跟读练习: Agentic AI Frameworks Explained: Workflows, Multi-Agent, & Production - 通过视频学习英语口语

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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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本课的词汇与口语要点

这节口语课以视频“Agentic AI Frameworks Explained: Workflows, Multi-Agent, & Production”为素材。视频中反复出现的词有：agent, framework, agentic, example, role。这段视频共有 115 个句子、1583 个单词可供跟读。讲话部分时长为 11:41。说话人语速平稳，每分钟约 135 个词，很适合跟读。只有 79% 的单词属于英语最常用的 3,000 词，词汇难度较高。

视频中的重点词汇

视频中 15 个值得学习的单词，附发音和释义：

单词	发音	释义
framework 名词	/ˈfɹeɪm.wɜːk/	骨架, 框架
autonomous 形容词	/ɔˈtɑnəməs/	自治
prototype 名词	/ˈpɹəʊtətaɪp/	原型, 雏形
rapid 形容词	/ˈɹæp.ɪd/	快, 迅速
suitable 形容词	[ˈsjuː.tə.bɫ̩]	合適 /合适, 適當 /适当
collaborate 动词	/kəˈlæb.ə.ɹeɪt/	合作, 協作 /协作
sequence 名词	/ˈsiː.kwəns/	序列, 順序 /顺序
setup 名词	/ˈsɛtʌp/	陷害
accomplish 动词	/əˈkɑm.plɪʃ/	達到 /达到
helpful 形容词	/ˈhɛlp.fl̩/	有幫助 /有帮助
architecture 名词	/ˈɑː.kɪˌtɛk.t͡ʃə/	建築 /建筑, 建築學 /建筑学
alert 形容词	/əˈlɝt/	警惕
component 名词	/kəmˈpoʊ.nənt/	元件, 部件
database 名词	/ˈdeɪtəˌbeɪs/	數據庫 /数据库
define 动词	/dɪˈfaɪn/	界定

视频中出现的短语动词

单词	释义
figure out 动词	弄清楚

值得反复跟读的句子

视频中简短而完整的句子，可以直接用在日常对话里：

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?

视频中的语法

说话人最常用的结构，并附上视频中的原话：

结构	视频中的用法
现在完成时 have/has + 过去分词 — 过去发生但与现在仍有关联的事	have predefined · has been fetched · has been written
被动语态 be + 过去分词 — 强调发生了什么，而不是谁做的	can be sent · has been fetched · has been written
定语从句 who / which + 从句 — 补充说明人或事物	frameworks which are · LangGraph, which is

需要注意的发音

说话人用了 10 次缩略和弱读形式，例如 don't, they're, gonna。请按听到的简短形式来说。

“sh” 和 “zh” 音: accomplish /əˈkɑm.plɪʃ/, integration /ˌɪn.tɪˈɡɹeɪ.ʃən/, essentially /ɪˈsɛnʃəli/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, documentation /ˌdɑkjəmɛnˈteɪʃən/
长单词——注意重音位置: autonomous /ɔˈtɑnəməs/, collaborate /kəˈlæb.ə.ɹeɪt/, architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, integration /ˌɪn.tɪˈɡɹeɪ.ʃən/, applicable /əˈplɪk.ə.bəl/

中文母语者容易读错的音：

/v/ — 不要读成 /w/，上齿轻触下唇: canvas /ˈkæn.vəs/, dive /daɪv/, observe /əbˈzɜːv/, retrieval /ɹɪˈtɹiːvəl/, validate /ˈvæl.ɪ.deɪt/
词尾辅音 — 要读清楚，后面不要加元音: framework /ˈfɹeɪm.wɜːk/, autonomous /ɔˈtɑnəməs/, prototype /ˈpɹəʊtətaɪp/, rapid /ˈɹæp.ɪd/, collaborate /kəˈlæb.ə.ɹeɪt/
/r/ 和 /l/ 的区别: collaborate /kəˈlæb.ə.ɹeɪt/, linear /ˈlɪn.i.əɹ/, predictable /pɹəˈdɪktəbləl/, reliable /ɹɪˈlaɪəbl̩/, experimental /ɪkspɛɹəˈmɛntəl/

如何用这段视频练习

先完整听一遍视频，不要开口，记下不认识的单词。
用正常速度逐句跟读，每句重复到你的节奏与说话人一致为止。
录下自己的声音并与原声对比，特别注意 framework, autonomous, prototype 这类单词。

什么是跟读法？

跟读法 (Shadowing) 是一种有科学依据的语言学习技巧，最初开发用于专业口译员的培训，并由多语言者Alexander Arguelles博士普及。这个方法简单而强大：您在听英语母语原声的同时立即大声重复——就像是一个延迟1-2秒紧跟说话者的影子。与被动听力或语法练习不同，跟读法强迫您的大脑和口腔肌肉同时处理并模仿真实的讲话模式。研究表明它能显着提高发音准确性，语调，节奏，连读，听力理解和口语流利度——使其成为雅思口语备考和真实英语交流最有效的方法之一。

影子跟读法: 阅读完整分步指南 →

跟读练习: Agentic AI Frameworks Explained: Workflows, Multi-Agent, & Production - 通过视频学习英语口语

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