Summarize the following quote:


"in this video we're going to have a

closer look at langtune agents and

understand what agents are all about

first we're going to dive into what an

agent is and understand how agents work

under the hood of link chain then we're

going to have a look at what we can do

with agents that we couldn't do before

and some of the future indications for

businesses that are already investing in

technology data and analytics and

finally I'm going to show you how to get

started building your own custom agents

I'm going to have gbt access data from

an external API and you can use this

example as inspiration for building

other types of Agents

in order to understand what a lang chain

agent is and what we can do with an

agent let's have a look at an example
What is a LangChain Agent? An example use case.

suppose you're building a chat bot for

an e-commerce business this could really

be any type of bc business but

e-commerce is easy to understand

right now businesses around the world

are already using GPT . and gpt to

build chatbots

we know that gbt has great general

knowledge but it's severely lacking when

it comes to your own data gbt does know

anything about your products for

instance

we have seen in earlier videos how we

can solve this using Lang chain and

Vector storage we slice and dice the

product data and put that into a

database like NAME_1 and then

we have the language model access that

data so now the chat model knows about

your products which is better than

before but it's still not good enough

because what does a chatbot really need

to know in order to give the customers a

good customer experience

the chatbot needs to know stuff about

the customer if this chatbot is on a web

page it needs to know the context of the

visit

and this could be information like is

this a new potential customer or an

existing customer or what is the

browsing history of this visitor

what products do we recommend based on

purchase history or browsing history

all this information needs to be made

available to the chatbot at runtime and

this is information that will help a

language model with the customer

interaction and ultimately help the

business convert the customers and

generate more sales

this information can be made available

to the chatbot for the language model

through microservices by letting the

language model access these resources of

computation and information through apis

in Lang chain connectors to apis and

computational reso Provide exactly two different responses that are separated with 6 asterisk symbols: ******.
user