Is the Prompt Era Over? The Shift That Changed How We Use AI

A while ago, one question was repeated everywhere.

What is the best Prompt?

Thousands of videos, hundreds of courses, and thousands of ready-made templates appeared, promising you better results from ChatGPT, Claude, and others.

For a long time...

Everyone was looking for the same answer.

How do I write a Prompt that makes AI do what I want?

But...

Maybe the question itself was the problem.

📌

If you use AI in your work or study, bookmark this article. Because the way we are used to using AI is changing rapidly, and this article will explain why.

Today, the most important question is no longer:

What is the best Prompt?

Instead, it has become:

How do I make AI achieve the goal?

The difference between the two questions might seem simple.

But it has completely changed how AI is built and used.

When the Prompt Was the Hero

In the beginning, most AI uses were simple.

You ask a question...

It answers.

You ask for a translation...

It translates.

You ask for a summary...

It summarizes.

You ask to write an email...

It writes it.

The relationship between you and the model was very direct.

Prompt → Response

Everything depended on the quality of the command you wrote.

If the prompt was vague, the answer was vague.

If it was clear, the result was usually better.

This is why an entire field called Prompt Engineering emerged.

Everyone started looking for ways to make the model understand the requirement the first time.

And that wasn't wrong.

At that time, it was the right approach.

But there was a problem...

No one noticed it.

Imagine you asked AI to build a website for you.

It wrote the code.

Then you discovered the code doesn't work.

What next?

Do you write a new Prompt?

Then if another error appears, do you write a third Prompt?

A fourth?

A fifth?

In the end, you'll find yourself spending more time managing the conversation than completing the task itself.

Here, the first sign appeared that the model, no matter how good its answers were, still depended on you at every step.

It was waiting for you to tell it:

Read this error.

Then...

Modify this file.

Then...

Try again.

Then...

There is a new problem.

You weren't completing the task...

You were managing every step of it.

💡

The prompt was excellent at producing answers... but it wasn't designed to complete tasks.

This is the difference that wasn't clear at first.

Giving an answer...

Is one thing.

Completing an entire task...

Is something else entirely.

From Giving Commands... to Defining Goals

Suppose you want to build an online store.

In the traditional way, you might start with this prompt:

Build me a homepage using React and Tailwind.

The model will create the page.

Then you discover you need a products page.

So you write a new Prompt.

Then a login page.

Then a dashboard.

Then connecting the API.

Then fixing a login error.

Then improving the design.

Then fixing a new problem that appeared after the modification.

Every step needs a new Prompt.

Every time, you are the one deciding the next step.

In other words...

You are the one leading the work entirely.

AI is just an executor of commands.

Now imagine a different way.

Instead of asking it to create one page, you said to it:

I want a ready-to-work online store, and these are the requirements.

What will happen?

If the system is designed in the modern way, it won't start writing code directly.

Instead, it will first stop to understand the goal.

Then it sets a plan.

Then it starts execution.

And if it faces a problem...

It won't wait for a new Prompt from you.

It will try to solve it itself.

If the first attempt doesn't succeed...

It will try a second time.

And maybe a third.

Until it reaches the desired result, or asks you for help when it becomes truly necessary.

Here the real difference appears.

In the old way, you managed every step.

In the modern way, you manage only the goal.

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The more prompts you need to write to complete a single task, the more likely it is that you are the one working... not the AI.

This is why many modern tools seem "smarter," even though they might use the same language model.

The difference wasn't always in the model...

But in the way it is managed.

This point was the beginning of a major shift in the world of AI.

Because the problem was no longer: "How do I write a better command?"

It became:

How do I make the system work until it reaches the goal, instead of waiting for a new command from me after every step?

Why do tools like Cursor and Claude Code look different?

If you used ChatGPT to write code, you've likely gone through this scenario.

You ask it to create a new feature.

It writes the code.

You try running the project...

An error message appears.

You copy the error message.

