Agentic AI – Complete Course for Beginners

0:00Learn how to build productionready

0:01multi- aent systems and automate

0:04workflows using lang chain and langraph.

0:08You'll master everything from core

0:10agentic fundamentals and pideantic

0:12validation to advanced sequential

0:15parallel and conditional langraph

0:17workflows. Along the way, you'll

0:19implement chat memory, rag, and human in

0:23the loop controls and finish by

0:25deploying your applications to AWS and

0:28render through real world projects like

0:30a custom chat GPT trip planner and auto

0:34content agent. Papy created this course.

0:38Hi guys, my name is BPI and you are

0:41welcome to my course. In this course

0:44you'll try to master the complete

0:46agentic AI with the help of Lang graph.

0:49If you have seen over the internet and

0:51everywhere nowadays people are moving

0:54towards agentic AI system.

0:56Previously we used to work on the LLM

0:59based application rag based application

1:01but right now agent is getting very much

1:03important and crucial for the

1:06application development. Nowadays AI

1:08agents are becoming very much powerful

1:10because of its automated workflows. So

1:13right now you only need to provide a

1:15prompt and from your prompt itself your

1:17agents can understand your goals and

1:19these goals would be divided into

1:21multiple tasks and all of the task would

1:23be completed by using some kinds of

1:25tools. Your AI agents can decide which

1:28tool to use to perform what kinds of

1:30task. So these kinds of automated

1:33workflows your AI agents is having and

1:35with the help of that it can perform any

1:36kinds of task you'll be providing to

1:38your AI agents. So that's why this is

1:40far better than our traditional geni

1:43application development because right

1:45now with the help of agents we can

1:47automated the workflows. So in this

1:49course guys I'm going to teach you each

1:51and everything you need to master the

1:53agentic AI with the help of langraph. Uh

1:55but before that first of all we'll try

1:57to understand my course plan. This

1:59course I have divided into multiple

2:00phases. So as you can see this is my

2:03entire plan for this course uh agentic

2:05using langraph. So first of all uh in

2:08the phase one we'll try to complete the

2:10introduction to uh agentic AI. First of

2:12all we'll try to understand what is

2:15agentic AI. Okay how AI agent works.

2:17We'll try to see the difference between

2:19LLA maps and AI agents application.

2:21We'll try to see the agentic behaviors

2:23like reasoning planning memory tool.

2:25Okay decision making. Then we'll try to

2:27see some real world use case of agentic

2:29AI traditional AI versus generative AI

2:32versus agent AI system. Okay. Then uh

2:34we'll try to see the evaluation from

2:36chat bots to automated agents. Okay.

2:38We'll try to see each and everything.

2:40Then uh some other stuff we'll try to

2:42cover as you can see like limitations uh

2:45why agent why agents need memory tools

2:47workflows control logic then agent

2:50architecture prompt and system

2:51instruction tools memory planning

2:54reflection environment interaction and

2:56human feedback okay then in phase two

2:59I'll try to start with asynchronous

3:00programming and pentic because uh if you

3:04are uh if you are already working with

3:07AI agents I think you know that uh you

3:09need these kinds to asynchronous

3:11programming because all of the agents

3:12are using especially all of the agents

3:15framework are using this kinds of

3:16asynchronous programming in the back

3:17end. Okay. So in this course I'm going

3:19to focus on the langraph. So langraph

3:22internally uses asynchronous programming

3:24that means you can run your agents in

3:26parallel. Okay. We'll try to understand

3:28this asynchronous programming. Okay. Why

3:30it is required for AI agents? How we can

3:32code inside Python. Okay. Then we'll

3:35also try to see about the pyic.

3:38So, pyic is a python library and uh we

3:42use this pentic for the uh data

3:44validation okay model validation uh and

3:47uh these things you need whenever you

3:49are implementing the agents okay I'm

3:51going to tell you why it is required and

3:53why you have to learn this pidentic as

3:55well okay so each and everything we'll

3:57try to cover in the phase two then in

3:59phase three guys I will start with our

4:01first uh orchestration framework which

4:03is langen now you can ask me why we'll

4:06be learning the langin Because if you

4:08know lang graph is a product of langchen

4:10okay langchen team has developed lang

4:12graph right. So that's why to master

4:15this lang lang graph we need some

4:19knowledge on langchen first of all we

4:20have to understand uh whenever we do

4:23didn't have this kinds of langraph

4:24framework so how people used to create

4:26the agents with the help of langchen so

4:28that's why we'll try to use langen to

4:30build this kinds of agents and multi-

4:32aents workflows okay and uh still if you

4:35are using lang graph you need to use

4:36langen because from the langen uh you

4:38will be loading the large language model

4:40you will be loading the prom templates

4:42okay all of the utility related ated

4:43code you will be writing with the help

4:44of langchen and lang graph you'll be

4:47using for building your agent workflows

4:50okay that's why langchen understanding

4:51is little bit required that's why I'm

4:53going to complete this langchen inside

4:54this particular course then we'll start

4:57with the phase four which is uh lang

5:00graph so here we'll try to understand

5:02each and every component of lang graph

5:04as you can see what is lang graph why

5:06lang graph is required langchen versus

5:08lang graph then graph based aent

5:10workflows state management node and ages

5:12okay state nodes edges then conditional

5:14edges. Okay. Start and end nodes, graph

5:17compilation, state graph, checkpointer,

5:19masses state, sequential workflows,

5:21parallel workflows, conditional

5:22workflows, iterative workflows. Okay.

5:25Then um we'll try to see basic chatbot

5:28architecture, masses handling, state

5:29management, user input and we'll try to

5:32see how we can implement agentic chatbot

5:34with the help of langraph. So after that

5:36we'll start with phase five. So here

5:38we'll try to learn about the memory

5:40planning, monitoring and autonomous

5:42system. So here we'll just try to learn

5:45um why what is persistence memory, why

5:47it is required, agent memory, short-term

5:49memory, chat history, how we can stream

5:51the responses, okay, chat trading,

5:54conversation management, permanent chat

5:57persistence memory with database, okay,

5:59tool integr uh integration in Langraph,

6:01okay, inside the uh um aentki

6:04application. Then we'll try to see

6:06different different tools. Okay. Then

6:08we'll also try to learn like how we can

6:10integrate RG that means rag features

6:12inside our AI agents. Then vector

6:14database integration. Okay. Then uh

6:17we'll be learning another important

6:18concept which is human in the loop. Uh

6:20that means HITL. This is required

6:22nowadays all the agentic application are

6:24having this kinds of human in the loop

6:26integration. Then we'll try to see the

6:28monitoring our agent monitoring agent

6:30tracing with the help of Langmith. Then

6:32we'll also see how we can debug um the

6:34agent behavior.

6:36Then uh phase six guys we'll try to

6:38start with the deployment and production

6:40grade engineering. So here we'll try to

6:42dockerize the entire agents. Okay. Then

6:45we'll try to add the fast API back end

6:47database setup GitHub action CI/CD AWS

6:49deployment render deployment. Okay.

6:52Environment variable then production

6:54folder structure logging monitoring each

6:56and everything we'll try to cover. Then

6:58uh the final phase uh phase seven we'll

7:00try to start with some uh end to end

7:02real world AI agents uh project

7:04implementation. So we'll be implementing

7:07basically three major project here in

7:09this course. The first project I'll be

7:10implementing one end to end agentic

7:12chatbot with the help of lang graph

7:14database langismith tools rag htl AWS

7:17and render. And second project we'll be

7:19implementing uh uh our own chat GP agent

7:22with the help of LLM langraph fast API

7:25lang chroma uh SQL alchemy database and

7:28AWS. And third project uh project we'll

7:30be implementing uh called tripmate AI.

7:33This should be end to end multi-agent,

7:35table, planner agent with grock,

7:37langraph, postgrql and fast API. Okay.

7:40So these are the three major project

7:41we'll be implementing in this course.

7:43Then uh you can ask me what would be the

7:45course requirement uh to start this code

7:47course. What are the things I need to

7:49know? I'm expecting you are familiar

7:51with uh advanced Python programming

7:53because all of the coding I'll be doing

7:55I'll be coding in advanced Python. Then

7:57basics of generative AI knowledge is

7:59required if you're understanding about

8:01agent AI agents especially. So you need

8:03some understanding about uh generate EBI

8:05at least about the large language model.

8:07Okay, these are the thing. Then software

8:09requirement wise you should have anagon

8:11installed in your system VS code G and

8:13GitHub and docker desktop and postman.

8:15Okay, so these are the tools if you have

8:16you can start with this course. But

8:18don't worry, I will take care each and

8:19everything. If you are not familiar with

8:21this concept, I'll take care I'll try to

8:23teach in a such a way so that you will

8:25be getting all of the concept in a clear

8:27way. Okay. So yes guys uh this is the

8:29plan entire plan and throughout the

8:30entire course we'll be completing all of

8:32this concept and trust me the way I'm

8:34going to complete all of the concept you

8:36will be loving a lot and after this you

8:38won't be having any kinds of doubt okay

8:40so if you're already familiar with

8:42agenti uh I will still tell you just try

8:44to go through the entire course I think

8:46you will be learning u some new concept

8:48here some some new implementation here

8:50okay so definitely you will be enjoying

8:53the entire course okay so yes guys this

8:55is the entire plan now let's start with

8:57the course concept. First of all, I'll

9:00uh give you the idea about the evolution

9:02of uh agentic AI how agentic AI came

9:05okay from the traditional large language

9:08model then we'll try to start with the

9:09other concept as well. So in this video

9:12first of all we'll try to understand and

9:15see the complete evolution of this

9:17agentic like how agentic came and uh

9:21what we used to do in our traditional

9:24generative application.

9:26uh first of all I will give you the

9:27entire understanding how agentic AI came

9:30what are the things they have introduced

9:32then uh I'm going to discuss about uh

9:35the detailed understanding of agentic AI

9:39uh the characteristic of agentic AI

9:41different component of agentic AI we'll

9:43try to understand with a good example so

9:46guys you can see on my screen here I

9:49have already written the definition like

9:52what is agentic AI so if you see here uh

9:56agent is nothing but uh it's a type of

9:59artificial intelligence that can take up

10:02a task or goal from a user and uh then

10:06work towards completing it on its own

10:10with minimal

10:12uh human guidance. Okay. And it plans,

10:16takes actions, adapts to change and

10:19seeks helps only when necessary. So by

10:23this definition itself I think uh you

10:26are getting little bit of understanding

10:29what I'm trying to say. Um those who are

10:32already familiar with uh chart GPT or

10:36any other uh agentic AI system uh if you

10:40have already used like u um VS code then

10:44anti-gravity cursor AI cloudy desktop

10:47right so this kinds of application if

10:49you have already used so there you will

10:51see that whenever user uh gives any

10:55kinds of uh prompt right based on the

10:58prompt uh that application decides what

11:00to you let's say if you are asking a

11:02very simple questions let's say you are

11:04asking tell me about Python so most of

11:08the large language model um have been

11:11trained with lots of data okay um

11:14especially whatever data we are having

11:17on the internet so they have used those

11:19data and they have trained those are the

11:21model and every model is having a

11:24knowledge cutoff okay every model is

11:26having a knowledge cutoff knowledge

11:27cutff means a specific date uh till they

11:31have trained the model. Let's say if I'm

11:33talking about uh chart GPT or let's say

11:36GPT uh 3.5 tour let's say GPT4 you will

11:40see that those model uh probably they

11:43have trained u uh on the year 2022

11:48or 2023 around okay uh till the date

11:52they have taken all of the data from the

11:54internet and they have trained those are

11:55the model so uh whenever I'm asking

11:58about the python so definitely uh in

12:012020 22 or 2023 this information was

12:04available on the internet and definitely

12:06our large language having this kinds of

12:09knowledge right so it will be able to

12:11give you the answer in short or directly

12:14but whenever I'm asking anything which

12:17is latest say I'm asking um I'm asking a

12:20latest information I'm asking like tell

12:23me about uh like uh the latest news of

12:27Iran and USA okay over in 2026 six. So

12:31that time definitely uh if you are using

12:34a single large language model okay uh

12:37this kinds of large language model won't

12:39be able to give you the response okay it

12:41will tell I don't have enough context

12:43okay uh after 2022 or 2003 so I I can't

12:48um answer your questions okay this kinds

12:51of I think you will uh get the answer if

12:54you have used the older chart GPT I

12:56think you are getting what I'm trying to

12:57say so but if I'm talking about uh

13:01nowadays uh whatever application we are

13:03using like clouded desktop then

13:06anti-gravity cursor id if I'm giving any

13:09kinds of uh prompt let's say I'm telling

13:12um just try to uh implement a

13:15application for me let's say implement a

13:18python game for me so what it it will do

13:20it will try to take that prompt as a

13:23command and it will automatically let's

13:25say plan for a task like what to do okay

13:28how uh it can implement the entire game

13:31for you. So to implement a game first of

13:33all it has to uh create the environment.

