Pratique du Shadowing: Agentic AI Frameworks Explained: Workflows, Multi-Agent, & Production - Apprendre l'anglais à l'oral avec la vidéo
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124 phrases
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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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Vocabulaire et conseils d’expression pour cette leçon
Cette leçon d’expression orale s’appuie sur la vidéo « Agentic AI Frameworks Explained: Workflows, Multi-Agent, & Production ». Les mots qui reviennent le plus souvent : agent, framework, agentic, example, role. Cette vidéo contient 115 phrases et 1583 mots à répéter en shadowing. La partie parlée dure 11:41. Le locuteur parle à un rythme régulier d’environ 135 mots par minute, confortable pour le shadowing. Seuls 79 % des mots font partie des 3 000 mots les plus courants en anglais, le vocabulaire est donc exigeant.
Vocabulaire clé de cette vidéo
15 mots de la vidéo à retenir, avec leur prononciation et leur sens :
| Mot | Prononciation | Sens |
|---|---|---|
| autonomous adjectif | /ɔˈtɑnəməs/ | autonome |
| prototype nom | /ˈpɹəʊtətaɪp/ | prototype |
| rapid adjectif | /ˈɹæp.ɪd/ | rapide |
| suitable adjectif | [ˈsjuː.tə.bɫ̩] | approprié, convenable |
| collaborate verbe | /kəˈlæb.ə.ɹeɪt/ | collaborer |
| sequence nom | /ˈsiː.kwəns/ | suite, séquence |
| setup nom | /ˈsɛtʌp/ | installation |
| accomplish verbe | /əˈkɑm.plɪʃ/ | accomplir |
| helpful adjectif | /ˈhɛlp.fl̩/ | utile, serviable |
| architecture nom | /ˈɑː.kɪˌtɛk.t͡ʃə/ | architecture |
| ease verbe | /iz/ | abaisser, abréger |
| alert adjectif | /əˈlɝt/ | alerte |
| component nom | /kəmˈpoʊ.nənt/ | composant |
| database nom | /ˈdeɪtəˌbeɪs/ | base de données, banque de données |
| define verbe | /dɪˈfaɪn/ | déterminer |
Les verbes à particule que vous entendrez
| Mot | Sens |
|---|---|
| figure out verbe | cerner |
| go off verbe | exploser |
Des phrases à répéter
Des phrases courtes et complètes de la vidéo, à réutiliser dans la conversation de tous les jours :
- 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?
La grammaire de cette vidéo
Les structures que le locuteur utilise le plus, avec les mots exacts de la vidéo :
| Structure | Dans la vidéo |
|---|---|
| Present perfect have/has + participe passé — une action passée qui compte encore maintenant | have predefined · has been fetched · has been written |
| Voix passive be + participe passé — l’accent est mis sur ce qui arrive, pas sur qui le fait | can be sent · has been fetched · has been written |
| Propositions relatives who / which + proposition — une précision sur une personne ou une chose | frameworks which are · LangGraph, which is |
Prononciation à surveiller
Le locuteur utilise 10 contractions et formes réduites, comme don't, they're, gonna. Prononcez-les sous leur forme courte, telles que vous les entendez.
- Les sons « sh » et « zh »: accomplish /əˈkɑm.plɪʃ/, integration /ˌɪn.tɪˈɡɹeɪ.ʃən/, essentially /ɪˈsɛnʃəli/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, documentation /ˌdɑkjəmɛnˈteɪʃən/
- Mots longs — placez bien l’accent: autonomous /ɔˈtɑnəməs/, collaborate /kəˈlæb.ə.ɹeɪt/, architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, integration /ˌɪn.tɪˈɡɹeɪ.ʃən/, applicable /əˈplɪk.ə.bəl/
Les sons difficiles pour les francophones :
- /h/ — il se souffle, il n’est pas muet: helpful /ˈhɛlp.fl̩/, hence /ˈhɛns/
- /tʃ/ et /dʒ/ — à ne pas adoucir en « ch » et « j »: architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, fetch /fɛt͡ʃ/, generate /ˈd͡ʒɛn.ə.ɹeɪt/, researcher /ˈɹiˌsɝ.t͡ʃɚ/, niche /nɪt͡ʃ/
- /r/ anglais — langue recourbée, sans frotter la gorge: framework /ˈfɹeɪm.wɜːk/, prototype /ˈpɹəʊtətaɪp/, rapid /ˈɹæp.ɪd/, collaborate /kəˈlæb.ə.ɹeɪt/, craft /ˈkɹæft/
Comment s’entraîner avec cette vidéo
- Écoutez la vidéo en entier une fois sans parler et notez les mots que vous ne connaissez pas.
- Répétez phrase par phrase à vitesse normale, en reprenant chacune jusqu’à ce que votre rythme corresponde à celui du locuteur.
- Enregistrez-vous et comparez avec l’original, en faisant attention à des mots comme autonomous, prototype, rapid.
Qu'est-ce que la technique du Shadowing ?
Le Shadowing est une technique d'apprentissage des langues fondée sur la science, développée à l'origine pour la formation des interprètes professionnels. Le principe est simple mais puissant : vous écoutez de l'anglais natif et le répétez immédiatement à voix haute — comme une ombre suivant le locuteur avec un décalage de 1 à 2 secondes. Les recherches montrent une amélioration significative de la précision de la prononciation, de l'intonation, du rythme, des liaisons, de la compréhension orale et de la fluidité.