शैडोइंग अभ्यास: Agentic AI Frameworks Explained: Workflows, Multi-Agent, & Production - वीडियो के साथ अंग्रेजी बोलना सीखें

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Hi, let me guess.

The world around you is abuzz with agentic AI systems and their massive potential.

So you decide to go off and build an agentic AI system.

You look for the best available framework out there.

And now, all of a sudden, you have 17 GitHub tabs open, five medium blocks bookmarked, and you are still clueless on how to proceed.

Yes, we've all been there.

LangChain, LangGraph, CrewAI, Autogen, SemanticKernel.

There are so many powerful frameworks out there, but which one would you pick?

And in order to answer that question, you first need to understand which type of agentic AI system you want to build.

In this video, we are going to cover five types of most common agent AI projects or systems.

We are going to discuss an example for each of those

and also list frameworks which are best suited for those types of agentic AI systems and projects.

First, let's get some basics out of the way.

What exactly is an agentic AI framework?

An agentic AI framework is a toolkit for building agentic AI systems.

Let's understand with an example.

Let's say you have an agent to analyze some sales data.

This agent goes to a database and extracts the relevant data.

It analyzes the data, maybe runs some calculations, and then generates a report and crafts a response that can be sent to the concerned person.

Now, there's a lot going on here and there's a lot of coordination that's needed.

And it gets even more complex when you have multiple agents working.

And that's exactly why you need an agentic AI framework.

The framework is like a building block.

Unlike a chatbot application where you just ask a question and get an answer,

the agentic AI system actually does a lot of planning, acting, and iterating.

It is because of this complexity involved that we need an agentic AI framework.

These are like building blocks that help us deploy and manage agentic AI systems.

Now, these have some predefined functions that help us build agentic AI systems with more ease and agility,

such as we have predefined architectures.

We might have integration and monitoring tools.

We might also have some task management capabilities and communication protocols.

Together, these features and functionalities give Agentic AI Frameworks the capability to allow us to build these systems with ease.

Like we discussed before, there are multiple Agentic AI Frameworks available out there, but they're not all competing to do the same type of tasks.

In fact, they're optimized for different types of Agentic AI systems.

Most Agentic AI systems and projects we are working on today fall into one of the five categories.

First, we have linear workflows.

We have autonomous AI agents or autonomous multi -agent systems.

We have role -based AI systems, production orchestration systems, and then we have rapid prototyping.

Let's dive in and understand each of these in more detail.

Let's start with the simplest one, linear workflows.

Now, this type of an agentic AI system is where things progress in a step -by -step fashion.

It is more predictable what's going to happen next.

And the steps follow a certain sequence.

For example, consider a customer -facing application.

Let's say a customer support agent.

The role of this agent is when a user asks a question,

the agent is going to take the question and search the knowledge base for relevant responses.

It's then going to craft a response and send it back to the user

and maybe take an additional action such as creating a support ticket.

Now, if you observe, these steps are progressing in a certain fashion,

in a certain sequence, and these systems are more useful when you need the flows to be more reliable.

There isn't a need for multiple agents to collaborate to make this happen, and that gives you more control on how things progress.

A good example of frameworks that are suitable for this kind of a setup include Lankchain and Lamaindex.

Lankchain is more suited for setups where multiple steps need to be happening in a certain sequence

and Lama index is highly suitable for applications that are heavy on the data retrieval and indexing.

For more complex setups, you could also use LangGraph, which is also by Langchain.

Next, we have the autonomous agentic AI systems.

In these systems, you typically give AI a goal and have it figure out how to accomplish it.

So in this system, it's very common to see multiple agents

These agents talk to each other to accomplish the common goal.

A good example of this could be an AI coding assistant.

You could have a planner agent that plans the solution for you.

You could have a coder agent that actually writes the code for you, and a reviewer agent that is reviewing the code,

making recommendations, and also helping with the debugging.

These agents are constantly talking to each other in order to give the best code possible to you.

So in this kind of setup, the problem is usually open -ended.

And that's the kind of problems this kind of setup is most helpful for.

So frameworks that work best for this kind of a scenario include autogen.

You could also use experimental setups like baby AGI.

And Crew AI could also be helpful

for designing these kinds of systems where problems are open -ended and multiple agents are collaborating together to achieve a shared goal.

Next, we have the role -based agentic AI systems.

These are kind of similar to the autonomous agentic AI systems where there are multiple agents collaborating.

So it is also a multi -agent setup.

But what makes it different is that each agent within the setup has a defined role.

They are still communicating with each other to accomplish that goal, but they are operating within the confines or the constraints posed by their role descriptions.

They are still working together, but with clear boundaries.

A good example of this could be a content generation agent.

Here you could have a researcher agent that goes on the web

and fetches all the material that's needed to write a piece of content.