13:35It has to uh add the requirements. It

13:38has to uh create the user interface. It

13:41has to make the character. Okay. So one

13:43by one all of the plan would be sorted

13:45then once all the plan is ready. Okay.

13:48It will execute the plan one by one and

13:51it will complete the entire system and

13:53definitely in between it will try to

13:55test that particular let's say

13:57application. Okay. If uh it uh doesn't

14:00get any kinds of bugs, it will continue

14:02and it will complete that particular uh

14:04work for you. Okay. So that means

14:07everything is happening automatically

14:09and sometimes you will see that in

14:10between it will ask a human interaction.

14:13It will ask for a human input. Let's say

14:15whenever it will try to implement a game

14:18that time it might ask you what kinds of

14:21color you want for this particular

14:22environment. How many character you want

14:24in this particular game? what would be

14:26the let's say car color what would be

14:29the car speed okay so sometimes it will

14:32ask some kinds of questions to the human

14:34okay for the guidance and once uh we'll

14:37try to provide the feedback or let's say

14:39our input it will take that input again

14:41it will try to continue the workflow

14:44okay so that's why here you can see it

14:46is telling with minimal human guidance

14:48okay not not complete human guidance we

14:51give like very minimal human guidance

14:53here and it try to uh plans takes action

14:56okay adapt to changes let's say it has

14:59let's say it has to do one particular

15:01changes in the environment or let's say

15:03color or let's say any character it will

15:05automatically do that okay and it seeks

15:08help only when necessary so guys before

15:11I uh give you the entire discussion on

15:14this agentic AI first of all I want to

15:17walk you through the fundamental concept

15:19of generative application like so far

15:21whatever application uh we usually uh

15:25Great. Okay. And how this agentic uh AI

15:28or let's say AI agents came in the

15:29market. Then we'll try to understand uh

15:32this agent concept. So for this guys I'm

15:35going to take you on my whiteboard and

15:37then we'll try to discuss each and

15:38everything. So guys I'm inside my board.

15:41So here I'm going to write down each and

15:43everything.

15:44So see whenever I'm talking about

15:48uh AI agents right

15:52AI

15:55agent

15:57so this is the application of generative

16:00AI

16:04okay this falls into generative AI

16:07domain and uh those who are already

16:10working with generative AI so I am

16:12having a dedicated course on my channel

16:15the complete generative BI course. So

16:16there I have already discussed the

16:18foundation of generative BI. So in that

16:20course I have already taught you um all

16:23the concept regarding generative AI

16:26large language model. Okay uh retrieval

16:28augmented generations. Uh so each and

16:30everything I have already covered there.

16:32So if you are not familiar with

16:33generative AI first of all try to

16:35complete that particular course then it

16:37would be easy for you to understand.

16:39Okay. So in generative AI uh the main

16:42component we usually work with large

16:46language model. Okay large language

16:48model. So there are different different

16:50large language model nowadays. I think

16:52you know um there are some organization

16:55there are some company they have

16:58launched different different models. If

17:00I'm talking about meta okay meta AI so

17:03they have launched something called

17:05llama.

17:09Okay. Llama. Then if I'm talking about

17:14OpenAI, they have launched GPT.

17:18Okay. Then we are having Mistral.

17:25We are having

17:27Gemini.

17:29Okay. This is from Google. So that's how

17:32we are having different different large

17:34language model. Uh nowadays we usually

17:36use. So previously whenever we started

17:40generative BI that time um we used to uh

17:46only use a fine-tune uh fine-tune based

17:49or let's say uh pretend based large lang

17:51based model. Let's say here I'm having a

17:53large lang based model. So we used to

17:57provide a prompt

18:00okay prompt and it used to give a

18:03response.

18:06Okay. Response. So basically we used to

18:09use this large lang based model for text

18:11generation. Okay. For very uh good

18:14quality text generation or uh for some

18:18other task also we used to use like for

18:20language translation.

18:27Okay. Then for text summarization

18:35then definitely for chat operation how

18:39we used to ask different kinds of

18:41question and we used to get the response

18:44then definitely for

18:47like uh some other NLP task like any

18:52and so on. Okay. So initially those who

18:56have already used this chart GPT I think

18:58you are trying to relate the concept

19:00what I'm trying to say it was like a

19:03very basic application okay we used to

19:05use for this kinds of text generation

19:06task but slowly what they did uh they

19:10actually introduced uh also image

19:12generation okay image

19:17generation

19:20so image generation happens whenever

19:22they introduce something called

19:23multimodel system. So in the multimodel

19:26uh you not only generate the text there

19:28you can also generate the image. Okay

19:31but what was the problem with this kinds

19:33of application as I already told you

19:35let's say if I'm talking about any kinds

19:37of large language model it is having a

19:40knowledge cutff okay this is having a

19:45knowledge

19:47cutff

19:50so what is knowledge cutff let's try to

19:52understand. So for this let's go to the

19:54Google and here if I am searching for

19:57any kinds of model. Let's say I'm

19:58searching for open AI models. Let's open

20:02up the models.

20:05Now let's pick any kinds of model from

20:07this openi. So let's say if I'm talking

20:10about the GPT4 uh 5.4 mini or let's see

20:13if I'm taking any older model. Older

20:15model. Yeah. So I think here some models

20:19are available. Let's say if I'm talking

20:21about this um

20:25the view wall.

20:27Let's say if I'm talking about this

20:28GPT4.1. So if I click on this model, you

20:32will see that this model having a

20:34configuration. Configuration means the

20:36context window like uh how much context

20:39it can take then maximum output tokens

20:43how how much token it can generates and

20:46there is a section called knowledge

20:47cutoff. Okay. So here the knowledge

20:50cutoff you can see January 1, 2024 that

20:53means this model uh has been trained uh

20:57till January 1, 2024

21:00uh internet data. Okay. So if you're

21:02asking anything after that let's say

21:04you're asking February 1, 2024

21:07definitely um this model is not going to

21:10give you the response because this model

21:12doesn't have uh the knowledge after uh

21:15January 1, 2024. Okay, whatever let's

21:19say uh recent uh update uh we are having

21:22on the internet this this model doesn't

21:24know about that. So the main problem I

21:27think you can understand let's say if my

21:29prompt

21:30is uh before okay before this particular

21:33knowledge cutff the information I'm

21:35looking for on from my large language

21:37model definitely this model uh can give

21:40you the response but if it is after the

21:42knowledge cutff that time it will not

21:44able to give you the response okay it

21:46will tell I don't have the um context I

21:49don't have the informations okay after

21:51this particular knowledge cutff so I'm

21:52extremely sorry for that so that that is

21:55the uh things actually uh uh happened uh

21:59whenever charg came okay uh initially in

22:02the market and I think you remember okay

22:05uh charg used to give this kinds of

22:07response then uh what uh they have

22:10introduced

22:11they have introduced a concept called

22:13rag okay why they have introduced the

22:16concept called rag because now let's say

22:19if I want to add some other information

22:21let's say this is 2026

22:24so now I have to I want to add some more

22:26informations okay inside my large bank

22:29model. So what I have to do I have to

22:31finetune this model right I have to

22:35fine tune this model and finetuning

22:38means we are taking the pre-ten model

22:42okay and on top of that we are adding

22:44some new data

22:47adding new latest data and we are

22:50training few parameters here okay and

22:52whenever I'm talking about the LLM

22:54parameters it will count like from

22:56million right million to billion

23:00Okay, this is the issue. So fine-tuning

23:02is not an easy task. For this you need a

23:04good resources then um good budget.

23:07Okay, then you you should have also

23:10time. If you're having these kinds of

23:12things then you can easily fine-tune one

23:14large language model. Okay, there is no

23:17issue with that. So for the company this

23:20fine-tuning task was easy because

23:21they're having a good resources. They're

23:23having uh like very uh heavy investment.

23:26Okay, they're having lots of time. So

23:28they can do that. But what about for the

23:30developers? Let's say if I'm creating a

23:32application, okay, for my client and if

23:35any new data is coming and I want my

23:38application to be aware on top of this

23:40new data. So for me for for me as a

23:43developer, this is this is going to be

23:45like very hectic task like for

23:47fine-tuning a model because I don't have

23:49this kinds of supercomput with me. I

23:51don't have this much of budget okay so

23:54that I can purchase a good cloud for the

23:56training. I don't have that much of time

23:58time so that my client will wait for me

24:01because they has to also do the business

24:02right if I'm running my business also

24:04this should be continuously running and

24:06I should have handled all of the client

24:09with the latest informations and

24:11everything okay so that time researcher

24:14introduced something called rag concept

24:17okay this is called retrieval

24:21okay retrieval augmented

24:27generation.

24:30Okay. Reg rack component. In the rack

24:33component uh concept what we used to do

24:36let's say we are having a large language

24:37model. This is completely fine.

24:40Let's say we are having a large language

24:42model.

24:47Okay. So it will be connected to a

24:51knowledge base.

24:54So knowledge base is basically a

24:56database.

24:58Okay, it's a vector database.

25:02So this is called knowledge base. So it

25:04is having all the latest information,

25:11latest data I can say.

25:15Okay. So this data you have to store in

25:18the knowledge base and you have to

25:19connect with your large language model.

25:23Okay. And for this kind uh connection we

25:26use the orchestration framework. Some

25:27orchestration framework uh I think you

25:29know inside generate we are having lang

25:32chain we are having llama index. Okay.

25:33So this is called orchestration

25:35framework. So we use this kinds of

25:36orchestration framework uh to make the

25:39connection with our LLM.

25:41Okay. Now if user is asking anything

25:45okay let's say user is giving uh input.

25:48Okay. First of all, this input would be

25:50verified in the uh pre-ten model that

25:54means the large language model itself.

25:56First of all, it will try to check

25:57whether this information he's asking or

26:01what kinds of uh question they're

26:02asking. It is available in the LLM

26:05itself or not. It is available in this

26:07knowledge cutff or not. If it is having

26:11okay in this knowledge cutff, this will

26:13give you the response directly. This

26:15will give you the response. Okay, this

26:18will give you the response directly.

26:21But what about this information is not

26:23available that time. It will go to the

26:25knowledge base. It will go to the

26:27knowledge base. Okay, it will do

26:28something called semantic search,

26:30similarity search. This will get the

26:32relevant uh result about the questions

26:36user is asking. Then this particular

26:39relevant answer again your large

26:42language model will take it will try to

26:44analyze it will try to um it will try to

26:47clean up it will try to rearrange the uh

26:50response then it will try to send it to

26:52the

26:54user again. Okay that's how the entire R

26:57system works.

26:59Okay system works. So basically the

27:02major component we have added this

27:03knowledge base and adding data in the

27:06knowledge base. It is super easy because

27:08only you just need to uh fetch the

27:11latest informations and add in the

27:13knowledge base and uh your LM is already

27:16connected to the knowledge base. So

27:17anytime if you're asking any kinds of

27:19question it will uh bring that

27:21particular latest informations and uh it

27:23will do the refining operation then it

27:25will pass to the human. Okay. So this

27:27will work like that. Okay. And this was

27:31the like uh very famous technique uh

27:34that time even nowadays also we use the

27:37same technique we we create the rag

27:39application and this actually helps us

27:42uh from this finetuning operation

27:44because here we are not doing the

27:46finetuning okay on our LLM only we're

27:49just working on the knowledge base we

27:50are adding the data in our vector

27:52database this is the things right but

27:55there are some problem with this vector

27:57database or this RG system what is the

27:59problem. Whenever I'm talking about the

28:02real time data, realtime data means the

28:03data is continuously changing. Let's say

28:05if I'm talking about weather

28:06informations, if I'm talking about

28:08temperature, if I'm talking about uh the

28:11latest news, okay, it is continuously

28:13changing. That time it is not possible

28:16for me to sit down whole day and take

28:20all of the latest informations and like

28:22add in my knowledge base. Okay, that

28:24that kinds of things we can't ever do

28:26that. So that that is why this rack

28:30system fails. Let's see if we're asking

28:32questions to the rack system. Let's say

28:34tell me about latest news. Okay, right

28:37now in the morning. So definitely this

28:39information is not available in the

28:40knowledge base. Okay, let's say morning

28:42news you have added but what about the

28:45afternoon news? What about after 1 hour

28:47news? Okay, so these kinds of things you

28:49don't have. Okay, so that time your

28:53application won't be able to give you

28:54the response. So what you have to do

28:56that time you have to

28:59uh you have to think about a different

29:01approach. So that's why researcher

29:03thought why not we can create a agent.