You paste it into the conversation.

It suggests a modification.

You try again.

Then a different error appears.

You repeat the process from the beginning.

If you repeat this scene several times, you'll discover something important.

AI wasn't solving the problem... it was waiting for your instructions after every step.

Now try a tool like Claude Code or Cursor.

You'll notice it handles the matter differently.

It reads the project files.

It looks for the location of the problem.

It modifies the code.

It runs the tests.

And if one of them fails...

It reads the reason for the failure.

Then it tries to fix it.

All this happens before you ask it for something new.

At first glance, you might think the reason is that it has a smarter model.

But in many cases...

The same model is used in more than one tool.

So where is the difference?

The difference is not in the mind...

But in the way this mind is managed.

Imagine you have a new employee.

You told him:

I want a report on this month's sales.

If he only executes commands, he will reply to you after every step.

Where do I get the data from?

Do I use this file?

Do I put a graph?

Do I send the report as a PDF or Word?

As for the expert employee...

He will understand the goal.

Collect the data.

Analyze it.

Review it.

Then return to you with the report ready.

You didn't tell him every step.

You told him what you want to reach.

This is exactly what modern AI tools are trying to do.

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AI didn't become smarter because it writes better code... but because it became aware of what to do after writing the code.

Here the concept of using AI began to change.

It was no longer just a question and answer.

It became closer to a complete workflow.

Understand.

Execute.

Review.

And decide the next step.

Here an idea appeared that will change everything we are used to...

That AI doesn't always need a new Prompt... sometimes it just needs a chance to complete the work.

The Prompt is Not Dead... But It's No Longer the Hero

After reading the previous parts, an important question might come to your mind.

Is the prompt era really over?

The answer is simply...

No.

The prompt is still important.

In fact, it is the first step in any interaction with AI.

But the problem is that many people still deal with it as if it were everything.

And that is no longer true.

Suppose you asked AI:

Create an online store for me.

This is the prompt.

But it's not what will determine the success of the task.

The more important questions are:

• Did it really understand the goal?
• Can it access the project files?
• Can it run the code?
• Can it read errors?
• Does it have the necessary tools to fix them?
• And if the first attempt fails... will it try again?

Notice that all these questions are not related to the prompt itself.

But to what happens after the prompt.

Here lies the difference.

In the past, the prompt represented 90% of the experience.

Today...

It might be just 10%.

The remaining 90% is the way the system works after it understands what you want.

For this reason, you might write the same prompt in two different tools...

And get two completely different results.

Not because one of the tools understood the words better.

But because each tool has a different way of executing the task.

💡

The prompt defines the starting point... but the execution method defines the finish line.

Here, what developers learn also began to change.

In the past, most advice revolved around:

• Use these words.
• Start the prompt with this sentence.
• Add this template.
• Don't forget to define the role.

Today...

The questions have become different.

• How do I make the system understand the goal?
• How do I provide it with the information it needs?
• How do I give it the right tools?
• And how do I make it review its work before considering the task finished?

Notice that the focus shifted from formulating the request...

To designing the workflow.

This is the shift that explains why the term Prompt Engineering alone is no longer enough to describe what is happening today.

Because AI is no longer just a model waiting for a new command...

It has become a system working to achieve a goal.

If the Prompt is No Longer the Hero... Who Is?

There is a mistake many people make.

They think that progress in AI always means a stronger model.

A model that understands more.

Writes better.

And answers with higher accuracy.

But the reality is different.

Often...

The model itself hasn't changed.

What changed is the system it works within.

Imagine you have the best chef in the world.

If you put an empty kitchen in front of him...

He won't be able to prepare a meal.

Not because he doesn't know how to cook.

But because he doesn't have the tools.

Now give him a full kitchen.

Refrigerator.

Oven.

Ingredients.

And a clear recipe.

He will provide a completely different result.

The chef is the same.

But the environment he works in has become better.

This is what happens with AI models.

