Shadowing Practice: Agentic AI Frameworks Explained: Workflows, Multi-Agent, & Production - Learn English Speaking with Video
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124 משפטים
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Hi, let me guess.
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The world around you is abuzz with agentic AI systems and their massive potential.
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So you decide to go off and build an agentic AI system.
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You look for the best available framework out there.
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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.
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Yes, we've all been there.
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LangChain, LangGraph, CrewAI, Autogen, SemanticKernel.
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There are so many powerful frameworks out there, but which one would you pick?
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And in order to answer that question, you first need to understand which type of agentic AI system you want to build.
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In this video, we are going to cover five types of most common agent AI projects or systems.
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We are going to discuss an example for each of those
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and also list frameworks which are best suited for those types of agentic AI systems and projects.
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First, let's get some basics out of the way.
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What exactly is an agentic AI framework?
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An agentic AI framework is a toolkit for building agentic AI systems.
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Let's understand with an example.
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Let's say you have an agent to analyze some sales data.
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This agent goes to a database and extracts the relevant data.
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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.
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Now, there's a lot going on here and there's a lot of coordination that's needed.
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And it gets even more complex when you have multiple agents working.
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And that's exactly why you need an agentic AI framework.
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The framework is like a building block.
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Unlike a chatbot application where you just ask a question and get an answer,
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the agentic AI system actually does a lot of planning, acting, and iterating.
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It is because of this complexity involved that we need an agentic AI framework.
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These are like building blocks that help us deploy and manage agentic AI systems.
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Now, these have some predefined functions that help us build agentic AI systems with more ease and agility,
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such as we have predefined architectures.
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We might have integration and monitoring tools.
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We might also have some task management capabilities and communication protocols.
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Together, these features and functionalities give Agentic AI Frameworks the capability to allow us to build these systems with ease.
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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.
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In fact, they're optimized for different types of Agentic AI systems.
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Most Agentic AI systems and projects we are working on today fall into one of the five categories.
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First, we have linear workflows.
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We have autonomous AI agents or autonomous multi -agent systems.
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We have role -based AI systems, production orchestration systems, and then we have rapid prototyping.
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Let's dive in and understand each of these in more detail.
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Let's start with the simplest one, linear workflows.
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Now, this type of an agentic AI system is where things progress in a step -by -step fashion.
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It is more predictable what's going to happen next.
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And the steps follow a certain sequence.
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For example, consider a customer -facing application.
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Let's say a customer support agent.
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The role of this agent is when a user asks a question,
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the agent is going to take the question and search the knowledge base for relevant responses.
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It's then going to craft a response and send it back to the user
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and maybe take an additional action such as creating a support ticket.
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Now, if you observe, these steps are progressing in a certain fashion,
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in a certain sequence, and these systems are more useful when you need the flows to be more reliable.
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There isn't a need for multiple agents to collaborate to make this happen, and that gives you more control on how things progress.
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A good example of frameworks that are suitable for this kind of a setup include Lankchain and Lamaindex.
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Lankchain is more suited for setups where multiple steps need to be happening in a certain sequence
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and Lama index is highly suitable for applications that are heavy on the data retrieval and indexing.
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For more complex setups, you could also use LangGraph, which is also by Langchain.
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Next, we have the autonomous agentic AI systems.
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In these systems, you typically give AI a goal and have it figure out how to accomplish it.
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So in this system, it's very common to see multiple agents
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These agents talk to each other to accomplish the common goal.
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A good example of this could be an AI coding assistant.
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You could have a planner agent that plans the solution for you.
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You could have a coder agent that actually writes the code for you, and a reviewer agent that is reviewing the code,
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making recommendations, and also helping with the debugging.
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These agents are constantly talking to each other in order to give the best code possible to you.
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So in this kind of setup, the problem is usually open -ended.
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And that's the kind of problems this kind of setup is most helpful for.
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So frameworks that work best for this kind of a scenario include autogen.
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You could also use experimental setups like baby AGI.
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And Crew AI could also be helpful
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for designing these kinds of systems where problems are open -ended and multiple agents are collaborating together to achieve a shared goal.
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Next, we have the role -based agentic AI systems.