29:06Okay why not we can create a agent. So

29:08that agent will be connected with some

29:11tool. Okay tool means we can use

29:13different different tool here. Uh let's

29:15say uh we can use any kinds of search

29:17tool. We can use any kinds of uh storage

29:20tool. We can use any kinds of calendar

29:22tool, Google drive tool. whatever we can

29:24use but there should be some kinds of

29:26tool. So with the help of that

29:28particular tool my AI agents will try to

29:30fetch the informations and it will give

29:33to the user. Okay. So what they

29:34introduce that time they introduce a

29:38agent system. Let's say this is your

29:41agent.

29:43Okay. Agent uh internally it is using a

29:46large language model only. Okay.

29:47Whenever user is giving any kinds of

29:49input it is connected with some kinds of

29:51tool. Okay. So let's say if I'm asking

29:54for uh any latest informations that time

29:56it is connected with a search tool okay

29:59internet search tool. So mostly this

30:01will search on the Google and Google is

30:03continuously updating okay with latest

30:05informations. So if you're asking any

30:07realtime question first of all what it

30:09will do it will um use this search tool.

30:12It will search over the internet it will

30:14get the informations okay latest

30:16informations and your agent LLM is

30:18trying to refining that and it is giving

30:20you the response again. Okay. And why

30:24I'm calling this particular system as a

30:25agent? Because your application is smart

30:28enough to understand what it needs to

30:31call this call this tool where when it

30:34doesn't need to call this tool. Okay.

30:35This kinds of uh reasoning capacity your

30:38application will be having. Okay. That's

30:40why we call it as a agentic agentic

30:42system. Okay. Here we are not deciding

30:45when to call this particular tool. You

30:47just give the prompt okay to the

30:49application. applicant uh application

30:51will decide whether I has I have to call

30:54this tool to get uh uh give the response

30:57or I have this information with me so I

31:00can give you the response okay so this

31:02kinds of capacity it was having so this

31:05was the first agent they have introduced

31:07with some realtime tool so if I uh take

31:10you to the chart GPT so let me give you

31:13the example so I'll open the chart GPT

31:17and uh here let's say

31:21I'm asking a question. Let's say I'm

31:23asking tell me

31:26about

31:28okay Python.

31:31Now see what will happen.

31:34Uh this is directly giving you the

31:37answer. Okay. It is not referring any

31:39kinds of tool search tools. It is not

31:41searching on the internet. Okay. Instead

31:43of that what it is doing? It is giving

31:45you the direct answer. Okay. because

31:48this information is already available in

31:50the knowledge bed itself. Okay,

31:52knowledge uh knowledge uh LLM knowledge

31:55itself. Okay, because it is already uh

31:58having uh before the knowledge cutoff.

32:01Get it? But whenever I'm searching for

32:04any other question, let's say I'm

32:05telling tell me

32:08the

32:10latest

32:14news

32:19News of

32:21India election.

32:26Now if I search that now see it is

32:29searching for web. Okay it is searching

32:31for web. It is using a internal search

32:34tool and with the help of that it is

32:36searching over the internet and it is

32:39referring some trusted uh let's say

32:42sources like Alajira ABC news. Okay,

32:46that's how it is searching on different

32:48different website. Okay, now if I open

32:50this website, you can see that this is a

32:52website. This is another website. Okay,

32:54and this website has already this kinds

32:56of latest news. It is bringing that

32:59particular informations. It is passing

33:01it to the LLM. LM is trying to refining

33:04LM is trying to summarizing all of these

33:07let's say uh all of this content of

33:10these kinds of sources and this is

33:13refining and giving you the

33:15answer okay refined version of answer

33:18okay so this is called actually um agent

33:22system okay it is utilizing some kinds

33:25of uh tools in the back end and this

33:28application is automatically deciding

33:30when it needs to call that tool tool

33:33when it doesn't need to call that tool.

33:36Okay, not only that, this is a simple

33:38example I have shown if you have already

33:40used uh like uh anti-gravity. Let's say

33:44if I open up my anti-gravity.

33:47So this is my anti-gravity. So here I

33:49can uh give the prompt to the agent. So

33:53let's say here I am telling

33:56um create

33:59a

34:01car racing game using Python.

34:11Okay, Python. Now here you can um select

34:14different different model because

34:16internally I told you agent uses a large

34:18language model. Okay, because this is

34:20the brain. Okay, it is having the

34:22reasoning power and it decides actually

34:25when to use the tool when uh it doesn't

34:27need to use the tool. Okay, so here you

34:30can select different different model. So

34:31anticip supports these are the model you

34:33can select any of them. Now if you give

34:36this prompt you will see that

34:37automatically first of all it will try

34:40to make the plan. Okay, what to do? Now

34:42see it is telling generating. Let's

34:44wait. Now see it is thinking. Okay, it

34:47is thinking. Now it is trying to making

34:50the entire plan for you. Okay. How it is

34:53going to uh create that particular uh

34:56racing game with the help of Python.

34:58What are the resources it need? What are

34:59the tools it needs? It will try to make

35:02the entire plan. See this is the plan.

35:05You can see this is the plan. Okay.

35:06Proposed plan. Now what it will do in

35:09the plan? First of all, it will do the

35:11initialization. Set up the pygram

35:13display front and clock. Then player

35:16card, obstacle, uh collision stone

35:18detection, score system, give over

35:22screen. Okay. Then what are the

35:24requirement? It needs verification plan.

35:26So this is the agent plan guys. That's

35:28how one agent works. First of all, it

35:30has to make a plan and based on the

35:32plan, it will start working on that.

35:36Okay. Now I told you in the definition

35:38itself uh it will seek for help when it

35:43necessary. That means little bit of

35:45human interaction is also needed. Now

35:47this plan is proposed to me. Now I can

35:50review the plan. Okay. I can make some

35:52changes. Okay. So let's say if you want

35:55to change anything. Let's say you don't

35:57need this particular step. You can

35:58change anything. Okay. You can change

36:00anything. You can edit anything. Okay.

36:02Then you can review it. You can like

36:05tell okay this plan is completely fine

36:07for me. You can continue. Now let's say

36:08if I do uh

36:12uh the plan

36:16is fine.

36:18Go ahead.

36:21Okay.

36:22Now if I give the prompt

36:25I think prompt uh you can't see because

36:27this is uh just uh beside my image but I

36:32think you can see okay uh the prompt I

36:34have given. Now see now it has started

36:37working on the plan okay one by one it

36:40will work on all of the plan okay and it

36:43will try to implement the entire game

36:45for you okay so this is called AI agents

36:49nowadays so AI agents is like uh this is

36:53not uh I mean um I mean uh restricted to

36:58the tools only okay now it can automate

37:01the workflow this is called automation

37:03right here I'm not writing the code. See

37:05my agent is writing all of the code and

37:08it is asking for the approved. Okay, if

37:10I show you, if I let's say show you, so

37:14here you can see it is telling do you

37:16want to run this pip install command. So

37:19it is asking for human interaction. Now

37:20if I give the human interaction if I

37:22give if I tell yes do it. If I tell okay

37:26I accept the code now the rest of the

37:29task it will automatically do that for

37:30me. Okay. So this is called AI agents.

37:33Now I think you have understood this

37:35particular definition. Now let me show

37:37you the definition once more time.

37:40So this is the definition guys. Okay. So

37:42here you can see agentic is a type of AI

37:45that can take up a task or goal from a

37:47user. So the here the task and goal I

37:50have given just create a car racing game

37:52with the help of Python. So then what it

37:54will do it will work towards completing

37:56uh this particular task is own with

37:58minimal human guidance. First of all it

38:00will try to plan. Okay. Then it will

38:02take action, adapt the changes. Okay,

38:04let's say whenever it requires any kinds

38:06of changes, it will automatically do

38:07that and seek help when it necessary.

38:09That means it will uh ask for my help.

38:12Okay, if I want to change anything uh so

38:15it will ask for that particular help for

38:17me, it will ask for ask uh for my

38:20feedback. Okay, if I give the feedback,

38:22it will start working on that. Okay. So

38:24I think guys you have understood uh the

38:27entire uh evaluation of this uh agentic

38:31AI how this agentic AI came okay right

38:34now in the market. Now in the next video

38:36guys what I'm going to do I'm going to

38:39uh discuss this agentic AI in detail.

38:42Okay the application working mechanism.

38:46Okay, I'm going to show you one example

38:49like how uh one aentk application works.

38:52Whenever we give any kinds of uh

38:54command, okay, we give any kinds of

38:56prompt. I think you have seen although

38:57in anti-gravity we given a prompt and it

38:59creates the plan. After creating the

39:01plan, what it will do, okay, each and

39:02everything I'm going to give you. I'm

39:04going to uh tell you the characteristic

39:07of this agent. What are the

39:08characteristic it follows? What are the

39:10component it is having? Okay. So with a

39:12good example, we'll try to understand

39:14the entire concept in the next video. So

39:16yeah, this was uh uh this was only the

39:19understanding uh like about this agenti

39:23evaluation like how this aenti came in

39:25the market and whatever traditional

39:27application we used to create in the

39:29geni. Uh nowadays people are uh actually

39:32um uh people are moving to the agentic

39:35protocol. People are moving moving to

39:36the workflow automation instead of

39:38creating the simple uh actually take

39:40generation based application because

39:42right now uh everything can be automated

39:45all the workflow can be automated. Okay.

39:47Uh instead of working on manually uh we

39:50can create a agents and that that agents

39:53will try to complete that particular

39:55task for me. That's how you can also

39:57scale up your business. You can uh you

40:00can actually uh uh create some agents

40:03for your business. So it will run

40:05automatically. It's a customer support

40:07agents you can create okay automatically

40:10uh email center agents you can create.

40:12So that's how you can minimize the uh

40:15employee in your company and you can uh

40:17save your budgets okay but let's say if

40:19you don't have this kinds of agent

40:21system that time what you have to do you

40:23have to hire someone to do that

40:24particular task. Okay, that's why

40:26companies are uh adopting this AI agents

40:29in their uh application development in

40:32their workflow automations. Okay,

40:34they're replacing some low-level

40:36employee uh which uh they feel like okay

40:39I don't need this kinds of employee and

40:40I can do this kinds of work uh automated

40:43way. Okay, people are uh thinking in

40:46that way. Okay, you have to also be

40:48smarter. Now people ask like uh whether

40:51we'll have the job or not. Okay,

40:53definitely you will have this job but

40:56you have to learn these kinds of

40:57technology. If you know these kinds of

41:00technology then tell me who will replace

41:01you. But if you don't know this

41:03technology let's say still you do the

41:05Excel uh uh let's say uh data collection

41:09autom uh data collection let's say uh

41:12strategy. Now tell me I can easily

41:14create a agents and I can do the Excel

41:16data collection.

41:18Okay. I can um easily handle the

41:21customer automation. I don't need

41:23someone to handle my customer. Let's say

41:25whatever customer uh are coming to my

41:28website. Okay, I don't need to like uh

41:30hire someone to sit and reply for that.

41:33So what I will do, I'll just create a

41:34agents. I'll give all of the

41:35informations about my website, all of my

41:38services. My agents will take care

41:40everything. Okay, this is the things uh

41:42nowadays people are moving. Okay, so

41:44yeah, trust me guys, this uh particular

41:46skill is having high demand in the

41:48market. So if you can master this one

41:50definitely you can um you can get lots

41:53of opportunity. Okay and I will try to

41:56complete this agentic in such a way so

41:59that uh after completing it you can

42:01create any kinds of agentic

42:03applications. So in this video I'm going

42:06to discuss about the detailed discussion

42:09about agentic AI. How uh one AI agent

42:12works how one agentic AI application

42:15works. we'll try to understand uh each

42:18and everything with a good example.

42:20Apart from that, I'm going to also

42:22discuss about the key characteristics

42:24and key component of agenti system. So

42:27this is going to be one amazing

42:29discussion guys. Make sure you watch uh

42:32till the end and if you have any kinds

42:34of doubt feel free to comments in the

42:36comment section. So instead of talking

42:39too much guys let's start with our

42:41discussion.

42:42So guys on my screen I think you have

42:44seen the definition of agentic AI. So

42:47this definition is already familiar with

42:49you. Uh in my previous video I have

42:51already given you the walk through. Let

42:53me uh again give you the walkthrough of

42:56the definition. As you can see, agentic

42:59AI is a type of AI that can take up a

43:02task or goal from a user and then work

43:06towards completing it on its own with

43:10minimal human guidance. It plans, takes

43:13action, adapt to changes

43:17and seeks helps when uh necessary. So in

43:21my previous uh video guys, I have given

43:24you the demo of a AI agents application.

