Pratica di Shadowing: Agentic AI Frameworks Explained: Workflows, Multi-Agent, & Production - Impara a parlare inglese con i video
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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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Vocabolario e note di pronuncia per questa lezione
Questa lezione di conversazione si basa sul video “Agentic AI Frameworks Explained: Workflows, Multi-Agent, & Production”. Le parole che ritornano più spesso: agent, framework, agentic, example, role. Questo video contiene 115 frasi e 1583 parole da ripetere con lo shadowing. Il parlato dura 11:41. Chi parla mantiene un ritmo costante di circa 135 parole al minuto, comodo per lo shadowing. Solo il 79% delle parole rientra nelle 3.000 più comuni dell’inglese, quindi il lessico è impegnativo.
Vocaboli chiave di questo video
15 parole del video che vale la pena imparare, con pronuncia e significato:
| Parola | Pronuncia | Significato |
|---|---|---|
| framework sostantivo | /ˈfɹeɪm.wɜːk/ | struttura portante |
| autonomous aggettivo | /ɔˈtɑnəməs/ | autonomo, indipendente |
| prototype sostantivo | /ˈpɹəʊtətaɪp/ | prototipo |
| rapid aggettivo | /ˈɹæp.ɪd/ | rapido, deciso |
| suitable aggettivo | [ˈsjuː.tə.bɫ̩] | adatto, idoneo |
| collaborate verbo | /kəˈlæb.ə.ɹeɪt/ | collaborare |
| sequence sostantivo | /ˈsiː.kwəns/ | sequenza |
| setup sostantivo | /ˈsɛtʌp/ | apparecchio, dispositivo |
| accomplish verbo | /əˈkɑm.plɪʃ/ | compiere, realizzare |
| helpful aggettivo | /ˈhɛlp.fl̩/ | disponibile, utile |
| architecture sostantivo | /ˈɑː.kɪˌtɛk.t͡ʃə/ | architettura |
| ease verbo | /iz/ | attenuare |
| alert aggettivo | /əˈlɝt/ | allerta, attento |
| component sostantivo | /kəmˈpoʊ.nənt/ | componente |
| database sostantivo | /ˈdeɪtəˌbeɪs/ | database, base di dati |
I phrasal verb che sentirai
| Parola | Significato |
|---|---|
| figure out verbo | scoprire, rendersi conto |
| go off verbo | esplodere |
Frasi da ripetere
Frasi brevi e complete del video che puoi riutilizzare nella conversazione di tutti i giorni:
- 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 grammatica di questo video
Le strutture che chi parla usa di più, con le parole esatte del video:
| Struttura | Nel video |
|---|---|
| Present perfect have/has + participio passato — un’azione passata che conta ancora adesso | have predefined · has been fetched · has been written |
| Forma passiva be + participio passato — conta ciò che accade, non chi lo fa | can be sent · has been fetched · has been written |
| Frasi relative who / which + frase — un’informazione in più su una persona o una cosa | frameworks which are · LangGraph, which is |
Pronuncia a cui fare attenzione
Chi parla usa 10 contrazioni e forme ridotte, come don't, they're, gonna. Pronunciale nella forma breve, così come le senti.
- I suoni “sh” e “zh”: accomplish /əˈkɑm.plɪʃ/, integration /ˌɪn.tɪˈɡɹeɪ.ʃən/, essentially /ɪˈsɛnʃəli/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, documentation /ˌdɑkjəmɛnˈteɪʃən/
- Parole lunghe — attenzione all’accento: autonomous /ɔˈtɑnəməs/, collaborate /kəˈlæb.ə.ɹeɪt/, architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, integration /ˌɪn.tɪˈɡɹeɪ.ʃən/, applicable /əˈplɪk.ə.bəl/
I suoni difficili per chi parla italiano:
- /h/ — va soffiata, non è muta: helpful /ˈhɛlp.fl̩/, hence /ˈhɛns/
- Consonante finale — senza aggiungere una vocale dopo: framework /ˈfɹeɪm.wɜːk/, autonomous /ɔˈtɑnəməs/, prototype /ˈpɹəʊtətaɪp/, rapid /ˈɹæp.ɪd/, collaborate /kəˈlæb.ə.ɹeɪt/
- /æ/ — più aperta della “e”: rapid /ˈɹæp.ɪd/, collaborate /kəˈlæb.ə.ɹeɪt/, craft /ˈkɹæft/, canvas /ˈkæn.vəs/, analyze /ˈæn.əˌlaɪz/
Come esercitarsi con questo video
- Ascolta tutto il video una volta senza parlare e annota le parole che non conosci.
- Fai shadowing frase per frase a velocità normale, ripetendo ognuna finché il tuo ritmo coincide con quello di chi parla.
- Registrati e confronta con l’originale, facendo attenzione a parole come framework, autonomous, prototype.
Cos'è la tecnica dello Shadowing?
Shadowing è una tecnica di apprendimento delle lingue supportata da studi scientifici, originariamente sviluppata per la formazione dei traduttori professionisti e resa popolare dal poliglotta Dr. Alexander Arguelles. Il metodo è semplice ma potente: ascolti un audio in inglese di madrelingua e lo ripeti immediatamente ad alta voce — come un'ombra che segue il parlante con un ritardo di solo 1–2 secondi. A differenza dell'ascolto passivo o degli esercizi di grammatica, lo shadowing costringe il tuo cervello e i muscoli della bocca a elaborare e riprodurre simultaneamente i modelli di discorso reale. La ricerca dimostra che migliora significativamente la precisione della pronuncia, l'intonazione, il ritmo, il discorso connesso, la comprensione dell'ascolto e la fluidità del parlato — rendendolo uno dei metodi più efficaci per la preparazione alla prova di speaking dell'IELTS e per la comunicazione reale in inglese.