Pratique du Shadowing: Most devs don't understand what agents are - Apprendre l'anglais à l'oral avec la vidéo
Choisissez un mode d'entraînement
Chaque mode ouvre une salle d'entraînement en plein écran.
106 phrases
Si les phrases sont trop courtes ou trop longues, cliquez sur Edit pour les ajuster.
1
OpenAI just introduced AgentKit, a complete set of tools for developers and enterprises to build, deploy and optimize agents.
2
This is cool, fairly exciting announcement, developers can now design workflows visually, embed agentic UIs faster.
3
Now I don't really care about this product, I'm not sure I'll end up using it really.
4
But what I am kind of interested in is how they're talking about agents.
5
Because if we look at this, this is not an agent to me.
6
We have here a set of deterministic steps.
7
We start, then we enter a jailbreak guardrail, which by the way just filters for malicious inputs I assume.
8
Then we have another LLM call here which just routes the input to one of three separate agents.
9
This to me is not an agent builder, this is a workflow.
10
I thought we as a kind of AI engineering typescripty community had landed on some definitions for what agents were
11
and what workflows were.
12
But it turns out no we didn't because OpenAI seems to have a different definition from the one that we've been using.
13
I want to talk through this debate so that you understand what agents are, what workflows are and why the distinction even matters.
14
And the best place to start is with Anthropic's famous article building effective agents.
15
This came out in December last year and it basically codified what an agent was and what a workflow was.
16
This is how Anthropic defines an agent, it's essentially a loop.
17
We'll talk more about what this loop is in a minute and what makes this agentic. But
18
if we zoom up to a workflow example here we can see a very similar example to what OpenAI just put out.
19
Instead of a loop here we have a directional flow, we have predetermined code paths.
20
And this is what Anthropic calls a workflow.
21
And it's kind of funny too that this famous article really talking about, you know, titled building effective agents actually talks through like six different kinds of workflows.
22
Let's go to TLDraw where we've got a bit of dark mode where we can actually dive into some of these concepts.
23
An agent is a loop where the LLM decides when to stop.
24
That loop is essentially multiple LLM calls one after the other.
25
Now if you call an LLM multiple times with the same information it's not going to do anything useful.
26
And so to make this an agent you kind of need to give it new information each time.
27
The way that works is the LLM calls tools.
28
It basically says execute this piece of code for me and then tell me what happened when that piece of code ran.
29
Just to dive into this for a minute it kind of looks like this.
30
Let's imagine our system has access to a tool called write file where it can write files to the file system.
31
The user can say to the agent, write a new file called gitignore.
32
Then the assistant comes back with a message here saying, okay, call this tool with this content and this path.
33
On our local machine then we execute the tool and we send the result back to the LLM.
34
And so this flow becomes a loop where the LLM is gaining more information each time.
35
This beautiful loop is what drives things like clawed code, coding agents, all the stuff that you're kind of used to using.
36
The key thing then is that the agent then decides when it's had enough.
37
So the agent can either continue to call tools or it can say stop.
38
At which point it will emit a special token
39
that just says stop and we can catch that in the frontend and no longer call the LLM again and again.
40
Workflows are of course much easier to define.
41
There's no loop here, it's just predetermined steps one after another.
42
You take one LLM call, you pass its result to another LLM call and you pass that result to another LLM call.
43
You might have some deterministic logic in these steps, like If the LLM call returns one thing, do one thing.
44
If it returns another, do another.
45
But all of those code paths are known ahead of time and written in code.
46
Workflows are neat, by the way, because you get opportunities to optimise the system.
47
For instance, you can have parallel workflows where you have multiple LLM calls at the same time.
48
We might take in or produce a chunk of text, split it into two parts, get the LLM to summarise each part of it, and then pass the results of those to another LLM call where we summarise the summaries.
49
Because the path to the solution is known up front, we can optimise it in all sorts of ways, which make workflows really, really powerful.
50
And by the way, if I had to pick between agents and workflows, like one that I could take to a desert island, I would probably pick workflows.
51
But that's just me because I'm a natural contrarian.
