शैडोइंग अभ्यास: 900+ hours of Learning AI Agents in 9 Minutes - वीडियो के साथ अंग्रेजी बोलना सीखें

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हर मोड पूरी स्क्रीन का अभ्यास कक्ष खोलता है।

148 वाक्य

यदि वाक्य बहुत छोटे या लंबे हों, तो समायोजित करने के लिए Edit पर क्लिक करें।

AI agents can already write code, use tools, browse files, and run commands.

So why do they still fail at real engineering tasks?

This is because writing code is only one part of the problem.

The harder part is controlling what the agent does after that first step.

How do you keep one agent reliable?

How do you let it work for hours without supervision?

And how do you coordinate dozens of agents on one massive task?

That's where these three ideas come in.

Harness engineering, loop engineering, and graph engineering.

In this video, I'll break down all three, how they fit together, and when each one actually makes sense.

Hi friends, I'm Maddie.

I'm a senior software engineer who previously worked at Google and interned at other big tech companies like Amazon, IBM, and Microsoft.

In this video, I'll talk you through what harness, loop, and graph engineering mean, how they fit together, and when each is actually worth using.

Let's get into it.

We're going to start with harness engineering.

Harness engineering is the work of designing the agent harness, or everything around the model so the tools it can use,

what goes into its context window, the rules it follows, and the checks it has to pass.

An agent is, simply put, a model plus a harness.

The model is whatever the lab ships, so the harness is the part you actually get to customize.

Here's what that looks like.

Say you're building a budgeting app.

You ask a coding agent to fix a bug where recurring expenses get counted twice,

and it tells you it's done while the tests are still failing.

To fix this, Harness Engineering treats it as a design problem.

You give the agent a tool that runs the test, a rules file that says the task isn't finished until they pass,

and a check that blocks the done message until the test pass.

Now, that mistake can't happen again on this bug or the next one.

Mitchell Hashimoto, the co -founder of HashiCorp, started calling this

Harness Engineering, described simply as this.

Every time your agent makes a mistake, you change the system around it so it can't make that mistake again.

Langchain reported moving their coding agent from around 30th place into

the top five on the Terminal Bench 2 .0 benchmark while keeping the model the exact same.

So all that improvement came from harness engineering.

And in practice, the work falls into four areas you can modify.

Number one, tools, what the agent can do.

Number two, context, what it sees at each step and what you keep out.

Number three, your project's conventions, written down once so it stops guessing.

And number four, feedback.

Tests, linters, and reviewers that tell it when it's wrong.

The tools area includes connections.

An agent can only act on what it can reach, an MCP.

The model context protocol is a standard way a harness plugs into outside tools and data.

And one connection worth adding to your harness is Granola, the sponsor of today's video.

Let's say you just finished a planning call for that budgeting app and you made a bunch of decisions on it.

Normally, your coding agent knows none of them.

But with Granola connected, you can type one line.

So pull the decisions and action items from today's call and turn them into tasks.

And a minute later, you have a task list with exactly what you agreed on.

So for example, your agent now knows that the duplicate expense bug has to be fixed before the export feature ships.

Granola is the AI notepad for people in back -to -back meetings.

It transcribes your computer's audio directly, so nothing extra has join your call.

And it works across

Zoom, Google Meet, Slack, and in -person conversations.

You can optionally also jot a few notes down like you normally would,

and Granola builds a clean summary with the decisions and action items around them.

And what makes that one -line request work is Granola's MCP connector.

You connect Granola to Claude, ChachiPT, or whichever agent you already use, and your meeting context becomes one more thing your harness can reach.

So your agent works from what was actually said in the meeting and you don't have to manually paste anything in.

And with speaker tags on Zoom and Google Meet, you can ask who said what, like what did

Sam say about the launch timing?

If you're a new user, you can get 100 % off your first month through my link in the description.

Okay, now let's get back to the second type of engineering.

So, harness engineering was about one agent doing one task well.

Loop engineering, on the other hand, asks a different question.

Who starts the task?

So, remember our budgeting app.