The language model is like the mind.

But it cannot do anything alone.

To accomplish a real task, it needs other things.

It needs to know where to find information.

It needs tools to help it execute.

It needs a memory that keeps track of what happened.

And it needs a way to review what it accomplished before moving to the next step.

For this reason, competition is no longer about who has the best model only.

But about who builds the best system around this model.

Take a simple example.

If you asked an AI model:

Summarize my company's latest sales.

It doesn't even know where the sales data is located.

But if it is connected to the database or the company system...

It will be able to access it.

Then analyze it.

Then write the report.

The difference here was not in the formulation of the prompt.

Nor in the intelligence of the model.

But in that the system became capable of accessing what it needs to complete the task.

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The model is the mind... but the mind alone does not complete the work.

This is why concepts that didn't receive much attention years ago started to appear.

We started talking about:

• Memory.
• Tools.
• Context.
• Connection to other systems.
• And working in stages instead of one step.

All these elements didn't come to replace the prompt.

They came to complete what was missing.

Because the prompt can tell AI what you want...

But these elements are what help it know how to reach it.

Here began the transition from just "writing commands" to building systems capable of executing tasks, which paved the way for the emergence of a new concept that has dominated the AI world recently:

AI Agents.

The Secret That Made AI Work Like a Human

Imagine you asked a developer to build an app.

Do you expect him to write it completely from the first attempt?

Of course not.

He will write part of the code.

Then run it.

He will discover an error.

So he fixes it.

Then he tries again.

Then he reviews the result.

And he might repeat this cycle dozens of times before considering the task finished.

This is not a sign that he failed.

This is the natural way to complete any complex work.

The strange thing is that we expected AI to work in a different way.

We write one Prompt for it...

And wait for it to come out with the final result from the first attempt.

When that doesn't happen, we think the model is not good enough.

While the problem was in our expectations.

Real work doesn't happen in one step.

But in a series of small attempts.

Execution.

Review.

Correction.

Re-execution.

Then review again.

This repeated cycle is what is known as a Loop.

In its simplest form, it can be imagined like this:

Notice something important.

In this diagram...

There is no new Prompt after every step.

The goal was written once.

Then the system started working inside the loop until it reaches the desired result.

This is the fundamental difference.

Take an example from modern programming tools.

You ask the tool to add a new feature.

It writes the code.

Then it runs the project.

An error appears.

It reads the error.

And modifies the code.

Then it runs the project again.

Then it repeats the test.

If it succeeds...

It moves to the next step.

If it fails...

It repeats the cycle again.

All this might happen without you writing any additional Prompt.

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The Prompt defines the goal... while the Loop is what leads the journey to reach it.

For this reason, when you hear that a tool like Claude Code or Cursor "thinks" before it replies, it is actually not performing magic.

It just doesn't stop after the first answer.

It continues in a cycle of:

Execution → Review → Correction → Retry.

Exactly as any developer or engineer does during their work.

Here we can understand why the term AI Agent has become so widespread.

Because a smart agent is not just a smarter language model...

But a system that has the ability to work inside this loop continuously until it gets closer to achieving the goal.

This is the real difference between a conversation with AI... and working with it.

What is an AI Agent really?

When many people hear the term AI Agent, they imagine it is a more advanced AI model.

Or a new version of ChatGPT.

Or a robot that can do more things.

But the truth is different.

An AI Agent is not a new model.

But a new way to use models.

And that is a big difference.

Imagine you have a new employee in the company.

If you ask him:

What is the capital of Japan?

He will answer:

Tokyo.

And that's it.

But if you say to him:

I want to launch a new product within a month.

Will he settle for one answer?

Of course not.

He will start asking questions.

He will collect information.

He will set a plan.

He will execute part of it.

Review the results.

Change the plan if he needs.

Then continue.

All that before he returns to you.

This is the human way of working.

For this reason, AI systems have started to adopt the same style.