52
So let's sum up then.
53
Agent and workflow.
54
What are the differences?
55
What are they good at?
56
Well, the first thing to say that to qualify in this category, you need multiple LLM calls.
57
Like a single LLM call all by itself doesn't really qualify as either an agent or a workflow.
58
It's just a frickin' API call.
59
We don't need an extra definition for that.
60
To me, the key difference is who decides when to stop the program.
61
With an agent, as we saw, it is the LLM really.
62
The LLM can say, OK, I've done the work, let's now stop.
63
Whereas in a workflow it is predetermined steps that are known up front.
64
Now the reason that this entire distinction matters is that agents and workflows are good for different things.
65
An agent is really good when the path to the solution is unclear
66
or when you need to be able to generalise it to lots and lots of different tasks.
67
Coding Coding assistants are a really, really, really good example of this.
68
Because the coding assistant in Clawcode or Cursor doesn't know what kind of codebase it's going to go into, it doesn't know what kind of bug you're going to throw at it, and so it needs to be able to adapt on the fly.
69
In other words, agents are really, really good at improvising.
70
But workflows are much better when the path to the solution is known up front.
71
When you need to do the same thing a thousand times, you always want a workflow.
72
Because as we saw with the parallelizable steps you can basically optimise it in all sorts of different ways.
73
Whereas an agent you really leave the optimisation up to the agent itself.
74
Agent is like jazz, you know, it's all improvisation, all feel.
75
And workflows are like classical music where you can spend ages optimising the upfront set up
76
so that the final output is as good as it can be.
77
The next thing to say though is that agents and workflows are a spectrum, not a hard definition.
78
Most systems out there you will see will be somewhere on this gradient between agent and workflow.
79
For instance, a pure agent where the LLM is solely in charge of deciding when to stop, well, I don't want to deploy that because that thing is going to eventually run forever.
80
And so most agents have a max steps counter, in other words a deterministic stop in the code to prevent the agent running infinitely.
81
This is so common that tools like the AISDK actually have a max steps parameter to their agents.
82
Going further down we have agents that contain workflows.
83
Many agents are able to call workflows from within tools.
84
Which by the way allows you to build really really smart systems
85
because you get the generalizability of the agent and then you're able to optimise the tools that that agent has.
86
Finally, of course, you can have workflows that contain loops.
87
This might be that you produce some text and you evaluate it multiple times to refine the output continuously.
88
The difference here, of course, is does the LLM itself have the ability to break the loop early?
89
For me that's a sign that it's an agent rather than a workflow.
90
But these terms are on a spectrum
91
and most systems out there will use some combination of each or have agents within workflows or workflows within agents.
92
And so the definitions are useful because they allow you to think about problems in terms of patterns.
93
And so it kind of hurts me a little bit when I see as agent workflows grow more complex.
94
Ah, what did we do to deserve this?
95
This is just so confusing.
96
Now of course I'm annoyed I suppose because I'm interested in agents versus workflows as like a pedagogical tool, as a teaching tool.
97
Because I do find the definitions useful for communicating what you're trying to build and the trade-offs between them.
98
But also there's a sense that everyone's using the word without there necessarily being a good definition behind it.
99
I only hope that this definition will spread
100
that the anthropic definition of just two calls in a loop will be what people land on.
101
Now if you're digging what I'm putting out then you will love AIHero.dev.
102
I'm going to be releasing something soon which is going to mash together AI
103
and TypeScript and give you the ability to ship really powerful AI applications with the language
104
that you know and you know that I love.
105
Thanks so much for joining along folks.
106
I will see you very soon.