Right now, nothing happens until you sit down and type the prompt.

You are the trigger.

So every bug fix, every dependency update, every failing test waits for you to notice it.

In June, Adi Azmani on the Cloud Code team at Anthropic published an essay that defined loop engineering as the following.

You replace yourself as the person who prompts the agent, and you design the system that does it instead.

Boris Cherney, who leads

Cloud Code Anthropic, says his job now is to write loops.

And Peter Steinberger, the founder of OpenClaw, said developers should be designing the loops that prompt their agents.

Osmani breaks a loop into five building blocks plus memory.

The first block is automations, which run on a schedule and go find the work.

Then we have work trees, so parallel agents can't overwrite each other's files.

Third, we have skills, which are your project knowledge written down once.

Fourth, we have connectors, which is the MCPP suite just covered.

And fifth, sub -agents.

So the agent that writes the code is never the one verifying it.

And we have a state file on disk because...

Every run starts with a blank memory, so progress has to live somewhere outside the conversation.

For the budgeting app, a loop could look like this.

Every morning, an automation reads last night's failed tests and new bug reports.

For each one worth fixing, it opens an isolated work tree.

One agent drafts a fix, a second agent reviews it gets your test, and a progress file records what passed.

You wake up to PRs waiting for your review.

There are two things I want you to keep in mind here.

First, the hard part of loop is actually the stop condition.

Keep going until every test in the auth folder passes and lint is clean is something a machine can check.

Make the code better is not.

Second, Osmani cautions in the same essay that he's skeptical.

If no one is actively watching the lube, no one is watching its mistakes either.

And the faster it ships code you didn't write, the wider that chasm gets between what exists and what you actually understand.

He calls this comprehension debt.

Finally, let's talk about the newest term, graph engineering.

As you probably learned in a math or data structures class, a graph is nodes connected by edges.

In an agent system, nodes are where work happens, regular code, a single monocle, or an entire agent.

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Edges decides what runs next, what runs in parallel, and who checks who.

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Take that budgeting app one more time.

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Let's say you decide to migrate the whole thing from JavaScript to TypeScript.

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A single agent working through 200 ,000 order would be slow and might lose track halfway.

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So instead, you can shape the work as a graph.

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For example, one node maps the code base and splits it into chunks.

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That fans out to many agents converting files in parallel.

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Each result goes to a reviewer node, and anything that fails review travels back along an edge for another attempt,

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and a final node runs the full test suite.

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This is the design behind dynamic workflows in Cloud Code.

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Dynamic workflows have clawed write -in orchestration script

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that spreads work across tens to hundreds of subagents and checks the results before they reach you.

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As an example, Jared Sumner used dynamic workflow to port a giant database from Zig to Rust.

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This was roughly 750 ,000 lines of Rust and took 11 days from first commit to merge.

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I also want to talk about Langchain's take specifically on graph engineering.

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Earlier this year, Langchain published a post called Three Years of Graph Engineering.

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Their argument is that the idea of graph engineering itself is actually old.

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They've been modeling agents as graphs for about three years.

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A loop by their account is just a simple graph.

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But the new part of all this is the node.

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That node used to hold one model call.

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Today, it can hold a full agent you trust with real work.

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Langchain also warns that for open -ended tasks like deep research, where you can't predict the steps ahead of time,

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they actually moved away from a fixed graph and went back to a single agent running in a good harness.

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A graph really helps when you know the structure of the work, but when you don't, it just gets in the way.

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So what should you do with these techniques if you're early in your career?

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First, don't just memorize these vocabulary buzzwords.

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Actually understand what's happening.

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The names of the specific components within the different types of engineering and even the types of engineering themselves, whether it's harness, loop, graph,

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might keep changing, but a few underlying skills won't.

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One skill is writing a checkable definition of done, which means tests, evals, and stop conditions.

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Two is decomposition, deciding what can run in parallel and what depends on what.

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And three is verification, keeping the maker separate from the checker and reading what your agents produced.

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So let's put this all together.