43:26And I think I showed you the

43:28anti-gravity example. So there what

43:30happens? Let's say whenever I used to

43:32give a prompt. Uh there I given a prompt

43:34like just uh create a game for me, color

43:37racing game for me with the help of

43:38Python. So what it was doing? It was

43:41creating a complete plan. Okay, I think

43:43you remember it was creating a complete

43:45plan like what to do, what are the

43:47environment it should use, what are the

43:48package it should use. Okay, then uh

43:51what should be the color, what should be

43:53the uh let's say involvement. So it each

43:56and everything it was uh like making the

43:59plan. Okay. After making the plan guys

44:02what uh it started it was looking for my

44:05confirmation whether if everything is

44:07fine or not. So it was looking for a

44:10minimal human interaction. Okay minimal

44:12human guidance. uh so whenever I

44:14approved everything it started uh taking

44:17the actions that means one by one all of

44:19the plan it was starting executing right

44:22and it was uh creating that particular

44:24games for me okay so that's why uh this

44:28definition is uh I think pretty clear

44:30like how one agentic system works but if

44:33I'm talking about a simple chatbot okay

44:35uh uh so in simple chatbot what happens

44:38you just try to do some question answer

44:40okay it will give you the answer with

44:42respect to that But it doesn't have any

44:44kinds of let's say tool integration. It

44:46doesn't have any kinds of let's say

44:49reasoning capacity. So that it can

44:52automatically think like okay now I have

44:54to use the tool and now I don't have to

44:57use the tool. Okay but in aenti

44:59application it has the cap capabilities

45:04for selecting uh any kinds of tools it's

45:07required. Okay. Let's say you are uh you

45:10are uh doing some automatic coding or

45:12let's say you are creating a game right

45:14that time what kinds of tools it is

45:15required it will automatically call that

45:17tool and it will start creating that

45:19particular application for you. So let

45:22me give you one example guys uh how this

45:24agentic system works.

45:27So as you can see guys uh this is the

45:29example I have taken. So let's say uh

45:32this is our agentic uh agentic system.

45:34Okay, this is a aentic AI application.

45:37So let's say DSP with BPI uh wants to

45:41hire some backend engineer or let's say

45:44some other kinds of engineer. So what I

45:46have done I have created this agent

45:48system okay for my platform. Now uh

45:52let's say if I'm not using this kinds of

45:54platform so what I have to do maybe I

45:56have to hire someone okay so he will try

45:59to or she will try to prepare everything

46:03okay for this particular job role that

46:05means the job description then once job

46:07description is ready uh he or she will

46:10be posting over different different job

46:12platform then uh they will continuously

46:15monitoring that how many applications

46:17are coming once uh application are

46:20getting submitted Again they will try to

46:22review that if application is coming

46:24very less again they will try to update

46:26that particular job description with a

46:28different job role again try to upload

46:30that okay that's how they will be

46:33continuously monitoring and once

46:34application got submitted we'll try to

46:36review that and once reviewed everything

46:38is fine okay let's say we got some

46:40amazing candidate we'll start uhuling

46:43the interview we'll take the interview

46:44after uh taking the interview what I

46:46have to do I have to uh I have to

46:50actually

46:51prepare a offer letter for him. Then

46:54we'll be sending the offer letter and

46:56once offer letter is approved then we'll

46:58try to u u do the onboarding operation.

47:01So this is a like very long and

47:03time-taking process and here I have to

47:05definitely uh pay for that particular

47:08work right uh let's say the person I'm

47:10hiring for this one so definitely I have

47:12to pay for uh that right but let's say I

47:14don't want to pay because nowadays

47:16people are using agenti system so what

47:18I'm going to do let's say I have created

47:20this agent for me so what this agent

47:23does so this agent is already connected

47:25with my platform DS with BP so it is

47:27having all the data okay about my uh

47:30about my let's say platform and I have

47:33already told uh this particular agents

47:36like what kinds of candidate I want what

47:38kinds of job requirement they are having

47:40what is the salary okay each and

47:42everything I have given uh uh okay uh uh

47:44to this particular agent now here what

47:47I'm going to do I'm going to simply give

47:48a prompt I want to hire a backend

47:50engineer and uh they should have two to

47:54four years of experience okay let's say

47:56this is my prompt so first of all I

47:58think you remember what a agent will do,

48:01right? A agent will first of all try to

48:03make a plan. Okay, a agent will try to

48:06make a plan. But how it is going to make

48:08the plan? First of all, the command the

48:11prompt you are giving this command and

48:13prompt would be taken as a goal. So as

48:15you can see uh my agent goal is hire a

48:18remote backend engineer. Okay. Uh uh

48:21their experience should be two to four

48:22years of experience and this is the plan

48:25actually it has automatically created.

48:27Now just try to see the plan. Okay. The

48:29way actually a manual human will do

48:32that. It has done the same thing. Okay,

48:34but with a revised version. Now you can

48:37see it is giving me a plan. First of

48:40all, it will try to make a draft job

48:42description and post on best platform.

48:45Okay, let's say LinkedIn it can post.

48:48No, it can post. Okay, then some other

48:53job uh platforms are also available.

48:55Okay. So there it will try to post that

48:57particular job description. So once

49:00posted it will continuously monitor the

49:02pipeline. Okay. Monitor the pipeline

49:04means let's say I have posted a job

49:06description. It it uh it doesn't mean

49:08that I will just try to disappear right

49:11automatically application will come.

49:13It's it's not like that. You have to

49:14continuously monitor that particular

49:16applica job description like how many

49:19applications are coming uh what are the

49:21candidates are applying for? Is there

49:23any issue or not? Right? we have to

49:25continuously monitor that particular

49:26pipeline. So what we are going to do

49:28guys, we'll be continuously monitoring

49:32that pipeline. So agent is also telling

49:34will monitor the pipeline and adjust

49:36strategy if needed. Okay, so it will

49:39automatically adjust. Okay, this

49:41particular strategy if needed. Let's say

49:43you are getting very less application.

49:45Let's say your expectation is let's say

49:4850 application but you are receiving

49:50four to five application that time

49:51definitely this is not uh meets your

49:54expectation right definitely there

49:56should be some problem with the job

49:57description that's why candidate are not

49:59preferring that uh uh preferring your

50:01job description so what agent will do

50:04maybe agent will try to change the job

50:06description let's say instead of backend

50:08engineer maybe uh it will tell like

50:10fully stack engineer or let's say fully

50:13stack AI engineer okay or let's say web

50:15developer. These kinds of uh job ro

50:18again it will try to set and again it

50:20will prepare the job description. Again

50:21it will post on the platform. Okay. Then

50:24again it will continuously monitor that

50:26particular pipeline. Then let's say now

50:29this particular pipeline is working

50:31fine. Uh so people are applying for that

50:34particular job role. We are getting lots

50:35of candidate. So from the all of the

50:37candidate guys we'll try to filter out

50:39like what would be the best fit for this

50:42job. for this particular job role we'll

50:44try to select that particular candidate.

50:46So agent will try to select that

50:47candidate and maybe let's say it will

50:49take two to three candidate and it will

50:51schedule interviews for them. Right? So

50:53once interviews is scheduled then uh it

50:56will uh we'll be taking the interview

50:59then after that uh let's say we selected

51:02a candidate agent will try to draft the

51:06offer letter. So offer letter would be

51:08created and it will be sending to the

51:10candidate. Once candidate is approved

51:12then uh it will start the onboarding

51:15process. Okay. So this is the entire

51:17plan it has proposed. Okay. It has the

51:19entire plan it is proposed. Okay. Now

51:22after preparing this particular plan I

51:24think you remember it will first of all

51:28okay it will first of all ask me should

51:31I continue with that? So definitely you

51:33have to give a permission. Yes continue.

51:35Okay. Then what it will do? It will

51:37first of all see what was the first

51:39plan. First one is drafting the job job

51:42description. Okay. So it will tell now I

51:44will first start with the drafting the

51:46job description taking help from the

51:48company documents like let's say I have

51:50already given my platform access okay DS

51:53with buppy platform access. So it is

51:55having all the documents all the data

51:57okay what are the things I am having the

51:59requirement it will try to take all of

52:00the data and it will try to prepare a

52:02job description for that and now it is

52:05telling do you want me to make some

52:06changes I'll try to review the entire

52:08let's say job description if completely

52:11fine with me so I'll just try to tell no

52:13this is absolutely fine you can continue

52:15with that now let's say my agent has

52:17prepared one job description for the

52:19backend engineer let's say we are

52:20looking for a remote backend engineer

52:22with two to four years of experience in

52:24backend development ment blah blah blah.

52:26Okay. So once this particular job

52:28description is ready, now the second

52:31plant was posting the job description in

52:33a different platform. Okay. Now it will

52:35tell all right shall I go ahead and post

52:37this job description on the following

52:39platforms like LinkedIn, no etc. Uh so I

52:43will try to review again and again I'll

52:44tell the yes you can do that. Then what

52:46it will do? It will try to post that

52:48particular job description.

52:51uh uh then uh it will tell I will

52:53continuously monitor the application and

52:54keep you posted and for uh posting this

52:57particular job description on a

52:58different platform we have to connect

53:00with the API of the platform okay so

53:03this particular API we can call it as a

53:05tool okay so LinkedIn having a tool no

53:09is having a tool okay that's how there

53:11are uh thousands of like u uh I mean job

53:16posting platform they're having the tool

53:19so you just need to give the tool access

53:20to the agent. So agent will try to

53:22decide when to call what kinds of tool.

53:25Okay, maybe you can't see now I think it

53:27is visible. So just right hand side you

53:29can see uh it is calling the API okay as

53:32a tool and it is trying to access over

53:35the LinkedIn and no and it is posting

53:37that particular job description in that

53:39particular platform. Okay so that's how

53:42this kinds of agent works. Now what

53:44should be the next plan? Let me show you

53:47what should be the next plan. Next plan

53:49would be revising the job description.

53:52Okay, let's say it was continuously

53:54monitoring. Okay, it was continuously

53:56monitoring the pipeline. Then it just

53:59received uh you can see the job posting

54:01uh posting has received only two

54:03applications so far much below our

54:05expectation. Let's say my expectation

54:06was 20 application but I'm getting only

54:09two applications. So definitely there

54:11would be some problem with my job

54:13description. So my agent has suggested

54:15me some kinds of feedback. Okay, you can

54:18see it is suggested some action. So it

54:20is telling uh broaden job description to

54:23include fully stack that uh that means

54:26instead of giving the backend engineer

54:28maybe we can make it to fully stack

54:30engineer because fully stack engineer uh

54:33might have demand in the market and

54:35people are looking for this kinds of job

54:38and promote job on LinkedIn or let's say

54:41no okay so what it is trying to say it

54:44is trying to say like why not we can

54:46promote that particular

54:48job on the LinkedIn

54:50by doing the advertisement. Okay, maybe

54:53by doing the advertisement uh it will go

54:55to that particular candidate and he or

54:58she might be interested and they can

54:59apply. Now it is telling shall I proceed

55:01with that? So if I'm fine with this

55:03particular suggestion I'll do yes

55:06please. Okay, you just continue. So this

55:08is called actually

55:10what I think you can see the definition.

55:13Let me show you see adapt to changes.

55:16Okay, adapt to changes and seeks helps

55:18when necessary. Okay, my agent itself is

55:21trying to planning, taking action and it

55:24is doing the changes if it's required

55:26and it is also seeking the helps when it

55:28necessary. It is trying to wait for my

55:30confirmation and it is doing all of the

55:33work for me. Okay. Now what it will do

55:35again it will try to uh change that

55:37particular job description. Revised

55:39version of job description would be

55:40posted and also promotion marketing

55:43would be activated. Then again it will

55:45start monitoring the entire process.

55:47Okay. So here now

55:51um what it will do guys it will try to

55:53continuously monitor. Now let's say uh

55:56here we are getting our expectation

56:00right now let's say u revive job

56:03description posted promotion activated

56:05and it is continuously monitoring. Now

56:07let's say eight application received.

56:09Okay this is uh completely fine. Uh so

56:11what I can do uh I can screen them using

56:14our checklist strong candidate partial

56:18matches and weak matches. Okay that

56:19means it will try to divide all of the

56:21candidate in three category. The first

56:23category would be strong candidate let's

56:25say from eight application two are

56:26strong three are partial matches and

56:29three weak matches. Okay. So what it

56:32will do it will try to only select the

56:34strong candidate. Okay. with respect to

56:36my um let's say u company's requirement.

56:41Now it is telling shall I schedule the

56:42interviews with the top two. So if

56:45everything is goes fine I'll tell okay

56:46you can continue with that. Okay. So

56:49what it will do it will try to uh

56:51schedule the interview. But beforeuling

56:54what it will tell it will first of all

56:57try to check my availability. So for the

56:59availability what I can do maybe I can

57:01give my calendar access to my agent.