Télécharger l'application
Tout pour parler couramment
Prononciation IAChaque phrase notée
Phonétique (IPA)Maîtrisez chaque son
VocabulaireEnrichissez votre lexique
Jeu de vocabulaireApprenez en jouant
Même chaîne
C14
New Skills! v1.3 brings /pr, /implement-spec, and /retro
Business EnglishWorking Adults
C13
mattpocock/skills: A complete AI Coding workflow, end-to-end
British AccentBusiness EnglishUniversity StudentsWorking Adults
B23
I was an AI skeptic. Then I tried plan mode
British AccentBusiness EnglishUniversity StudentsWorking Adults
B23
/wayfinder: Nothing is too big to plan anymore
British AccentBusiness EnglishUniversity StudentsWorking Adults
C15
The 7 phases of AI-driven development
British AccentBusiness EnglishUniversity StudentsWorking Adults
C12
How to actually force Claude Code to use the right CLI (don't use CLAUDE.md)
British AccentBusiness EnglishUniversity StudentsWorking Adults
C13
New Skills! v1.2 brings /wait-what, /writing-for-agents, and fixes /grill-me
British AccentDaily ConversationWorking Adults
C13
Learn anything with the /teach skill
British AccentBusiness EnglishUniversity StudentsWorking Adults
C16
5 Claude Code skills I use every single day
British AccentBusiness EnglishUniversity StudentsWorking Adults
C15
Can Cursor's HARDCORE Review Skill Stop The Slop?
British AccentBusiness EnglishUniversity StudentsWorking Adults
C14
New Skills! v1.1 brings /wayfinder, /research, /implement, /to-spec, /to-tickets
British AccentBusiness EnglishUniversity StudentsWorking Adults
C12
Most devs don't understand how LLM tokens work
British AccentBusiness EnglishUniversity StudentsWorking Adults
C1New Skills! /handoff, /prototype, /review and /writing-* | Skills Changelog
British AccentBusiness EnglishUniversity StudentsWorking Adults
FOR YOU
Vidéo recommandée
C11
124 phrases
Agentic AI Frameworks Explained: Workflows, Multi-Agent, & Production
Daily ConversationUniversity StudentsWorking Adults
C1678 phrases
So I tried Matt's skills...
Daily ConversationUniversity StudentsWorking Adults
C11
11 phrases
Why Is There So Much AI Slop?
Daily ConversationUniversity StudentsWorking Adults
C11
207 phrases
Your AI Agent Starts Every Session Like a New Hire - Killian Carlsen-Phelan, Sonar
Daily ConversationUniversity StudentsWorking Adults
C11
37 phrases
How Anthropic uses Claude in Product Management
Daily ConversationUniversity StudentsWorking Adults
C184 phrases
The 4-Hour Workweek in a World of AI
Daily ConversationUniversity StudentsWorking Adults
C13
148 phrases
900+ hours of Learning AI Agents in 9 Minutes
Daily ConversationUniversity StudentsWorking Adults
C11
484 phrases
How to Build an AI Agent for Every Part of Your Life
Daily ConversationUniversity StudentsWorking Adults
C12
78 phrases
The last real programmers are dying out...
Daily ConversationUniversity StudentsWorking Adults
C12
182 phrases
We Let $200 GPT-Pro Think for 7 Days on Unsolved Math
Daily ConversationUniversity StudentsWorking Adults
C11
346 phrases
Trust the Process: How to Design Teams That Actually Work
Daily ConversationUniversity StudentsWorking Adults
C12
272 phrases
How I'd Learn n8n if I had to Start Over in 2026
Daily ConversationUniversity StudentsWorking Adults
Vocabulaire et conseils d’expression pour cette leçon
Cette leçon d’expression orale de niveau B2 s’appuie sur la vidéo « Most devs don't understand what agents are ». Les mots qui reviennent le plus souvent : agent, workflow, Llm, loop, tool. Cette vidéo contient 106 phrases et 1632 mots à répéter en shadowing. La partie parlée dure 7:35. Le locuteur parle vite, environ 215 mots par minute : attendez-vous à des liaisons et à des sons réduits. 83 % des mots font partie des 3 000 mots les plus courants en anglais ; le reste mérite d’être vu avant de commencer.