You can imagine the AI Agent as a small project manager.

Not because he knows everything...

But because he knows what the next step is.

When a step ends...

He doesn't wait for a new Prompt.

Instead, he asks himself:

What should I do now to get closer to the goal?

Then he continues.

This is why most modern AI Agents do not depend on one element.

Instead, they consist of several parts working together.

Notice that the prompt didn't disappear from this diagram.

It became just the beginning.

After that, the system starts thinking, working, and making decisions.

For this reason, when you use a real Agent, you might feel it is "thinking."

But what happens in reality is simpler than that.

It doesn't think in the human way.

Instead, it repeats a very organized cycle:

Understand.

Decide.

Execute.

Evaluate.

Then decide the next step.

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An AI Agent is not a different artificial intelligence... it is artificial intelligence given the ability to continue working until it achieves the goal.

This explains why the phrase "write me a strong prompt" has become less important than it was.

Because the question occupying companies and developers today is no longer:

How do I make the model answer?

Instead, it has become:

How do I make the system work on its own for as long as possible before it needs my intervention?

This question is what drives the future of AI, not the search for a magic prompt.

How does AI see the world around it?

So far, we've talked about how an AI Agent can plan, execute, review, and retry.

But there is an important question.

How does it even know what it is working on?

If you ask it to analyze a file on your device...

Or read a project on GitHub...

Or search inside Notion...

Or extract data from a database...

Where will it get all this information from?

The answer might seem simple.

It can't.

At least...

Not on its own.

A language model, no matter how smart, cannot automatically access your files, apps, or databases.

It needs a secure and organized way that allows it to communicate with these tools.

Here, an important concept in the world of AI appeared called MCP, which is an abbreviation for Model Context Protocol.

Simply, it can be considered a common language between AI and the tools it uses.

Instead of every app learning a different way to communicate with GitHub, Notion, or databases...

MCP provides a unified way that makes the model deal with all these tools in one style.

For this reason, when you see a tool like Claude Code reading your project files, searching inside GitHub, or modifying files on your device...

This doesn't mean the model has become "seeing" your device in a magical way.

But because it uses a protocol that allows it to access these sources in an organized and secure way.

💡

If the prompt defines the goal... then MCP opens the door for AI to access the information and tools it needs to achieve that goal.

📊 The idea can be simplified this way

Why are all companies moving towards this style?

If you followed AI news recently, you'll notice something striking.

Almost every company is talking about building Agents.

And not about building a new Chatbot.

At first, it might seem like just a change in terminology.

But the truth is the reason is very practical.

Imagine you have a team of 20 employees.

Every five minutes, one of them comes to ask you:

• What do I do now?
• Do I continue?
• Do I wait?
• Do I change this part?
• Do I start the next task?

After a few hours...

The problem won't be in the efficiency of the employees.

But in that all the work is dependent on a decision from you.

Now imagine the other scenario.

Every employee knows the goal.

Knows how to start.

And knows how to review his work.

And doesn't return to you unless he faces a real problem he cannot solve.

Which of the two teams will accomplish more?

The answer is clear.

For this reason, companies are trying to build systems that need the least possible amount of human intervention.

This doesn't mean they want to replace humans.

But they want to stop interrupting them every minute.

There is a big difference between asking AI to execute 50 steps...

And explaining the goal to it once, then leaving it to work until it reaches the best possible result.

Every additional Prompt you write...

Is a sign that the system stopped, and is waiting for new instructions.

As for the good system...

It can continue alone for as long as possible.

💡

Success is no longer measured by the number of prompts you write... but by the number of prompts you no longer need to write.

This sentence summarizes a large part of the shift we are living today.

For this reason, tools have started to appear that can:

• Read the entire project files.
• Search inside thousands of lines of code.
• Run tests.
• Analyze results.
• Fix errors.
• Retry automatically.

All that before you write any new Prompt.

Notice that these tools are not trying to be "smarter" in the reply.