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Harness engineering is designing everything around one model so it's reliable in one task.

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Loop engineering is designing a system that starts that work and keeps it going until a condition you wrote is true.

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And graph engineering is wiring many of those together, with rules for who runs when and who checks whom.

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Each of these builds onto the next, and each one costs more than the previous one.

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Anthropic published numbers on this when they wrote up how they built Claude's research feature.

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One agent burns roughly four times the token of a normal chat, and a team of agents burns roughly 15 times.

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So before you automatically decide to go with the more complex workflow, ask whether the task is valuable enough and parallel enough to pay for it.

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For example, a bug fix might need a harness, nightly maintenance might need a loop, and a thousand file migration might need a graph.

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So in conclusion, harness engineering makes one agent reliable, loop engineering keeps that agent running without you, and graph engineering coordinates many of them.

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They are different layers of the agentic build system, and the skills that carry across all three layers are defining done,

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breaking work down, and verifying the results.

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And as As a reminder, if you want your meeting context plugged into your own agent setup, check out Granola through my link in the description.

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And that's all I have for you in this video.

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If this was helpful, hit that like button, hype the video, and subscribe for more tech and AI content.

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Thanks for watching, I'll see you next one!

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अनुशंसित वीडियो

26 वाक्य

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इस पाठ की शब्दावली और बोलने के सुझाव

यह स्पीकिंग पाठ वीडियो “900+ hours of Learning AI Agents in 9 Minutes” पर आधारित है। सबसे ज़्यादा दोहराए गए शब्द ये हैं: agent, engineering, harness, graph, loop। इस वीडियो में शैडोइंग के लिए 148 वाक्य और 1928 शब्द हैं। बोला गया हिस्सा 9:54 लंबा है। वक्ता तेज़ बोलता है, लगभग 195 शब्द प्रति मिनट, इसलिए जुड़ी हुई और कमज़ोर ध्वनियाँ ज़्यादा मिलेंगी। केवल 81% शब्द अंग्रेज़ी के 3,000 सबसे आम शब्दों में से हैं, इसलिए शब्दावली कठिन है।

इस वीडियो की मुख्य शब्दावली

वीडियो के 15 सीखने लायक शब्द, उच्चारण और अर्थ के साथ:

शब्द	उच्चारण	अर्थ
graph संज्ञा	/ɡɹæf/	लेखाचित्र, ग्राफ़
loop संज्ञा	/luːp/	कुंडली
parallel विशेषण	/ˈpæ.ɹəˌlɛl/	समानान्तर
dynamic विशेषण	/daɪˈnæm.ɪk/	क्रियाशील, गतिशील
reliable विशेषण	/ɹɪˈlaɪəbl̩/	निष्ठावान
founder संज्ञा	/ˈfaʊ̯n.dəː/	संस्थापक, प्रवर्तक
layer संज्ञा	/ˈleɪ̯ɚ/	परत, तह
essay संज्ञा	/ˈɛs.eɪ/	निबंध
expense संज्ञा	/ɪkˈspɛns/	व्यय, लागत
verify क्रिया	/ˈvɛɹɪfaɪ/	परखना, जाँचना
automation संज्ञा	/ˌɔ.təˈmeɪ.ʃən/	स्वचालन
technique संज्ञा	/tɛkˈniːk/	तकनीक
helpful विशेषण	/ˈhɛlp.fl̩/	मददगार
wire संज्ञा	/ˈwaɪɚ/	तार
skill संज्ञा	/skɪl/	कुशलता, कौशल

वीडियो में आने वाली फ़्रेज़ल वर्ब

शब्द	अर्थ
sit down क्रिया	बैठना

दोहराने लायक वाक्य

वीडियो के छोटे, पूरे वाक्य जिन्हें आप रोज़ की बातचीत में इस्तेमाल कर सकते हैं:

How do you keep one agent reliable?
How do you let it work for hours without supervision?
We're going to start with harness engineering.
Say you're building a budgeting app.
Finally, let's talk about the newest term, graph engineering.