Vocabulaire clé de cette vidéo
Les 15 mots les plus avancés de la vidéo, avec leur prononciation et leur sens :
| Mot | Prononciation | Sens |
|---|---|---|
| workflow nom | /ˈwɝkfloʊ/ | workflow |
| optimize verbe | /ˈɑptɪmaɪz/ | agir en optimiste |
| anthropic adjectif | /ænˈθɹɒp.ɪk/ | anthropique |
| deterministic adjectif | /dɪˌtɜː.mɪˈnɪs.tɪk/ | déterministe |
| predetermine verbe | /ˌpɹiːdɪˈtɝ.mɪn/ | prédéterminer |
| agentic adjectif | /eɪˈd͡ʒɛn.tɪk/ | en mode mission |
| summarize verbe | /ˈsʌməˌɹaɪz/ | résumer, récapituler |
| execute verbe | /ˈɛksɪˌkjuːt/ | exécuter, mettre à mort |
| distinction nom | /dɪˈstɪŋkʃən/ | distinction, différence |
| dive verbe | /daɪv/ | nager, plonger |
| qualify verbe | /ˈkwɑ.lɪ.faɪ/ | caractériser, qualifier |
| developer nom | /dɪˈvɛləpɚ/ | concepteur, développeur |
| codebase nom | codebase | |
| codify verbe | /ˈkɑ.dɪˌfaɪ/ | codifier |
| contrarian nom | /kənˈtɹɛ.ɹi.ən/ | anti-conformiste, contradicteur |
Les verbes à particule que vous entendrez
| Mot | Prononciation | Sens |
|---|---|---|
| end up verbe | se terminer, se finir | |
| set up verbe | /ˌsɛt ˈʌp/ | mettre en place, installer |
| turn out verbe | se finir, se terminer |
La grammaire de cette vidéo
Les structures que le locuteur utilise le plus, avec les mots exacts de la vidéo :
| Structure | Dans la vidéo |
|---|---|
| Voix passive be + participe passé — l’accent est mis sur ce qui arrive, pas sur qui le fait | are known · is known · is predetermined |
| Present perfect have/has + participe passé — une action passée qui compte encore maintenant | have predetermined · I've done |
Prononciation à surveiller
Le locuteur utilise 24 contractions et formes réduites, comme I'm, you're, doesn't. Prononcez-les sous leur forme courte, telles que vous les entendez.
- Les sons « sh » et « zh »: distinction /dɪˈstɪŋkʃən/, directional /dɪˈɹɛk.ʃə.nəl/, improvisation /ɪmˌpɹɑvəˈzeɪʃən/, mash /mæʃ/, optimization /ˌɑptəmaɪˈzeɪʃən/
- Mots longs — placez bien l’accent: deterministic /dɪˌtɜː.mɪˈnɪs.tɪk/, predetermine /ˌpɹiːdɪˈtɝ.mɪn/, developer /dɪˈvɛləpɚ/, contrarian /kənˈtɹɛ.ɹi.ən/, directional /dɪˈɹɛk.ʃə.nəl/
Les sons difficiles pour les francophones :
- /tʃ/ et /dʒ/ — à ne pas adoucir en « ch » et « j »: agentic /eɪˈd͡ʒɛn.tɪk/, generalize /ˈd͡ʒɛn.(ə.)ɹə.laɪz/, jailbreak /ˈd͡ʒeɪlˌbɹeɪk/, pedagogical /ˌpɛdəˈɡɑd͡ʒɪkəl/, chunk /t͡ʃʌŋk/
- /r/ anglais — langue recourbée, sans frotter la gorge: anthropic /ænˈθɹɒp.ɪk/, predetermine /ˌpɹiːdɪˈtɝ.mɪn/, summarize /ˈsʌməˌɹaɪz/, contrarian /kənˈtɹɛ.ɹi.ən/, directional /dɪˈɹɛk.ʃə.nəl/
Comment s’entraîner avec cette vidéo
- Écoutez la vidéo en entier une fois sans parler et notez les mots que vous ne connaissez pas.
- Commencez à la vitesse 0,75×, répétez phrase par phrase, puis revenez à la vitesse normale quand cela devient facile.
- Enregistrez-vous et comparez avec l’original, en faisant attention à des mots comme workflow, optimize, anthropic.
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.