シャドーイング練習: Agentic AI Frameworks Explained: Workflows, Multi-Agent, & Production - 動画で英語スピーキングを学ぶ

練習モードを選ぶ

各モードは全画面の練習ルームで開きます。

124 文

文が短すぎたり長すぎる場合は、Editをタップして調整してください。

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.

100

Another good example here is line graph,

101

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

102

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

103

Last but not least, you have the rapid prototyping.

104

So you always don't need a perfect architecture.

105

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

106

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

107

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

108

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

109

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

110

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

111

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

112

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

113

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

114

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

115

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

116

If it's predictable, use a workflow approach.

117

If it's exploratory, use the autonomous agents.

118

If it needs teamwork, use role -based systems.

119

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

120

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

121

a team of agents, or a production AI system.

122

Which agentic AI framework is your typical go -to?

123

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

124

Thank you.

アプリをダウンロード

流暢に話すために必要なすべて

AI発音採点文ごとに採点

IPA練習すべての音をマスター

単語学習自分の単語帳を作る

単語ゲーム遊びながら覚える

QRをスキャン

Download on theApp Store Get it onGoogle Play

同じチャンネル

FOR YOU

このレッスンの語彙とスピーキングのポイント

このスピーキングレッスンは、動画「Agentic AI Frameworks Explained: Workflows, Multi-Agent, & Production」を教材にしています。繰り返し出てくる語は次のとおりです：agent, framework, agentic, example, role。この動画には、シャドーイング用の文が115文、単語が1583語あります。音声の長さは11:41です。話す速さは1分あたり約135語で安定しており、シャドーイングしやすいペースです。英語の頻出3,000語に含まれる単語は79%だけなので、語彙は難しめです。

この動画の重要語彙

動画に出てくる覚えておきたい単語15語を、発音と意味つきで紹介します。

単語	発音	意味
framework 名詞	/ˈfɹeɪm.wɜːk/	骨組み
autonomous 形容詞	/ɔˈtɑnəməs/	自治, 自主的な
prototype 名詞	/ˈpɹəʊtətaɪp/	プロトタイプ, 原型
rapid 形容詞	/ˈɹæp.ɪd/	速い, 急な
suitable 形容詞	[ˈsjuː.tə.bɫ̩]	相応しい, 相応な
collaborate 動詞	/kəˈlæb.ə.ɹeɪt/	協力する, 共同して働く
sequence 名詞	/ˈsiː.kwəns/	順序
helpful 形容詞	/ˈhɛlp.fl̩/	役に立つ, 有用
architecture 名詞	/ˈɑː.kɪˌtɛk.t͡ʃə/	建築, 建築学
alert 形容詞	/əˈlɝt/	機敏な, 警戒する
component 名詞	/kəmˈpoʊ.nənt/	部分
database 名詞	/ˈdeɪtəˌbeɪs/	データベース
integration 名詞	/ˌɪn.tɪˈɡɹeɪ.ʃən/	統合, 融合
scenario 名詞	/sɪˈnɛəɹioʊ/	脚本, 台本
interface 名詞	/ˈɪn(t)ɚˌfeɪs/	インターフェース

繰り返し練習したい文

動画に出てくる短く完結した文です。日常会話でそのまま使えます。

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?

この動画の文法

話し手がよく使っている文型を、動画の実際の表現とともに紹介します。

文型	動画での表現
現在完了形 have/has + 過去分詞 — 過去の出来事が今も関係している	have predefined · has been fetched · has been written
受動態 be + 過去分詞 — 誰がするかより、何が起きるかに焦点を当てる	can be sent · has been fetched · has been written
関係詞節 who / which + 節 — 人や物について情報を加える	frameworks which are · LangGraph, which is

注意したい発音

話し手はdon't, they're, gonnaなど、短縮形や弱形を10回使っています。聞こえたとおりの短い形で発音しましょう。

「sh」と「zh」の音: accomplish /əˈkɑm.plɪʃ/, integration /ˌɪn.tɪˈɡɹeɪ.ʃən/, essentially /ɪˈsɛnʃəli/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, documentation /ˌdɑkjəmɛnˈteɪʃən/
長い単語（アクセントの位置に注意）: autonomous /ɔˈtɑnəməs/, collaborate /kəˈlæb.ə.ɹeɪt/, architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, integration /ˌɪn.tɪˈɡɹeɪ.ʃən/, applicable /əˈplɪk.ə.bəl/

日本語話者が苦手な音：

/r/ と /l/ の区別 — /l/ は舌先を歯茎につけ、/r/ はどこにもつけない: collaborate /kəˈlæb.ə.ɹeɪt/, linear /ˈlɪn.i.əɹ/, predictable /pɹəˈdɪktəbləl/, reliable /ɹɪˈlaɪəbl̩/, experimental /ɪkspɛɹəˈmɛntəl/
/v/ — /b/ にならないように、上の歯を下唇に当てる: canvas /ˈkæn.vəs/, dive /daɪv/, observe /əbˈzɜːv/, retrieval /ɹɪˈtɹiːvəl/, validate /ˈvæl.ɪ.deɪt/
/f/ — 「フ」ではなく、上の歯と下唇で出す: framework /ˈfɹeɪm.wɜːk/, helpful /ˈhɛlp.fl̩/, define /dɪˈfaɪn/, craft /ˈkɹæft/, interface /ˈɪn(t)ɚˌfeɪs/

この動画での練習方法

まず声を出さずに動画を最後まで聞き、知らない単語をメモします。
通常の速度で一文ずつシャドーイングし、話し手のリズムに合うまで繰り返します。
自分の声を録音して元の音声と比べます。framework, autonomous, prototypeなどの単語に特に注意しましょう。

シャドーイングとは？英語上達に効果的な理由

シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。

シャドーイングのやり方: ステップ別の完全ガイドを読む →

シャドーイング練習: Agentic AI Frameworks Explained: Workflows, Multi-Agent, & Production - 動画で英語スピーキングを学ぶ

練習モードを選ぶ

アプリをダウンロード

同じチャンネル

Every Generation Thinks This Time Is Different: The AI Job Myth

What is Event Driven Architecture (EDA)?

AI, Machine Learning, Deep Learning and Generative AI Explained

Spec-Driven Development: AI Assisted Coding Explained

AI Agents vs Business Rules: Which Should Make Decisions?

AI & Education: Generative AI & the Future of Critical Thinking

EDR vs. EPP vs. NGAV

MCP vs ADK: How Modern AI Agents Connect and Work Together

Cybersecurity Architecture: Data Security

The 7 Skills You Need to Build AI Agents

What Is Jev? The AI Model That Doesn't Generate Text

How Smart Organizations Will Use AI: Jevons Paradox & Workforce Impact

What are AI Agents?

Cybersecurity Architecture: Five Principles to Follow (and One to Avoid)

Zero Trust Explained in 4 mins

What is Responsible AI? A Guide to AI Governance

What is AI Ethics?

おすすめ動画

LangChain in 5 Minutes (Explained Clearly)

Alexandr Wang on what matters in an age of abundant intelligence

Harnessing generative AI in architecture, engineering & construction

Most devs don't understand what agents are

Trust the Process: How to Design Teams That Actually Work

How to Become an AI Product Manager in 2026 | Ex-Google, Microsoft

When AIs act emotional

How To Learn To Code In 2026

Dear Higgsfield, I reverse engineered you in 12 hours

How does AI actually work?

AI Governance Explained: Ethical, Transparent & Accountable AI for the Future