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These are kind of similar to the autonomous agentic AI systems where there are multiple agents collaborating.
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So it is also a multi -agent setup.
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But what makes it different is that each agent within the setup has a defined role.
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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.
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They are still working together, but with clear boundaries.
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A good example of this could be a content generation agent.
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Here you could have a researcher agent that goes on the web
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and fetches all the material that's needed to write a piece of content.
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There could be a writer agent that looks at all the content
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that has been fetched and writes up an article that goes out on a social media website, let's say.
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And then there could be an editor agent that's looking at the article
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that has been written and makes some edits to it.
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Now they have very clearly defined roles and they don't go into other agents' roles when they do this.
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They have discussions, but they're strictly confined to the description that has been given to them for their particular roles.
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A good framework that is applicable here is crew AI.
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But you could also use autogen with some structures around it for this kind of agentic AI systems.
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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.
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So these are the kind of frameworks that you would use for role -based agentic AI systems.
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Next up, we have the production orchestration systems.
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Like the name suggests, this is when AI moves out of the experimentation phase
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and gets real or moves into a real world system.
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These kind of systems require deep integration with APIs, databases, and business workflows.
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Consider the example of an AI operations agent.
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This agent detects alerts, searches the documentation for the alerts and then run some automation scripts
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and send summaries in a real world scenario within an organization.
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Good examples of frameworks that are suitable for this kind of an AI system include agent framework,
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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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אוצר מילים והערות הגייה לשיעור הזה
שיעור הדיבור הזה ברמה C1 מבוסס על הסרטון “Agentic AI Frameworks Explained: Workflows, Multi-Agent, & Production”. המילים שחוזרות הכי הרבה: agent, framework, agentic, example, role. בסרטון הזה יש 124 משפטים ו-1595 מילים לתרגול Shadowing. הדיבור נמשך 11:42. הדובר שומר על קצב יציב של כ-136 מילים בדקה, קצב נוח לתרגול Shadowing. רק 79% מהמילים נמצאות בין 3,000 המילים הנפוצות ביותר באנגלית, כך שאוצר המילים מאתגר.
אוצר מילים מרכזי בסרטון הזה
15 המילים המתקדמות ביותר בסרטון, עם ההגייה והמשמעות שלהן:
| מילה | הגייה | משמעות |
|---|---|---|
| autonomous שם תואר | /ɔˈtɑnəməs/ | אוֹטוֹנוֹמִי |
| prototype שם עצם | /ˈpɹəʊtətaɪp/ | אב טיפוס |
| collaborate פועל | /kəˈlæb.ə.ɹeɪt/ | שיתף פעולה |
| accomplish פועל | /əˈkɑm.plɪʃ/ | בִּצֵּעַ |
| fetch פועל | /fɛt͡ʃ/ | הביא, השיג |
| canvas שם עצם | /ˈkæn.vəs/ | קַנְבָס |
| bookmark שם עצם | /ˈbʊkmɑːɹk/ | סִימָנִיָּה |
| clueless שם תואר | /ˈkluː.ləs/ | אוֹבֵד עֵצוֹת |
| calculation שם עצם | /ˌkælkjuˈleɪʃn̩/ | חִשׁוּב \ חישוב |
| automation שם עצם | /ˌɔ.təˈmeɪ.ʃən/ | אוֹטוֹמַצְיָה |
| niche שם עצם | /nɪt͡ʃ/ | גומחה, נישה |
| coordination שם עצם | /kəʊˌɔːdɪˈneɪʃən/ | תיאום |
| tab שם עצם | /tæb/ | לשונית |
| subscribe פועל | /səbˈskɹaɪb/ | נִמְנָה |
| extract שם עצם | /ˈɛkstɹækt/ | תַּמְצִית |
Phrasal verbs שתשמעו
| מילה | משמעות |
|---|---|
| figure out פועל | הֵבִין, מצא תשובה |
משפטים שכדאי לחזור עליהם
משפטים קצרים ושלמים מהסרטון שאפשר להשתמש בהם בשיחה יומיומית:
- 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.
- Let's say a customer support agent.