There could be a writer agent that looks at all the content

that has been fetched and writes up an article that goes out on a social media website, let's say.

And then there could be an editor agent that's looking at the article

that has been written and makes some edits to it.

Now they have very clearly defined roles and they don't go into other agents' roles when they do this.

They have discussions, but they're strictly confined to the description that has been given to them for their particular roles.

A good framework that is applicable here is crew AI.

But you could also use autogen with some structures around it for this kind of agentic AI systems.

There are also some niche frameworks that are applicable to very specific tasks like for example, for software development kind of tasks, you have chat dev.

So these are the kind of frameworks that you would use for role -based agentic AI systems.

Next up, we have the production orchestration systems.

Like the name suggests, this is when AI moves out of the experimentation phase

and gets real or moves into a real world system.

These kind of systems require deep integration with APIs, databases, and business workflows.

Consider the example of an AI operations agent.

This agent detects alerts, searches the documentation for the alerts and then run some automation scripts

and send summaries in a real world scenario within an organization.

Good examples of frameworks that are suitable for this kind of an AI system include agent framework,

which essentially is a combination of semantic kernel and autogen.

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Another good example here is line graph,

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which works for well -structured, multi -layered applications.

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Agent Framework is suitable both for orchestration as well as for running autonomous workflows.

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Last but not least, you have the rapid prototyping.

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So you always don't need a perfect architecture.

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You just need to check if your idea would work or not.

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These types of systems are best when you need to quickly validate ideas.

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It helps you build quick prototypes to see if you can bring your ideas to reality.

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These kind of systems are where you ideally have a user interface where you can drag components

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and bring them onto a canvas to build quick workflows and test ideas.

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Examples of tools or frameworks that are useful here include Langflow and FlowWise.

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These tools offer you a good graphical user interface where you can bring components onto a canvas

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and connect models and workflows and quickly test out your ideas that you can later on take into production.

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These are very quick for rapid prototyping, and hence the name.

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So when choosing a framework, do not ask which framework is the best.

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Instead, ask what kind of system am I trying to build?

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If it's predictable, use a workflow approach.

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If it's exploratory, use the autonomous agents.

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If it needs teamwork, use role -based systems.

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If it's going into production, use the production orchestration frameworks.

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And if you are testing ideas, use the rapid prototyping tools because the right framework depends on whether you're building a pipeline,

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a team of agents, or a production AI system.

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Which agentic AI framework is your typical go -to?

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Feel free to comment below and don't forget to like and subscribe.

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Thank you.

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इस पाठ की शब्दावली और बोलने के सुझाव

यह स्पीकिंग पाठ वीडियो “Agentic AI Frameworks Explained: Workflows, Multi-Agent, & Production” पर आधारित है। सबसे ज़्यादा दोहराए गए शब्द ये हैं: agent, framework, agentic, example, role। इस वीडियो में शैडोइंग के लिए 115 वाक्य और 1583 शब्द हैं। बोला गया हिस्सा 11:41 लंबा है। वक्ता लगभग 135 शब्द प्रति मिनट की स्थिर गति से बोलता है, जो शैडोइंग के लिए आरामदायक है। केवल 79% शब्द अंग्रेज़ी के 3,000 सबसे आम शब्दों में से हैं, इसलिए शब्दावली कठिन है।

इस वीडियो की मुख्य शब्दावली

वीडियो के 15 सीखने लायक शब्द, उच्चारण और अर्थ के साथ:

शब्द	उच्चारण	अर्थ
autonomous विशेषण	/ɔˈtɑnəməs/	स्वायत्त
suitable विशेषण	[ˈsjuː.tə.bɫ̩]	उपयुक्त, उचित
accomplish क्रिया	/əˈkɑm.plɪʃ/	सफलता, उपलब्धि
helpful विशेषण	/ˈhɛlp.fl̩/	मददगार
architecture संज्ञा	/ˈɑː.kɪˌtɛk.t͡ʃə/	वास्तुकला, स्थापत्य
alert विशेषण	/əˈlɝt/	सतर्क, सजग
database संज्ञा	/ˈdeɪtəˌbeɪs/	डेटाबेस
integration संज्ञा	/ˌɪn.tɪˈɡɹeɪ.ʃən/	एकीकरण
scenario संज्ञा	/sɪˈnɛəɹioʊ/	परिदृश्य
interface संज्ञा	/ˈɪn(t)ɚˌfeɪs/	राब्ता, वास्ता
capability संज्ञा	/ˌkeɪ.pəˈbɪl.ɪ.ti/	क्षमता
analyze क्रिया	/ˈæn.əˌlaɪz/	विश्लेषण करना
dragon संज्ञा	/ˈdɹæɡən/	अज़दहा, अजदहा
monitor संज्ञा	/ˈmɑ.nɪ.tɚ/	पर्यवेक्षक
summary संज्ञा	/ˈsʌm.ə.ɹi/	सारांश, ख़ुलासा

दोहराने लायक वाक्य

वीडियो के छोटे, पूरे वाक्य जिन्हें आप रोज़ की बातचीत में इस्तेमाल कर सकते हैं:

Yes, we've all been there.
First, let's get some basics out of the way.
Let's understand with an example.
Let's start with the simplest one, linear workflows.
Instead ask what kind of system am I trying to build?