57:03Okay, I think you know that we can

57:05integrate any kinds of tool. Okay, any

57:07kinds of applications to the agents

57:09nowadays. You can connect uh connect

57:11your Slack, you can connect your

57:13calendar, you can connect your Google

57:14drive, anything you can give the access

57:17even if you have used already uh clouded

57:20desktop you will see that clouded

57:22desktop you can also provide your

57:23computer access entire computer access

57:26okay and you can control your entire

57:28computer this is also possible right so

57:30what I will do I will [clears throat]

57:31give my calendar access so my agent will

57:34try to check my availability

57:36okay now it is telling let's say sure

57:38let me check your availability for this

57:39week you are free on Friday. Let's say

57:41I'm free on Friday. Uh do you want me to

57:44schedule the interview on Friday? So

57:46here I will tell yes go ahead. Okay. I

57:50don't have any kinds of issue. I'm

57:52completely free on Friday. Now what it

57:54will do? It will try to draft an uh

57:56invitation email for the candidate.

57:59Let's say this is the inter uh email it

58:01has prepared. Hi candidate we would like

58:03to schedule a 45 minutes of interview

58:06for the back end role. Please share your

58:08availability. Okay. So this email would

58:11be sended. Now sixth step it will try to

58:14do the interview process. Okay. So fifth

58:16step it was doing theuling. Then fourth

58:18step it was doing the short listing.

58:19Okay. And third step I think you know it

58:21is doing the re revision of the job

58:23description. Step by step it is running

58:25the plan. Okay. Not randomly. Now once

58:28let's say candidate

58:31accepted the invitation uh of this

58:34interviewing. So what will happen? It

58:36will try to remind me. Uh so it will

58:39tell quick reminder you have two

58:41interviews lined up for Friday. So I'll

58:44tell okay uh thanks for reminding. Now

58:46it will tell I have mailed you a doc

58:49documents containing a list of interview

58:51question asked in previous interview for

58:53the same role. Now it is not only

58:55schedule the interview for me. It is al

58:58also preparing the interview questions

59:00okay for that particular job role and it

59:03is giving to me. Okay. So I'll tell okay

59:06I'll check that now let's say these are

59:08my interview question it has prepared

59:10okay now what I'm going to do I'm going

59:12to take the interview of the candidate

59:15okay manually I'm going to take the

59:17interview for the candidate let's say 30

59:18to 35 minutes I'll take the interview I

59:21will ask all of the question my agent

59:22has suggested and if uh he or she is

59:25completely fine with that questions he

59:27is he is able to give me all of the

59:29answer so definitely I'm going to select

59:31okay one of the candidate now let's say

59:33I told I have uh finalized one

59:36candidate. Can you draft an offer

59:37letter? My agent will tell sure here is

59:39the offer letter. Please review. I will

59:41tell okay yes uh it works. Then offer

59:43letter would be sended and cracking the

59:46acceptance. Okay. That means this let's

59:47this is my offer letter. Okay. My agent

59:49has prepared. It will send to the

59:51candidate. Okay. Let's say we are

59:52pleased to offer you the position of

59:53backend engineer. Please let us know. Um

59:56let uh please let us know if you accept.

59:58Okay. So once the candidate has accepted

1:00:01now what it will do it will do the

1:00:03onboarding process. Okay, after sending

1:00:04the offer later it will do the

1:00:05onboarding process. So here candidate

1:00:07has accepted the offer. I have initiated

1:00:09the onboarding. Welcome email sent. It

1:00:12access requested submitted. Laptop has

1:00:14been pro uh pro uh pro provisioned.

1:00:16Okay. Shall I schedule a introduction

1:00:18meeting with him? I'll tell yes. So the

1:00:20introduction meeting would be scheduled.

1:00:22Okay. So that's how guys a agent system

1:00:24works with a very minimal human

1:00:27interaction here. So you just only give

1:00:30to uh you just only need to give a task.

1:00:33You just only need to give a prompt.

1:00:35Let's say I want that or you have to do

1:00:36that particular work. It will

1:00:38automatically

1:00:40make the plan, take the actions and it

1:00:43will ask for the help when it necessary.

1:00:45Okay. So this is not possible with a

1:00:48simple chatbot or the simple RGB based

1:00:51application whatever we used to create

1:00:53previously. This is only possible in the

1:00:56agentic AI system. Okay. And this is

1:00:57called actually AI agents and every AI

1:01:00agents works in that way. Okay. First of

1:01:02all, it will try to make a plan and step

1:01:04by step all of the plan would be

1:01:06executed. This is called take actions.

1:01:09Then adapt the changes. That means

1:01:11whenever it necessary, it will do the

1:01:12changes.

1:01:14And whenever let's say uh it feels like

1:01:17okay, it needs to ask to the human for

1:01:19the confirmation, it will do that

1:01:20because I can't give full access to my

1:01:22agents to do everything because

1:01:24definitely there should be some uh

1:01:26manual human observation. Okay,

1:01:28otherwise uh some other things might be

1:01:30happen, right? That's why some minimal

1:01:32human interaction is required. If you

1:01:34take any kinds of agenti application

1:01:37whether it's clouded desktop, whether

1:01:39it's uh your anti-gravity

1:01:42cursor AI, it works in that way. Okay, I

1:01:45think I showed you the example of

1:01:47anti-gravity. Uh there I was doing the

1:01:50automatic coding, right? I was

1:01:51implementing a game. So there I gave the

1:01:53prompt. It was taking that particular

1:01:55prompt as a goal. After that, it was

1:01:57creating the plan. Okay? uh in the plan

1:02:00itself step by step all of the things it

1:02:02has suggested me then I approved

1:02:05everything it was creating step by step

1:02:07it was also executing in between if any

1:02:10changes required it was doing that and

1:02:12it is asking for my confirmation and

1:02:14once I confirm it is doing each and

1:02:16everything for me okay so I think now

1:02:19the agentic system is clear what this

1:02:21aentic system is how it works okay now

1:02:25we'll try to understand the key

1:02:26characteristic of a aentki application.

1:02:29So for this let's go to the next uh

1:02:32actually diagram. As you can see these

1:02:35are some key characteristic of AI uh AI

1:02:38agents or agenti applications. So the

1:02:40first characteristic you can see it

1:02:42should be autonomous. So definitely the

1:02:45example I have showed you this is

1:02:46completely autonomous agent. So there I

1:02:48already told I u I need to hire a

1:02:51backend engineer with two to four years

1:02:52of experience. So what it started it was

1:02:55started creating the plan taking the

1:02:58actions okay everything was auto auto

1:03:01automatically doing there right it was

1:03:03posting the job description it was

1:03:05continuously monitoring that it was u

1:03:08asking the help u uh to me if I confirm

1:03:13that it will again reconte the work so

1:03:15it was completely autonomous okay then

1:03:18it should be goal oriented so definitely

1:03:21the task you are giving it should be

1:03:24taking that particular task as a goal.

1:03:26Okay. Without goal, how it will achieve

1:03:28that particular work, right? So in in

1:03:30our life also whenever we get any kinds

1:03:32of work, whenever we get any kinds of

1:03:34task, definitely we have to take it as a

1:03:37goal. Okay. If we take it as a goal,

1:03:39then we can complete that particular

1:03:41goal by planning something, right? We'll

1:03:44do the different different planning.

1:03:46We'll execute those those plan and we'll

1:03:48try to achieve that particular goal.

1:03:49Okay? That's why the next

1:03:50characteristics the planning. So to

1:03:52achieve this goal we have to make the

1:03:54plan. That means for this particular

1:03:56example you saw that it was creating the

1:03:58plan like first of all job description

1:04:00would be created posting in the

1:04:01different platform continuously

1:04:02monitoring after that uh it will change

1:04:05if it is required then uhuling the

1:04:08interview

1:04:10sending the offer letter it was complete

1:04:12plan right then reasoning. So this is

1:04:15the most important characteristic of a

1:04:17AI agent the reasoning and here your LLM

1:04:20comes right because LLM is the only

1:04:23brain your agentic AI system is having

1:04:26with the help of this particular LLM it

1:04:28performs the reasoning operation and it

1:04:30automatically decides uh whether it

1:04:33needs to call any kinds of tool or not

1:04:35because your agents will have connected

1:04:37with different different tools right

1:04:39different different application sources

1:04:41and it will automatically decide when it

1:04:44needs to call what kinds of tool let's

1:04:45say whenever it was posting the job

1:04:48description it needs to call the

1:04:49LinkedIn or no API right this is the

1:04:53tool definitely it will not call the

1:04:55calendar API right so it is

1:04:58automatically thinking in that way this

1:05:01is called reasoning right then

1:05:02adaptability okay adaptability

1:05:06it should have it it it will

1:05:07automatically decide uh when to change

1:05:10something okay what should be the

1:05:12suggestion for that Okay. Then the

1:05:15context awareness. Context awareness

1:05:16means it should remember the previous

1:05:19context. Let's say I given um let's say

1:05:23I want to hire a backend engineer. Let's

1:05:25say today I have given this particular

1:05:26prompt and for some reason uh I just I I

1:05:30went out. Okay. Let's say for 2 days I

1:05:33went out. Then again I came to my agents

1:05:36and I told just tell me the progress

1:05:37about the back end engineer. Now it is

1:05:39if it doesn't have the context awareness

1:05:42that means the memory integration that

1:05:44time your agent will tell what kinds of

1:05:46back end engineering you are uh telling

1:05:49me right uh what is the task you are

1:05:51telling me I don't know about that but

1:05:52if it is having the context awareness

1:05:54that means the memory it can tell okay

1:05:57so this is the progress I have already

1:05:58posted the job description continuously

1:06:01monitoring and let's say 8 to nine

1:06:03application I got so far so this is

1:06:05called context awareness and every aentk

1:06:07application should have this particular

1:06:09context awareness. Okay. So guys, now

1:06:12let's try to see the detailed discussion

1:06:14of each and uh every characteristic. Uh

1:06:16so here I have already listed down each

1:06:19and everything. So first of all, let's

1:06:21try to understand this autonomy. Okay,

1:06:24this autonomy means the autonomous the

1:06:26first characteristic. So as you can see

1:06:28autonomy refers uh to the AI systems

1:06:32ability to make decisions and take

1:06:35actions on its own to achieve a given

1:06:38goal without needing step-by-step human

1:06:41interaction. That means if you have

1:06:44already seen the example of our AI

1:06:45recruiter so there it was kinds of

1:06:49autonomous okay it was uh it was

1:06:52autonomous agent and it it doesn't need

1:06:55any kinds of stepby-step human

1:06:57interaction so there I was not giving

1:06:59step-by-step human interaction I was not

1:07:01giving step-by-step prompt what to do

1:07:03right so the things is that I have only

1:07:05given my uh requirement my goal so it

1:07:10took that particular prompt as a goal

1:07:12It was creating the plan. It was

1:07:14executing step by step. That's why you

1:07:17can see it is uh proactive. That means

1:07:20continuously it is working on that

1:07:22particular goal. Then autonomy in

1:07:24multiple facts like execution. It was

1:07:26executing the plan step by step. It was

1:07:29doing the decision making. Okay. Uh by

1:07:31the decision itself it was uh thinking

1:07:34like what to do when it needs to post it

1:07:36on different different platform when it

1:07:38needs to make the changes. Then tool

1:07:40uses. Okay. Let's say when to use what

1:07:42kinds of tool let's say whenever I want

1:07:44to post the job description what kinds

1:07:46of tool I need to call definitely to

1:07:48call the LinkedIn

1:07:51and no

1:07:53right so this kinds of ability my

1:07:56autonomous agents will be having that's

1:07:57why the first characteristic the

1:07:59autonomy we can also call it as a

1:08:01autonomous right now the uh thing is

1:08:05that autonomy can be controlled

1:08:07permission scopes that means you can

1:08:09limit

1:08:10what uh what tools or actions the agents

1:08:14can perform independent uh independently

1:08:16can screen candidate but needs approval

1:08:19before rejecting anyone okay that means

1:08:21it's not like that I am given the full

1:08:23autonomous permission to my agents so

1:08:26definitely you can set the limit you can

1:08:28set the permission so let's say once uh

1:08:30one interviewer

1:08:33uh having the screening round uh so

1:08:35before approval or rejecting so

1:08:37definitely I I have to see that manually

1:08:41then I will approve that then my agents

1:08:42will do that for me. Then human in loop

1:08:45we can also call it as a HITL. So insert

1:08:48checkpoints where human input is

1:08:51required before continuing. That means

1:08:54in this case let's say my agent was

1:08:55telling can I post this particular job

1:08:57description or not. Okay, this is called

1:08:59human in loop and every agents are

1:09:02having this kinds of functionality.

1:09:03Okay, going forward we'll be

1:09:05implementing the agents right with

1:09:07different different uh framework. So

1:09:10there also you are having this kinds of

1:09:12functionality with the help of that you

1:09:13can um you can actually create this

1:09:16kinds of system whenever uh your agent

1:09:18is uh needed your human approval it will

1:09:21ask for that then you can continue and

1:09:23it will start working on that. Then

1:09:25override controls allow users to stop,

1:09:28pause or change the agents behavior at

1:09:31any time. Pause screening command to

1:09:34halt

1:09:35resume process. Okay. So what happens?

