💡Vector Storage
💡Chatbot
💡Microservices
💡Personalized Customer Experiences
💡API
💡Business Communication Channels
💡No-Code Tools
💡AI-Driven Solutions
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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