इस वीडियो का व्याकरण

वक्ता जिन संरचनाओं का सबसे अधिक प्रयोग करता है, वीडियो के असली शब्दों के साथ:

संरचना	वीडियो में
Present perfect have/has + past participle — बीता हुआ काम जिसका असर अभी भी है	have predefined · has been fetched · has been written
Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है	can be sent · has been fetched · has been written
Relative clauses who / which + उपवाक्य — व्यक्ति या वस्तु के बारे में अतिरिक्त जानकारी	frameworks which are · LangGraph, which is

ध्यान देने योग्य उच्चारण

वक्ता 10 बार संक्षिप्त रूपों का प्रयोग करता है, जैसे don't, they're, gonna। इन्हें वैसे ही छोटा बोलें जैसा आप सुनते हैं।

“sh” और “zh” की ध्वनियाँ: accomplish /əˈkɑm.plɪʃ/, integration /ˌɪn.tɪˈɡɹeɪ.ʃən/, essentially /ɪˈsɛnʃəli/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, documentation /ˌdɑkjəmɛnˈteɪʃən/
लंबे शब्द — बलाघात सही रखें: autonomous /ɔˈtɑnəməs/, collaborate /kəˈlæb.ə.ɹeɪt/, architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, integration /ˌɪn.tɪˈɡɹeɪ.ʃən/, applicable /əˈplɪk.ə.bəl/

हिंदी भाषियों के लिए मुश्किल ध्वनियाँ:

/v/ और /w/ का फ़र्क़ — /v/ में ऊपर के दाँत निचले होंठ को छूते हैं, /w/ में होंठ गोल होते हैं: framework /ˈfɹeɪm.wɜːk/, sequence /ˈsiː.kwəns/, canvas /ˈkæn.vəs/, dive /daɪv/, observe /əbˈzɜːv/
/z/ — इसे “ज” (/dʒ/) न बनाएँ: ease /iz/, analyze /ˈæn.əˌlaɪz/, pose /poʊz/, observe /əbˈzɜːv/, abuzz /əˈbʌz/
शब्द की शुरुआत में s + व्यंजन — पहले “इ” न जोड़ें: script /skɹɪpt/, strictly /ˈstɹɪktli/

इस वीडियो से अभ्यास कैसे करें

पहले बिना बोले पूरा वीडियो एक बार सुनें और जो शब्द नहीं आते उन्हें लिख लें।
सामान्य गति पर एक-एक वाक्य की शैडोइंग करें और हर वाक्य तब तक दोहराएँ जब तक आपकी लय वक्ता से न मिल जाए।
अपनी आवाज़ रिकॉर्ड करें और मूल से तुलना करें, ख़ासकर autonomous, suitable, accomplish जैसे शब्दों पर ध्यान दें।

शैडोइंग तकनीक क्या है?

शैडोइंग (Shadowing) एक विज्ञान-समर्थित भाषा सीखने की तकनीक है जो मूल रूप से पेशेवर दुभाषिया प्रशिक्षण के लिए विकसित की गई थी। विधि सरल लेकिन शक्तिशाली है: आप मूल अंग्रेज़ी ऑडियो सुनते हैं और तुरंत इसे ज़ोर से दोहराते हैं — जैसे वक्ता की छाया 1-2 सेकंड की देरी से। शोध से पता चलता है कि यह उच्चारण सटीकता, स्वर, लय, जुड़ी हुई ध्वनियाँ, सुनने की समझ और बोलने की प्रवाहशीलता में काफ़ी सुधार करता है।

शैडोइंग तकनीक: पूरी चरण-दर-चरण गाइड पढ़ें →

शैडोइंग अभ्यास: Agentic AI Frameworks Explained: Workflows, Multi-Agent, & Production - वीडियो के साथ अंग्रेजी बोलना सीखें

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इस पाठ की शब्दावली और बोलने के सुझाव

इस वीडियो की मुख्य शब्दावली

दोहराने लायक वाक्य

इस वीडियो का व्याकरण

ध्यान देने योग्य उच्चारण

इस वीडियो से अभ्यास कैसे करें

शैडोइंग तकनीक क्या है?