1:09:38Let's say in my agents, okay, whatever

1:09:41I'm doing, it's completely fine. But it

1:09:43should have

1:09:46uh it should have my control. Okay, my

1:09:49control means let's say I can stop this

1:09:51particular agents anytime. I can pause

1:09:53anytime. Let's say my um screening route

1:09:57is going on in between if I feel like

1:09:58okay I have to stop the screening route

1:10:00I if I give the command it should stop

1:10:02that okay it should stop the process

1:10:05that time okay so this is called

1:10:07actually override controls then

1:10:09guardrails and policies so definitely

1:10:12your agent should have guardrails and

1:10:13policies nowadays you will see that

1:10:16agent integrates different different

1:10:18guardrails okay and for this one

1:10:20framework came in the market called

1:10:21guardrails AI with the help On top of

1:10:23that you can define hard rules or

1:10:25ethical boundaries to the agent must

1:10:27follow. That means let's say if I give

1:10:29you one example it will tell never

1:10:31schedule

1:10:33interview on weekends. Let's say

1:10:35weekends I'm completely occupied. I'm

1:10:37not available. So I can give the

1:10:39restriction. I can give the rules. Never

1:10:42schedule interviews on weekends. So my

1:10:44agent will never do that. And if you see

1:10:47nowadays all the agenti application is

1:10:49having this kinds of guidels and

1:10:50policies. Let's say if you're asking any

1:10:52kinds of violating content, if you're

1:10:55asking any kinds of sexual content, if

1:10:56you're asking any kinds of let's say

1:10:59adult content, definitely it will not

1:11:00give you the response with respect to

1:11:02that because it has a guardrails and

1:11:04policies restriction in the agent

1:11:06itself. Okay. And for this we are having

1:11:09some library, we having some framework

1:11:12definitely will also see in our playlist

1:11:14itself. Okay. Then uh autonomy can be

1:11:18dangerous. The application autonomously

1:11:22send uh sends out job offers with

1:11:24incorrect salaries or terms. So if you

1:11:27completely make it automated, so what

1:11:28we'll do uh there is a possibility your

1:11:31agents will send a job uh job letter

1:11:34with incorrect salaries. Let's say your

1:11:36budget is one lakh but your agent is

1:11:38sending the expected salary 10 lakhs,

1:11:41right? So there definitely this is this

1:11:43is going to be an issue. So that time

1:11:45the applications shortlisted candidate

1:11:47by age or nationality violating anti-

1:11:51discrimination laws. Okay, this is

1:11:52another uh terms then the application uh

1:11:55spending extra

1:11:57on LinkedIn ads. Let's say sometimes

1:11:59what will happen if it is running

1:12:00autonomously although you are getting a

1:12:03good uh let's say application uh from

1:12:07your job description but still your

1:12:08agent will feel like okay I need more

1:12:10and it is spending more money on

1:12:12LinkedIn ads. Okay. So these kinds of

1:12:14things definitely you have to uh

1:12:16overcome. Okay. By uh actually doing the

1:12:20human in loop hit okay functionality

1:12:24inside your agent. Now the next uh

1:12:27things we are having which is uh this

1:12:31goal oriented.

1:12:33Okay goal oriented. Now you can see what

1:12:35is goal oriented. Being a goal oriented

1:12:37means that the AI system operates with a

1:12:40persistent objective in mind and

1:12:43continuously directs its actions to

1:12:46achieve that object rather than just

1:12:48responding to the isolated prompts.

1:12:51Okay, that means if you read here you'll

1:12:54see that

1:12:56goal acts as a com compass for a

1:12:58autonomy. Okay. So to make your agent

1:13:03autonomous definitely there should be a

1:13:05goal. In this case our goal was hire a

1:13:07backend engineer. Okay. This was the

1:13:09goal and will act as a compass for the

1:13:12autonomy. That means your autonomous

1:13:14agent should follow this particular goal

1:13:16and to achieve this goal it needs to

1:13:18execute different different plan. Okay.

1:13:20Now goal can be goal can comes with

1:13:22constraint. Definitely you can set some

1:13:24constraint. In this case, let's say my

1:13:26constraint was four to two to four years

1:13:28of experience candidate I only want.

1:13:30Okay. Then goals are stored in the core

1:13:32memory. That means goal should be stored

1:13:34in the core memory. That means in the

1:13:36context awareness memory otherwise your

1:13:39agent will forget that. What to do?

1:13:40Right? Let's say if I give you a basic

1:13:44actually template the common template

1:13:46for all the agents it stores the data in

1:13:48the memory. So this is a JSON format.

1:13:50Let's say you can store any kinds of

1:13:53database here. Uh internally agent uses

1:13:56a specific database or storage services

1:13:59and it stores this kinds of meta data so

1:14:02that it can remember. So in this case

1:14:04let's say the goal was hire a backend

1:14:06engineer. The constraint was 2 to four

1:14:09years of experience. Remote is true.

1:14:11Stack Python Django cloud. Okay. These

1:14:14are the skills I'm looking for. Start is

1:14:16active. My agent is always active. when

1:14:19I posted the job description. This

1:14:22particular date would be also saved.

1:14:23Progress job uh description is created.

1:14:26True. If not created, it should be

1:14:28false. Okay. Posted on different

1:14:31platform like LinkedIn and angel list or

1:14:34no. Yeah, it has posted. And this is the

1:14:36list. How many application received so

1:14:39far? Only eight applications. Interview

1:14:42schedules with two applications. Okay.

1:14:44So this is the storing uh I mean

1:14:47strategy for every agents. So let's say

1:14:50each and every framework is having

1:14:51different way they stores the data but

1:14:54this is the common if I tell uh one

1:14:57common let's say structure. So this is

1:14:59the common structure that's how the goal

1:15:01should be stored in the core memory.

1:15:03Okay. All the uh running instance

1:15:06metadata would be saved in the core

1:15:07memory. That's how it usually saves and

1:15:10it will take that particular data from

1:15:12the memory itself to continue the plan

1:15:15to continue the goal. Okay, that's how

1:15:17one agent remembers each and everything.

1:15:19Now I think you are getting now goals

1:15:22can be altered as well. Let's say once

1:15:25you have set one goal, you can also

1:15:26change that particular goal to another

1:15:28one. This is also possible here. Okay.

1:15:31Now the next characteristic it is the

1:15:33planning.

1:15:35Okay, planning. So you can see planning

1:15:37is is the agent's ability to break down

1:15:39a highle goal into structured sequence

1:15:42of actions or sub goals and decide the

1:15:45best path to achieve the desired

1:15:47outcome. Okay, that means let's say I

1:15:50have given a prompt I have to hire a

1:15:52backend engineer. Now to achieve this

1:15:54particular goal, what uh this agent has

1:15:57to do? Agent has to create a

1:16:00different different Okay, agent has to

1:16:02create a different different uh plan. I

1:16:05think you saw that what was the plan. So

1:16:07for that particular example, it was uh

1:16:10preparing the job description, posting

1:16:13on different different platform. Okay,

1:16:15then it was uh continuously monitoring

1:16:17that this was the plan. Now whenever it

1:16:20is planning, you'll see that generating

1:16:23multiple candidate plans. It will only

1:16:25not let's say proposed one plan. It

1:16:27might give you multiple plan. Okay,

1:16:29let's say plan A post a job description

1:16:32on LinkedIn, GitHub, jobs or angel list.

1:16:37Okay, plan B is that use internal

1:16:39referrals. Okay, and hiring agencies.

1:16:43So, it has suggested you two plans. Now,

1:16:46it is asking for your feedback. Okay,

1:16:50now based on your choice, you can select

1:16:53which plan you want to go ahead with. So

1:16:56whenever your agent

1:16:59uh agents are working it has the

1:17:01reasoning capacity that means uh it

1:17:03should also minimize my cost right and

1:17:06if it is suggesting me to plan so

1:17:10definitely if I'm taking the plan A so

1:17:12there I will I will let's say spend less

1:17:16less amount because here I directly post

1:17:18on LinkedIn GitHub jobs and handle list

1:17:20okay but if I'm taking the plan B I if

1:17:22I'm taking some hiring agency so

1:17:24definitely I have to take their

1:17:25subscription plan right so there I have

1:17:27to pay more money so get it so that's

1:17:29why it is giving you different different

1:17:32plan okay then evaluate each plan as I

1:17:36told you agents will itself evaluate the

1:17:38plan let's say plan A is perfect or plan

1:17:41B is perfect for this particular work

1:17:43based on that it will give you the

1:17:45suggestion then the efficiency which is

1:17:47faster so if I'm going with plan A or

1:17:49plan B which is faster so definitely

1:17:51plan um uh A is little bit faster

1:17:54because I can directly

1:17:56post the jobs on the platform itself and

1:17:59get the applications. Okay. Then which

1:18:01one is having less cost? Definitely plan

1:18:03A is having less cost. Then risk will it

1:18:07fail if we get no applications? Okay.

1:18:09Then alignment with constraints remote

1:18:11jobs only budget. Okay. So these are my

1:18:15uh steps inside the plan. So based on

1:18:18this particular evaluation steps it will

1:18:21select the plan. Okay. Now third step

1:18:23select the best plan with the help of

1:18:26human in loop. That means it will

1:18:28definitely ask to the human for the best

1:18:29plan which one you would like to go

1:18:31ahead. So which of these option do you

1:18:33prefer a pre-programming policy uh favor

1:18:37low cost channel first? Okay. So

1:18:39definitely whenever I will take any

1:18:41kinds of plan I'll try to check this

1:18:43particular low cost

1:18:45okay uh option in my mind. So yes uh

1:18:48this was the u I mean characteristic

1:18:52which is planning. Now the next

1:18:54characteristic the reasoning. Now let's

1:18:56try to understand this reasoning guys.

1:18:59So what is reasoning? First of all let's

1:19:01try to understand. As you can see

1:19:02reasoning is the uh cognitive process

1:19:05through which an agentic system

1:19:07interprets informations draws conclusion

1:19:10and makes decisions both while planning

1:19:13ahead and while executing the actions in

1:19:15real time. Reasoning uh during planning

1:19:18goals decompositions break down abstract

1:19:21goals into cons uh concrete steps tool

1:19:25selection decide which tool will be

1:19:27needed uh for which step resource uh

1:19:30estimation estimated time dependencies

1:19:33and risk. Okay, as I already told you,

1:19:35whenever a aentic system is working,

1:19:38uh it will be working with respect to

1:19:41the reasoning because behind the

1:19:43reasoning one LLM works, right? And LLM

1:19:45has the u actually intelligence um

1:19:48capacity so that it can understand uh

1:19:51and it can automatically make the

1:19:53decision. It can automatically let's say

1:19:56break down the goals. Okay, it can

1:19:58automatically tell you what kinds of

1:19:59tools should be selected. You can also

1:20:02uh like work on the resources

1:20:04estimations like estimated time,

1:20:05dependencies and risk. Okay. So with the

1:20:08help of this reasoning guys, these

1:20:10agents would be more powerful, more

1:20:13efficient. Why? Because I told you if

1:20:17one agent is one agent doesn't have the

1:20:20reasoning capacity that means this

1:20:22should be the simple chatbot only. Okay,

1:20:24this should be the simple chatbot only.

1:20:26Why a chatbot is different from a AI

1:20:29agent? Okay. Why AI agent is powerful?

1:20:32Because of this reasoning. Okay. Let's

1:20:35say if I'm having a agent, if I'm giving

1:20:37any kinds of input and it is connected

1:20:40with different different tools, right?

1:20:42Some kinds of tools it is connected.

1:20:43Now, whenever I'm giving a input, now

1:20:46this will perform this reasoning

1:20:48operation. It will decide the question

1:20:50human is asking whether I have to

1:20:52directly give the answer. I have to

1:20:54refer the tool for that. Okay. So, this

1:20:56is called actually reasoning. So,

1:20:58reasoning is super important here. Okay.

1:21:01Now here you can see reasoning,

1:21:03duration, execution, decision making,

1:21:05choosing between option uh three

1:21:08candidate matches, schedule uh for two

1:21:10uh two uh best candidate and reject one.

1:21:14So this kinds of decision making is

1:21:15taking your uh agent okay with the help

1:21:18of this reasoning with the help of this

1:21:20LLM. Now, HITL handling

1:21:24knowing when to pause and ask for help.

1:21:27Uh unsure about the salary range. Okay,

1:21:29for an example, let's say with the help

1:21:31of this reasoning, it is automatically

1:21:33decide when it should ask for the help

1:21:36to the human when it needs to ask for

1:21:38the confirmation from the human. Okay,

1:21:40in this case, let's say uh my uh my

1:21:43recruiter agent is asking what should be

1:21:46the salary range just try to tell me.