But they are trying to be "more independent" in the work.

This is the direction the entire industry is moving towards.

But here an important question appears.

If AI becomes capable of executing tasks in this way...

What will remain for humans?

This is the question that worries many people.

But it actually leads to one of the most important skills in the AI era.

The Skill That Will Become More Important Than Writing Prompts

For a long time, the person who writes good Prompts was seen as the most professional in using AI.

But if you contemplate the way modern tools work...

You'll discover that the value is no longer in writing the command.

But in defining the goal.

There is a big difference between the two.

Imagine a manager said to his employee:

Write me a report.

This is a command.

But is it enough?

Usually, the employee will return with dozens of questions.

Which report?

For whom?

About which period?

And what is the goal of it?

Now imagine he said to him:

I want a report that helps management know the reason for the decline in sales during the last three months, with practical suggestions to increase sales.

Notice the difference.

The second sentence was not just longer...

It was clearer in the goal.

When the goal becomes clear...

Making decisions becomes easier.

The same thing happens with AI.

The clearer the goal...

The system becomes capable of choosing the appropriate steps itself.

But if the goal is vague...

It will be forced to stop often, or it will make wrong decisions.

For this reason, a new skill has started to emerge strongly.

Not the skill of writing prompts.

But the skill of designing the goal.

To know:

• What do you want?
• What is the final result you are looking for?
• What are the constraints that must be respected?
• And how do you know that the task ended successfully?

These questions have become much more important than searching for the "best Prompt."

💡

AI doesn't always fail because it didn't understand the command... but because it didn't understand the goal.

For this reason, when you see someone spending a whole hour improving a Prompt...

Ask yourself a different question.

Is the problem really in the prompt... or is the goal itself unclear?

In many cases, you'll discover that rewriting the goal entirely gives a better result than modifying the prompt ten times.

This is why many companies are no longer looking for people who are good at using ChatGPT only.

Instead, they are looking for people who can transform a real problem into a clear goal that AI can work on.

This skill is not for developers only.

But the manager needs it.

And the marketer.

And the designer.

And the researcher.

And the content creator.

Because the future won't be for those who write the best Prompt...

But for those who know what they want to achieve in the first place.

Mistakes That Have Become Common... Because They Belong to the Old Way

After this shift, it has become easy to notice some habits that many people still practice.

Not because they are wrong...

But because they were suitable for a different stage.

Mistake One: Searching for the "Magic Prompt"

If you search the internet, you'll find thousands of titles like:

• Best Prompt for writing articles.
• Best Prompt for programming.
• Best Prompt for marketing.
• Best Prompt for anything.

As if there is a magic sentence that will make AI solve all your problems.

But the reality is different.

The same Prompt might work wonderfully in one project...

And fail completely in another project.

Not because the model became worse.

But because the problem itself is different.

Every project has a different goal.

And has different data.

And has different tools.

And has a different context.

Therefore, there is no Prompt that fits everything.

Mistake Two: Believing That the Length of the Prompt Means the Quality of the Result

Some believe that adding more instructions always means better results.

So you find a prompt of a thousand words...

Asking the model to be an expert, a consultant, a reviewer, an auditor, and a designer at the same time.

But many instructions do not necessarily mean clarity of the goal.

Sometimes...

A Prompt of three clear sentences achieves a better result than an entire page full of unimportant details.

The question is not:

"How much did you write?"

The question is:

"Did you convey the goal clearly?"

Mistake Three: Believing That the Model Knows Everything

The model doesn't know your project.

And doesn't know your files.

And doesn't know how your company works.

And doesn't know what happened in last week's conversation, unless you provide it with this information.

For this reason, Contexts, memory, and tools have become an essential part of any modern system.

The problem is not that AI doesn't understand.

The problem is that it cannot depend on information it didn't receive.

Mistake Four: Stopping at the First Result

This is one of the most widespread mistakes.