דקדוק בסרטון הזה
המבנים שהדובר משתמש בהם הכי הרבה, עם המילים המדויקות מהסרטון:
| מבנה | בסרטון |
|---|---|
| הווה מושלם (Present perfect) have/has + past participle — פעולה שקרתה בעבר ועדיין יש לה חשיבות או השפעה עכשיו | have predefined · has been fetched · has been written |
| סביל (Passive voice) be + past participle — הדגש הוא על מה שקורה, לא על מי שעושה את הפעולה | can be sent · has been fetched · has been written |
| משפטי זיקה (Relative clauses) who / which + clause — מידע נוסף על אדם או דבר | frameworks which are · LangGraph, which is |
הגייה ששווה לשים לב אליה
הדובר משתמש ב-9 קיצורים וצורות מקוצרות, כמו don't, they're, isn't. אמרו אותם בצורה הקצרה, כפי שאתם שומעים אותם.
- הצלילים של “sh” ו-“zh”: accomplish /əˈkɑm.plɪʃ/, experimentation /ɪkˌspɛɹ.ə.mənˈteɪ.ʃən/, calculation /ˌkælkjuˈleɪʃn̩/, automation /ˌɔ.təˈmeɪ.ʃən/, coordination /kəʊˌɔːdɪˈneɪʃən/
- מילים ארוכות — שימו לב להטעמה: autonomous /ɔˈtɑnəməs/, collaborate /kəˈlæb.ə.ɹeɪt/, predictable /pɹəˈdɪktəbləl/, capability /ˌkeɪ.pəˈbɪl.ɪ.ti/, agility /əˈd͡ʒɪl.ɪ.ti/
צלילים שדוברי עברית מתקשים בהם:
- /æ/ — פתוח יותר מה-“אֶ” העברית, לא כמו “a” ב-“cup”: collaborate /kəˈlæb.ə.ɹeɪt/, analyze /ˈæn.əˌlaɪz/, canvas /ˈkæn.vəs/, chatbot /ˈtʃætbɑt/, graphical /ˈɡɹæfɪkəl/
- /r/ — הלשון לא נוגעת בשום מקום, לא ה-“ר” הגרונית: prototype /ˈpɹəʊtətaɪp/, collaborate /kəˈlæb.ə.ɹeɪt/, predictable /pɹəˈdɪktəbləl/, bookmark /ˈbʊkmɑːɹk/, constraint /kənˈstɹeɪnt/
- עיצור קולי בסוף מילה — /b/, /d/, /g/, /z/, /v/ בלי להפוך אותם לאטומים: analyze /ˈæn.əˌlaɪz/, optimize /ˈɑptɪmaɪz/, tab /tæb/, subscribe /səbˈskɹaɪb/, observe /əbˈzɜːv/
איך לתרגל עם הסרטון הזה
- הקשיבו לכל הסרטון פעם אחת בלי לדבר, ורשמו את המילים שאתם לא מכירים.
- תרגלו Shadowing משפט אחר משפט במהירות רגילה, וחזרו על כל משפט עד שהקצב שלכם תואם את הדובר.
- הקליטו את עצמכם והשוו למקור, תוך תשומת לב למילים כמו autonomous, prototype, collaborate.
מהי טכניקת השאדואינג?
שאדואינג היא טכניקת למידת שפה מבוססת מדע שפותחה במקור להכשרת מתורגמנים מקצועיים ופופולרית על ידי הפוליגלוט ד"ר אלכסנדר ארגואלס. השיטה פשוטה אך עוצמתית: אתה מקשיב לאודיו באנגלית ילידית וחוזר עליו בקול רם מיד — כמו צל העוקב אחרי הדובר עם עיכוב של 1–2 שניות בלבד. בניגוד להאזנה פסיבית או תרגילי דקדוק, שאדואינג מכריח את המוח והשרירים בפה לעבד ולשחזר דפוסי דיבור אמיתיים בו זמנית. מחקרים מראים שזה משפר משמעותית את דיוק ההגייה, האינטונציה, הקצב, הדיבור המחובר, הבנת השמיעה ושטף הדיבור — מה שהופך אותו לאחת השיטות היעילות ביותר להכנה לIELTS Speaking ולתקשורת באנגלית בעולם האמיתי.