1:21:48Then error handling, interpreting tools,

1:21:50API failure and re re uh re uh

1:21:54recovering. Okay, let's say sometimes

1:21:56some of the tool might be down right

1:21:58that time uh how this particular agent

1:22:01will handle that particular error. Let's

1:22:03say if my tool to tool A is not working

1:22:06so definitely it will try to move to

1:22:08tool B. Okay. So this kinds of ability

1:22:11this reasoning will be having. Now the

1:22:14next one adaptability. So what is

1:22:16adaptability? You can see adaptability

1:22:18is the agent's ability to modify its

1:22:21plan, strategies or action responses to

1:22:23unexpected conditions all while staying

1:22:27aligned with the goal. Okay. So that

1:22:29means I told you let's say in that

1:22:31particular case I think you remember

1:22:34uh we got very less application right

1:22:36and my agent was automatically give you

1:22:39the suggestion and it was it was telling

1:22:41like I need to change the job

1:22:43description to backend engineering to

1:22:45full stack engineer. So this is called

1:22:47adaptability. Your agent is

1:22:48automatically modifying the plans,

1:22:50modifying the strategy. Then again it is

1:22:52working with respect to that. Okay. You

1:22:54can see failures in uh then external

1:22:57feedbacks and changing the goals.

1:23:00Okay. Now what is the next next uh

1:23:05characteristic is the context awareness.

1:23:07I already told you what is context

1:23:09awareness. Context awareness is the

1:23:10agent's ability to understand, retain

1:23:13and utilize relevant information from uh

1:23:16from the ongoing task, past interaction,

1:23:19user preferences and environmental

1:23:22uh cues to make better decisions through

1:23:24the multiple steps. Okay. And if I'm

1:23:27talking about context awareness, this is

1:23:29nothing but it's a memory. Okay. You can

1:23:31see context awareness is implemented

1:23:34through memory. Here we create two kinds

1:23:36of memory. one is short memory, one is

1:23:38long me long-term memory, short-term

1:23:40memory and long-term memory. So as you

1:23:41can see

1:23:43uh if my uh agent doesn't have the

1:23:47context of my previous let's say uh

1:23:50goals or plan so then how it will

1:23:52progress the current one. So definitely

1:23:55to run the current progress it should

1:23:58have the previous context right. So

1:24:00let's say in this case I have given I

1:24:02want to hire a backend engineer with two

1:24:03to four years of experience. So it

1:24:05should have these kinds of informations

1:24:07in the context memory based on that it

1:24:09will make the decision. Okay, this is

1:24:10what actually it is explaining and what

1:24:13are the tool responses it is getting

1:24:15definitely it will also try to uh store

1:24:18that particular responses to the context

1:24:20memory so that it can give you the

1:24:22better response with respect to that.

1:24:24Now at the end this context awareness

1:24:26implemented through memory. So here we

1:24:28create short-term memory and long-term

1:24:29memory. Short-term memory we create for

1:24:31the current state. Let's say if one

1:24:33current state is running if it is

1:24:34creating some metadata we will be

1:24:36storing in the short-term memory and

1:24:37long-term memory means the whole

1:24:39conversation the whole uh user

1:24:41interaction it is doing it should have

1:24:43the u u present in the long-term memory.

1:24:46Okay. So this is the idea. So yes guys

1:24:48these are some characteristic uh we

1:24:51usually have inside any kinds of AI

1:24:53agents and how you will understand this

1:24:55particular application is a AI agents

1:24:57application or authentic application. If

1:24:59this application is having this kinds of

1:25:01characteristic definitely you can uh

1:25:04think about this is a aenti application.

1:25:06Okay. Definitely in the architecture

1:25:07itself these kinds of character uh

1:25:10characteristic should be mentioned. So

1:25:12guys uh now we'll try to see the

1:25:14components of agenti application what

1:25:17are the component one AI agent is having

1:25:20uh using uh you can see the first

1:25:21component is the brain. Okay whenever

1:25:23I'm talking about the brain so here LLM

1:25:27would be utilized. You can use any kinds

1:25:29of LLM here. So LLM has the reasoning

1:25:32capacity with the help of that it will

1:25:34make the decisions. It will try to do

1:25:37the HITL operation. It will do the uh

1:25:41tool selections. Okay. All the

1:25:42operations be happening with respect to

1:25:43this particular brain. And the second is

1:25:46the orchestrator. So what is

1:25:47orchestrator? To build a entire aenti

1:25:50system, okay, we need some orchestrator.

1:25:54Orchestrator means this is the

1:25:56framework. Okay, framework let's say it

1:25:58will do the connection with the LLM that

1:26:00means brain it will make the connection

1:26:02with tool then whenever it is uh

1:26:05required for the tool calling it will

1:26:07perform the tool calling operation

1:26:08whenever it is required let's say it

1:26:10will uh call the uh h ITL that means

1:26:14human in loop right it will ask for the

1:26:16uh approval from the human so these

1:26:18kinds of things if I want to create a

1:26:20complete aent application I need to use

1:26:22some framework I can't create with the

1:26:25help of simple python or I can't with

1:26:27the help of simple tensorflow right or

1:26:29langen this is not possible so I have to

1:26:31use some end to-end framework for that

1:26:33okay so in the market there are some

1:26:35famous framework like we are having crew

1:26:37AI

1:26:39okay we are having langraph

1:26:44we are having autogen

1:26:47some some no code platform is also

1:26:49available like n okay so a apart from

1:26:52that some other like framework are also

1:26:54available but these are framework

1:26:56actually very uh common and very popular

1:26:59and very powerful in the market nowadays

1:27:01and people use that developer use that

1:27:03to create agenti applications. Okay. So

1:27:05throughout the entire um this uh course

1:27:08guys we'll try to master these are the

1:27:10framework we'll see the entire crew AI

1:27:12framework we'll try to develop a AI

1:27:14agent with help of crew AI okay end to

1:27:16end multi- aent system we'll try to

1:27:17create then lang graph we'll try to

1:27:19explore we'll try to create end to end

1:27:20application with lang graph we'll try to

1:27:22see the autogen we'll try to understand

1:27:24the entire autogen autogen component

1:27:26we'll create the AI agents with autogen

1:27:28we'll try to see some no code platform

1:27:30as well like n we'll see how we can uh

1:27:34create the AI agents without writing any

1:27:36kinds of code by doing some drag and

1:27:38drop operation each and everything we'll

1:27:40be learning. Okay. So that's why

1:27:42orchestrator is also required and this

1:27:43is one other component of a AI agent and

1:27:46orchestrator means I AI agent building

1:27:49framework. Okay. Always remember langen

1:27:52can be also utilized with the help of

1:27:54lang also you can create some of the

1:27:56simple agents but uh later on

1:27:58[clears throat] langen published

1:27:59actually langraph so people are moving

1:28:01to the langraph for building these kinds

1:28:03of AI agents. Okay. Now [clears throat]

1:28:05the next one is the tool. So definitely

1:28:09tool should be available as a component.

1:28:11Here we can utilize any kinds of tool

1:28:13whether it's any kinds of search tool,

1:28:15whether it is any kinds of calendar tool

1:28:18or any kinds of application tool. Okay.

1:28:20If you just open the internet and if you

1:28:22search like agentic AI tools list, okay,

1:28:25you'll see that there are thousands of

1:28:26tool listers available. Okay, whether it

1:28:28is search, whether it is uh any kinds of

1:28:31application with your Google drive, with

1:28:33your calendar. So, let me show you some

1:28:35of the list.

1:28:38So guys, as you can see there is a

1:28:39GitHub called AINE tools catalog and

1:28:42there are some other website as well you

1:28:44will be getting over the internet. So if

1:28:45you open it uh open this up. So this uh

1:28:49this is having all kinds of uh tool uh

1:28:51tools and toolkit as you can see. Uh so

1:28:53we are having archives. So tools to read

1:28:56archive papers. So I think you know if

1:28:58we read any kinds of research paper we

1:29:00go to the archive website. Okay

1:29:02archive.org. So this is also a tool. Uh

1:29:05this tool is available in the framework

1:29:07itself. You can use this tool for

1:29:09reading any kinds of research paper. You

1:29:11can connect your AI aens with the

1:29:12research paper uh actually world. Okay.

1:29:15Then uh by search is there. Bing search

1:29:17is there. B search is there. These are

1:29:19my search tool. Okay. Then code doc

1:29:22search is there. CSV search is there.

1:29:23That's how you can see Google search is

1:29:25there. We are having different different

1:29:27tool. Okay. So this is for the code

1:29:29interpreter. Okay. These are the code

1:29:31interpreter. If you want to interpret

1:29:32any kinds of code, these tools you can

1:29:34use. If you want to do a productivity,

1:29:35these tools you can use. Okay. You can

1:29:37connect with your calendar, email, zoom.

1:29:39Okay. This is for regular automation.

1:29:42Now this is for web browsing. If you

1:29:43want to browse the web, these tool you

1:29:45can use. If you want to connect with the

1:29:46database, these are the database tools

1:29:48are available. If you want to do the

1:29:49file operation, these are the tools are

1:29:51available. Okay. So that's how whatever

1:29:53tool you want everything is available

1:29:56okay in the orchestrator framework in

1:29:57the AI agents you can connect with

1:29:59anyone okay

1:30:02now next we are having the memory

1:30:06memory is another component so I told

1:30:09you context uh awareness is required so

1:30:12here we use something called memory okay

1:30:15so memory should be also integrated and

1:30:17for memory development uh we use

1:30:20orchestrator framework in the

1:30:21orchestrator framework Mark itself we

1:30:22are having different different memory

1:30:23function either you can also use

1:30:25different different database for the

1:30:26memory you can do anything here then the

1:30:29supervisor so this is the

1:30:32hittl that means human in loop okay

1:30:36human in the loop so with the help of

1:30:38the supervisor what it does it try uh it

1:30:41try to interact with the human when any

1:30:44helps is uh required it will try to uh

1:30:46ask for the help it will ask for the

1:30:48human guidance or feedback and it will

1:30:51continue agents. Okay. So these six

1:30:53actually components uh sorry five

1:30:56components are available in any kinds of

1:30:58AI agents and this is the highle

1:31:00actually diagram I have shown you. Some

1:31:02other component might be available as a

1:31:05lowle but this is the main one. Okay. So

1:31:07if you find these are the components are

1:31:09available inside AI agents. uh yeah you

1:31:12can I mean select that particular

1:31:15application as aka application and

1:31:17whenever you are creating you have to um

1:31:20take care these are the component okay

1:31:22inside your application now uh let me

1:31:27show you the component explanation as I

1:31:30already told you the brain wise here

1:31:32what we do we use a large language model

1:31:34and what is the use of large language

1:31:36model the goal interpretation planning

1:31:38reasoning tool selections okay

1:31:40everything is uh everything we do with

1:31:43the help of this particular brain or

1:31:44large language model. The orchestrator

1:31:46orchestrator is a framework

1:31:49uh AI agents implementation framework

1:31:50with the help of uh we can do task

1:31:52sequencing conditional routing, ret

1:31:55logic, looping, iterations and

1:31:57delegations. Okay. Then tools we are

1:31:59having so tools with the help of tools

1:32:01we can uh connect our AI agents with

1:32:03external sources. Okay. uh and to

1:32:05knowledge base as well. That's if you're

1:32:06creating a ragbased agents that time our

1:32:09uh knowledge base should be external

1:32:10sources. Okay, this is this would be

1:32:12perform as a tool that time. Now the

1:32:14next component I told you the memory. So

1:32:16what memory does? So memory actually

1:32:19basically uh stores all of the context

1:32:22of the conversation and uh we create

1:32:25actually two kinds of memory short-term

1:32:27memory and long-term memory and it will

1:32:29uh do the state uh tracking. Okay.

1:32:33um research tracking is required. I

1:32:34already told you I already showed you

1:32:36previously, right? Uh it was uh storing

1:32:38the data in a JSON format. So this is

1:32:40also required. Then the supervisor so

1:32:43approval uh requested for HITL that

1:32:46means human in the loop. Then guardrails

1:32:48uh enforcement then age case uh

1:32:51escalation. Okay. So let's say if you

1:32:53want to block some unsafe or non uh

1:32:57complaint behavior, you can use the

1:32:58guardrails evaluation. I will also show

1:33:00you how to perform the guard's

1:33:02evaluation and uh age uh case uh

1:33:05escalation that means alert human when

1:33:07uncertainity conflict arises. Okay. And

1:33:10you already understood about this uh

1:33:12htil okay human in loop uh human in the

1:33:15loop format. So yes guys these are some

1:33:17components are available of AI agents

1:33:20and whenever we are developing our own

1:33:22AI agents we have to take care these are

1:33:24the part and every AI agents are having

1:33:26this kinds of component uh nowadays.