इस वीडियो का व्याकरण

वक्ता जिन संरचनाओं का सबसे अधिक प्रयोग करता है, वीडियो के असली शब्दों के साथ:

संरचना	वीडियो में
Relative clauses who / which + उपवाक्य — व्यक्ति या वस्तु के बारे में अतिरिक्त जानकारी	person who prompts · skills, which are · connectors, which is
Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है	be fixed · was actually said

ध्यान देने योग्य उच्चारण

वक्ता 20 बार संक्षिप्त रूपों का प्रयोग करता है, जैसे can't, don't, I'll। इन्हें वैसे ही छोटा बोलें जैसा आप सुनते हैं।

“th” की ध्वनियाँ: mathematics /mæθ(.ə)ˈmæt.ɪks/, authorization /ˌɔːθəɹaɪˈzeɪʃən/
“sh” और “zh” की ध्वनियाँ: automation /ˌɔ.təˈmeɪ.ʃən/, conclusion /kənˈkluːʒən/, migration /maɪˈɡɹeɪʃ(ə)n/, caution /ˈkɔːʃ(ə)n/, supervision /ˌs(j)uːpəˈvɪʒən/
लंबे शब्द — बलाघात सही रखें: coordinate /koʊˈɔɹ.dəˌneɪt/, automation /ˌɔ.təˈmeɪ.ʃən/, developer /dɪˈvɛləpɚ/, mathematics /mæθ(.ə)ˈmæt.ɪks/, supervision /ˌs(j)uːpəˈvɪʒən/

हिंदी भाषियों के लिए मुश्किल ध्वनियाँ:

/v/ और /w/ का फ़र्क़ — /v/ में ऊपर के दाँत निचले होंठ को छूते हैं, /w/ में होंठ गोल होते हैं: suite /swiːt/, verify /ˈvɛɹɪfaɪ/, wire /ˈwaɪɚ/, actively /ˈæktɪvli/, developer /dɪˈvɛləpɚ/
/z/ — इसे “ज” (/dʒ/) न बनाएँ: authorization /ˌɔːθəɹaɪˈzeɪʃən/, browse /bɹaʊz/, chasm /ˈkæzəm/, customize /ˈkʌs.tə.maɪ̯z/, decomposition /diːˌkɒmpəˈzɪʃən/
शब्द की शुरुआत में s + व्यंजन — पहले “इ” न जोड़ें: suite /swiːt/, skill /skɪl/, script /skɹɪpt/, sponsor /ˈspɒn.səː/, skeptical /ˈskɛptɪkəl/

इस वीडियो से अभ्यास कैसे करें

पहले बिना बोले पूरा वीडियो एक बार सुनें और जो शब्द नहीं आते उन्हें लिख लें।
0.75× गति से शुरू करें, एक-एक वाक्य की शैडोइंग करें, और सहज लगने पर सामान्य गति पर लौटें।
अपनी आवाज़ रिकॉर्ड करें और मूल से तुलना करें, ख़ासकर graph, loop, parallel जैसे शब्दों पर ध्यान दें।

शैडोइंग तकनीक क्या है?

शैडोइंग (Shadowing) एक विज्ञान-समर्थित भाषा सीखने की तकनीक है जो मूल रूप से पेशेवर दुभाषिया प्रशिक्षण के लिए विकसित की गई थी। विधि सरल लेकिन शक्तिशाली है: आप मूल अंग्रेज़ी ऑडियो सुनते हैं और तुरंत इसे ज़ोर से दोहराते हैं — जैसे वक्ता की छाया 1-2 सेकंड की देरी से। शोध से पता चलता है कि यह उच्चारण सटीकता, स्वर, लय, जुड़ी हुई ध्वनियाँ, सुनने की समझ और बोलने की प्रवाहशीलता में काफ़ी सुधार करता है।

शैडोइंग तकनीक: पूरी चरण-दर-चरण गाइड पढ़ें →

शैडोइंग अभ्यास: 900+ hours of Learning AI Agents in 9 Minutes - वीडियो के साथ अंग्रेजी बोलना सीखें

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