쉐도잉 연습: Agentic AI Frameworks Explained: Workflows, Multi-Agent, & Production - 영상으로 영어 말하기 배우기

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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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이 레슨의 어휘와 말하기 포인트

이 말하기 레슨은 영상 “Agentic AI Frameworks Explained: Workflows, Multi-Agent, & Production”을(를) 바탕으로 합니다. 가장 자주 반복되는 단어는 다음과 같습니다: agent, framework, agentic, example, role. 이 영상에는 섀도잉할 문장 115개와 단어 1583개가 있습니다. 말하는 구간의 길이는 11:41입니다. 화자는 분당 약 135단어의 일정한 속도로 말해서 섀도잉하기에 편한 속도입니다. 영어에서 가장 많이 쓰이는 3,000단어에 속하는 단어가 79%뿐이라 어휘가 어려운 편입니다.

이 영상의 핵심 어휘

영상에 나오는 익혀 둘 만한 단어 15개를 발음, 뜻과 함께 정리했습니다.

단어	발음	뜻
autonomous 형용사	/ɔˈtɑnəməs/	자치, 자주적
rapid 형용사	/ˈɹæp.ɪd/	빠르다
collaborate 동사	/kəˈlæb.ə.ɹeɪt/	공동으로 일하다
sequence 명사	/ˈsiː.kwəns/	순서
architecture 명사	/ˈɑː.kɪˌtɛk.t͡ʃə/	건축, 건축학
alert 형용사	/əˈlɝt/	기민하다, 경계하다
component 명사	/kəmˈpoʊ.nənt/	부분
database 명사	/ˈdeɪtəˌbeɪs/	데이터베이스
integration 명사	/ˌɪn.tɪˈɡɹeɪ.ʃən/	통합, 융합
scenario 명사	/sɪˈnɛəɹioʊ/	각본, 대본
applicable 형용사	/əˈplɪk.ə.bəl/	적용할 수 있는
interface 명사	/ˈɪn(t)ɚˌfeɪs/	인터페이스
canvas 명사	/ˈkæn.vəs/	타프, 범포
analyze 동사	/ˈæn.əˌlaɪz/	분석하다
fetch 동사	/fɛt͡ʃ/	가지고 오다

따라 말해 볼 만한 문장

영상에 나오는 짧고 완결된 문장으로, 일상 대화에서 그대로 쓸 수 있습니다:

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?

이 영상의 문법

화자가 가장 많이 쓰는 문형을 영상 속 실제 표현과 함께 정리했습니다.

문형	영상 속 표현
현재완료 have/has + 과거분사 — 과거의 일이 지금도 관련이 있을 때	have predefined · has been fetched · has been written
수동태 be + 과거분사 — 누가 하는지보다 무슨 일이 일어나는지에 초점	can be sent · has been fetched · has been written
관계절 who / which + 절 — 사람이나 사물에 대한 추가 정보	frameworks which are · LangGraph, which is

주의할 발음

화자는 don't, they're, gonna 같은 축약형과 약화된 형태를 10번 사용합니다. 들리는 대로 짧게 발음하세요.

“sh”와 “zh” 소리: accomplish /əˈkɑm.plɪʃ/, integration /ˌɪn.tɪˈɡɹeɪ.ʃən/, essentially /ɪˈsɛnʃəli/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, documentation /ˌdɑkjəmɛnˈteɪʃən/
긴 단어 — 강세 위치에 주의: autonomous /ɔˈtɑnəməs/, collaborate /kəˈlæb.ə.ɹeɪt/, architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, integration /ˌɪn.tɪˈɡɹeɪ.ʃən/, applicable /əˈplɪk.ə.bəl/

한국어 화자가 어려워하는 소리:

/f/ — ㅍ(/p/)으로 바꾸지 말고 윗니를 아랫입술에 대기: framework /ˈfɹeɪm.wɜːk/, helpful /ˈhɛlp.fl̩/, define /dɪˈfaɪn/, craft /ˈkɹæft/, interface /ˈɪn(t)ɚˌfeɪs/
/v/ — ㅂ(/b/)과 구별하기: canvas /ˈkæn.vəs/, dive /daɪv/, observe /əbˈzɜːv/, retrieval /ɹɪˈtɹiːvəl/, validate /ˈvæl.ɪ.deɪt/
/z/ — ㅈ이 아니라 성대를 울리는 /s/: ease /iz/, analyze /ˈæn.əˌlaɪz/, pose /poʊz/, observe /əbˈzɜːv/, abuzz /əˈbʌz/

이 영상으로 연습하는 방법

먼저 말하지 않고 영상을 끝까지 듣고 모르는 단어를 적어 둡니다.
보통 속도로 한 문장씩 섀도잉하고, 화자의 리듬과 맞을 때까지 반복합니다.
자신의 목소리를 녹음해 원본과 비교하고, autonomous, rapid, collaborate 같은 단어에 특히 주의합니다.

쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법

쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.

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쉐도잉 연습: Agentic AI Frameworks Explained: Workflows, Multi-Agent, & Production - 영상으로 영어 말하기 배우기

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