1:33:29Okay. So apart from that I think uh uh

1:33:32there is nothing uh inside a if you feel

1:33:34like okay if there is any new things you

1:33:36can just do let me know in the comment

1:33:38section definitely I'll try to cover

1:33:39that as well. Okay but so far my

1:33:42understanding I think these are some uh

1:33:44we have to follow whenever we are

1:33:46creating or whenever we are working on

1:33:48aentki system. So yes guys uh this is

1:33:50all about from this video. Uh I hope you

1:33:53have understood and this was helpful for

1:33:55you. I think I already told you uh in my

1:33:58previous uh video uh like there are uh

1:34:01some components are available especially

1:34:04whenever I'm talking about uh the

1:34:06agentic AI uh one of the component is

1:34:09the orchestrator right orchestrator

1:34:11means there we use some kinds of

1:34:14framework okay with the help of these

1:34:16are the framework we create we implement

1:34:18the AI agents whether it's a single

1:34:20agents whether it's a multi multiAI uh

1:34:23agent system uh we try to create these

1:34:26kinds of things right

1:34:28so there is a concept guys uh you have

1:34:32to understand before I start with this

1:34:34kinds of orchestrator framework the

1:34:36concept name is asynchronous programming

1:34:39okay so why this asynchronous

1:34:41programming is required uh because if

1:34:43you see u going forward we'll be

1:34:46creating the multi- aent system and to

1:34:49create the multi- aent system guys we'll

1:34:51be using this kinds of orchestrator

1:34:53framework and internally This

1:34:55orchestrator framework uses asynchronous

1:34:57programming. Okay, that means it will

1:34:59run your agents in parallel. Let's say

1:35:02you have created uh 20 agents. So what

1:35:06it will do instead of running u agents

1:35:09as a sequentially, it will run all of

1:35:12the agents in parallel so that your

1:35:14execution would be more fast. So this

1:35:16concept I'm going to discuss in detail

1:35:18guys. No need to worry. Uh first of all,

1:35:21let me show you one thing. actually uh I

1:35:23have just figured out

1:35:25let's say if I go to the Google so here

1:35:28if I search like is lang graph uses

1:35:31asynchronous in the back end for multi-

1:35:33aents so I think you know lang graph is

1:35:35one of the orchestrator framework with

1:35:37the help of lang graph we create agentic

1:35:40AI applications right and we'll be also

1:35:43mastering this langraph inside our codes

1:35:45so as you can see the response was yes

1:35:47lang graph is uh designed with first

1:35:50class asynchronous supports in the back

1:35:52end for multi- aent system that means uh

1:35:56it is utilizing the asynchronous

1:35:58programming asynchronous concept in the

1:35:59back end okay now if I just go below

1:36:03let's say I have asked for about the

1:36:05autogen because autogen also will be

1:36:07covering inside our course so as you can

1:36:09see autogen also utilizes asynchronous

1:36:12programming at it core but it is uh

1:36:16architecture fundamentally different

1:36:17from the langraph state uh langraph's

1:36:20state machine approach approach but

1:36:22internally it is uses asynchronous

1:36:24programming concept. Now again I asked

1:36:26like uh what about crew AI? Okay so you

1:36:30can see crewi also supports asynchronous

1:36:32execution but it approaches

1:36:34orchestration differently than langraph

1:36:36and autogen while uh langraph uses uh a

1:36:40state machine and autogen uses a actor

1:36:43model. Crew AI is built around

1:36:46role-based collaboration and

1:36:47processdriven execution model. Okay. But

1:36:51the main fun is that all of the

1:36:53orchestrator framework we are using for

1:36:55developing these kinds of multi- aents

1:36:57application internally it is uses

1:37:00asynchronous okay as synchronous

1:37:02programming. Now before I start with

1:37:05these are the orchestrator framework

1:37:06first of all I want to clarify what is

1:37:08asynchronous programming why it is

1:37:10required how asynchronous works okay

1:37:12what is parallelism what is uh let's say

1:37:16sequential execution each and everything

1:37:17I'm going to clarify then we'll start

1:37:20with the uh orchestrator framework

1:37:22understanding but after that there is

1:37:24one more topic uh we have to cover which

1:37:27is nothing but pentic pyntic validation

1:37:30this is also important because you will

1:37:31see that Whatever large language model

1:37:33we are having it will generate the

1:37:35unstructured output and to make it a

1:37:37structured even whenever we are giving

1:37:40any kinds of prompt to make our prompt

1:37:43uh more structured we use this pentic

1:37:45validation okay so this pentic

1:37:47validation will be understanding in the

1:37:49next video but in this video I will only

1:37:50focus on the asynchronous understanding

1:37:54okay so here I'm going to give you the

1:37:56detailed understanding of asynchronous

1:37:59with a theoretical understanding as well

1:38:01as the practical understanding as well.

1:38:03So let me show you guys what is this

1:38:05asynchronous. After that your

1:38:07understanding would be more clear and

1:38:10you can easily understand these are the

1:38:11framework whenever you will be doing the

1:38:13coding.

1:38:15So let me give you the definition of the

1:38:17asynchronous programming. So here is the

1:38:19definition.

1:38:20Um here is a simple definition you can

1:38:23see asynchronous programming in Python.

1:38:26Okay, you can see asynchronous

1:38:27programming in Python is a programming

1:38:30paradigm that allows code to handle okay

1:38:33multiple task uh concurrently without

1:38:36blocking the program's execution. It is

1:38:39primary used to IO bound task example

1:38:42network uh request file input and output

1:38:46operation database queries. Okay,

1:38:48allowing the program to perform other

1:38:51operations while waiting for slow

1:38:54external events to complete. That means

1:38:56this as asynchronous programming will

1:38:59help you. Okay, asynchronous programming

1:39:01will help you. Okay, asynchronous

1:39:03programming will help you to run your

1:39:05task in parallel. Okay, so you are not

1:39:08supposed to wait for the uh execution.

1:39:11Let's say we know that we use synchron

1:39:13synchronous programming so far. Yes or

1:39:15no guys, we use synchronous programming

1:39:18so far. In synchronous programming, what

1:39:20happens? Let's say if I execute a code

1:39:22block, first of all, it will complete

1:39:24that. Okay, after the execution is

1:39:28complete, okay, then it will execute the

1:39:30second part of that particular code.

1:39:32Okay, but in between, let's say if you

1:39:34are waiting for the execution, okay, if

1:39:36you're using asynchronous programming,

1:39:38it can run another task in parallel.

1:39:41Okay, so these are the like say

1:39:43functionality we'll be getting here. Now

1:39:45you can ask me why this is required.

1:39:47Okay, why this asynchronous programming

1:39:49is required inside AI agents

1:39:50implementation. Agentic AI, you can

1:39:53create two kinds of agent. One is the

1:39:54simple agent. Okay. One is the simple

1:39:58agent. So basically simple agents you

1:40:00create

1:40:02only one block. Okay. It can only handle

1:40:05one particular task. Okay. But whenever

1:40:10let's say you have complex problem you

1:40:12have to divide the task in smaller

1:40:13chunks. And what you will do? You will

1:40:15be creating multiple agents. Okay.

1:40:18Multiple agents you will be creating.

1:40:19Let's say this is agent one. This is

1:40:21agent two. This is agent three. Okay.

1:40:24And what will happen? You will assign

1:40:26the task for all the agents. And these

1:40:29agents will be executing either

1:40:32independently,

1:40:34independently

1:40:38or dependently.

1:40:43Okay. Dependently.

1:40:45So it's your design philosophy. If you

1:40:47are making it as a independently that

1:40:49time it will run as an independently. If

1:40:51you are making it to dependent okay it

1:40:53will run as a dependently. So basically

1:40:56what happens let's say if you run these

1:40:58are the agents okay these are the agents

1:41:01in a synchronous programming okay in a

1:41:03synchronous programming what will happen

1:41:05first of all these agents need to be

1:41:07completed these agents execution need to

1:41:09be completed once this agent uh when

1:41:12this agents will be executed then it

1:41:14will move to the next agents okay next

1:41:17code block then this agents will be

1:41:19executed okay once it is completed then

1:41:21it will go to this particular agents

1:41:23okay then it will try to run this

1:41:25particular agent. So let's say this

1:41:27agents is taking 3 minute to run. Sorry,

1:41:30let's say here I can just tell you

1:41:33let's say this agency is taking 1 minute

1:41:34to run. This agency is also taking let's

1:41:37say 1.5 minute to run. Okay. This agency

1:41:40is also taking let's say 1 minute to

1:41:41run. So what is happening? You have to

1:41:43wait for 1 minute. Again you have to

1:41:45wait for 1.5 minute. Again you have to

1:41:46wait for 1 minute. Okay. So if you like

1:41:50uh add all of the time you will see that

1:41:52at the end you are having okay 3.5

1:41:55minutes 3.5 minutes you have to wait for

1:41:58the execution but if you run this task

1:42:00in parallel okay if you run this task in

1:42:02parallel let's say this particular

1:42:04agents will do the online search

1:42:05operation so basically if you're doing

1:42:07the online search operation you'll be

1:42:09hitting some URL like right URL and to

1:42:11get the response this will take some

1:42:13time let's say sometimes this web server

1:42:16might be slow that time it will be

1:42:17giving you flow response. So instead of

1:42:19waiting for that you can execute your

1:42:22other agents. So let's say these agents

1:42:23will try to collect the uh images. These

1:42:26agents will try to collect the let's say

1:42:29any other file okay from the internet.

1:42:31So don't wait to execute any other

1:42:34agents. Just try to run them in a

1:42:36synchronous way. Okay, in a parallel

1:42:38way. So each of the agents will be

1:42:40running in a parallel way. So let's say

1:42:42this if this agent is also taking 1

1:42:44minutes. Okay. So simultaneously you are

1:42:47running something okay in the back end.

1:42:48So you are not supposed to wait for 3.5

1:42:50minutes. Get it? So that is the things I

1:42:53just wanted to tell you. So let's say

1:42:55whenever you are creating multi- aent

1:42:57system and whenever you are creating

1:42:58let's say independent connection okay

1:43:01independent let's say policy that time

1:43:03you should use this asynchronous

1:43:05programming inside um inside let's say

1:43:08Python or let's say whatever programming

1:43:09you're using you have to follow that.

1:43:11And if you see any kinds of agents code

1:43:13you will be uh seeing people are using

1:43:16this as okay uh essence

1:43:20this particular syntax people are using

1:43:22that okay so what is this essence as

1:43:24means asynchronous okay as synchronous

1:43:27functionality so in python there is a

1:43:28library called asense IO so we'll be

1:43:30following that particular library to

1:43:32implement this particular code okay so

1:43:33let me give you one example of

1:43:35asynchronous and synchronous programming

1:43:38so let's say here I can write

1:43:40synchronous Synchronous

1:43:45programming

1:43:52and this side we have asynchronous

1:43:54programming.

1:44:07So in synchronous programming what

1:44:08happens? Let's say

1:44:12what I can do I can give you one

1:44:14example. Uh let's say here

1:44:18um let's say you are you are having a

1:44:23you are having a

1:44:27gas stove. Okay. Gas stove.

1:44:31Uh in this gas stove you only have one

1:44:35uh

1:44:37one fire section. Okay. one fire

1:44:40section. So let's say you are having uh

1:44:42three dishes. Okay, you are having three

1:44:45dishes

1:44:47to cook.

1:44:50So what you will do? First of all, you

1:44:52will take the first dish and you will

1:44:54cook that. Once first dish is complete,

1:44:56then you will take the second dish. Then

1:44:58you have to complete then you will be

1:45:00taking the third dish. Then you will be

1:45:01completing. Okay, that's how you can see

1:45:03it is taking T1, it is taking T2, it is

1:45:06taking T3 time. Okay. So basically you

1:45:09have to wait for u you have to wait for

1:45:13the previous execution or let's say

1:45:14previous task to be completed then you

1:45:16can start the remaining task. But in

1:45:19asynchronous programming what happens

1:45:21let's say you are having same gas stove

1:45:24but here you are having let's say three

1:45:26fired section. You are having three fire

1:45:29section and you are having three dishes.

1:45:32Okay, let's say dish one,

1:45:34dish two and dish three. So what you

1:45:38have to do? You just need to

1:45:41run all of them simultaneously.

1:45:44Basically you are giving three dish to

1:45:46the three fire section. Okay. Now at the

1:45:49T1 time, okay, your all of the dishes

1:45:52should be completed. So you are not

1:45:54supposed to wait for the T1, T2 and T3

1:45:56to be completed. Okay. So this is the

1:45:58difference between synchronous

1:46:00programming and asynchronous

1:46:01programming. I hope you get it guys. So

1:46:03in Python we usually follow this

1:46:04synchronous programming. That means if

1:46:06you're running a function first of all

1:46:08that function would be completed then

1:46:10the remaining code would be executed.

1:46:12Now in programming we we call it as a

1:46:16sub routine. Let me just write here

1:46:19sub

1:46:21routine

1:46:24and we also call it as cool routine.

1:46:29I'll be discussing about what is this

1:46:32okay cool routine. So what is sub