The person writes a Prompt.

Gets the first answer.

Then considers that the task ended.

While modern tools deal with the first answer as a beginning and not an end.

If the result needs improvement...

It improves it.

If an error appears...

It tries to fix it.

If it fails...

It tries again.

This is the difference between a system that answers...

And a system that works.

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The best AI users today are not those who write the longest prompts... but those who build the best environment that helps AI succeed.

For this reason, the question worth asking yourself has become different.

Instead of asking:

How do I write a better Prompt?

Ask:

What does AI need to succeed in this task without me intervening in every step?

This question alone... changes your way of using AI completely.

What will the way of using AI look like during the coming years?

It is difficult to predict accurately what will happen.

But if you look at the way tools are developing today, you'll notice a clear pattern.

AI is not just changing...

But the way we use it is what is changing.

In the beginning, we dealt with it as we deal with a search engine.

We ask...

It answers.

Then we finish.

After that, we started asking it to write articles, translate texts, create images, and write codes.

But this also was no longer enough.

Today, tools have started to deal with AI as an executor of tasks, and not just an answerer of questions.

This direction will increase in clarity.

It is likely that you will spend less time writing prompts.

And more time defining:

• What do you want to achieve?
• What result do you consider a success?
• And what are the limits that the system must not exceed?

Then you leave it to work.

Instead of saying:

Write this code.

You will say:

Add a login system to this project, use best practices, and make sure the tests pass before you finish.

And instead of saying:

Write an article for me.

You will say:

Write an article targeting developers, in a practical style, with real examples, then review the language, and suggest a better title if you find an opportunity for that.

And instead of saying:

Analyze this data.

You will say:

Analyze the data, look for unusual patterns, then provide a report with practical recommendations.

Notice something important.

In all these examples...

You didn't define the steps.

You defined the result.

As for execution...

It became part of the system's responsibility.

💡

The more AI tools develop, our interest in how to execute the task will decrease... and our interest in defining the result we want to reach will increase.

This will change the role of the human as well.

Today, many users spend their time directing AI step by step.

In the future, it is likely that they will spend their time in:

• Defining goals.
• Reviewing results.
• Making decisions.
• And intervening only when their human experience becomes necessary.

In other words...

The human will gradually move from executor of work...

To manager of work.

This doesn't mean that AI will work alone always.

There will still be tasks that need human experience, human judgment, and creativity that cannot be reduced to instructions.

But the way of cooperation will change.

Instead of leading AI in every step...

You will set the destination for it.

Then monitor its journey to it.

Maybe this is the best description of the shift we are living today.

We no longer use AI as a machine that writes answers for us...

Instead, we have started to deal with it as a work partner that can execute a large part of the task, while the human remains responsible for defining the goal, making the decision, and judging the quality of the result.

This is the direction that seems to shape the way of using AI during the coming years.

How do you prepare for this shift?

If you reached here, you have become aware that the way of using AI is changing.

But the most important question remains...

What should you learn?

If your goal is to keep pace with this shift, here are five skills I believe are worth your time more than memorizing hundreds of prompts.

1. Learn how to define the goal before you write the Prompt

Don't start with the question:

What do I write?

Start with the question:

What do I want to achieve?

The clearer the goal...

The easier it was for any AI system to reach it.

For this reason, spend time thinking about the final result, more than thinking about formulating the first sentence.

2. Learn how to provide the right context

Even the smartest models cannot work with information they don't have.

If you want AI to help you in your project...

It's not enough to tell it the task.

Instead, provide it with the files, information, requirements, and constraints it needs.

The better the context...

The better the decisions it makes.

3. Get to know the tools that work around the model

AI today doesn't work inside a conversation window only.

There are tools that can read files, run codes, connect to databases, search the internet, and manage projects.

The more you understand this ecosystem...

You become capable of building solutions larger than just a conversation.

4. Understand how Loops work

Not every answer is the final result.

Often, the best results come after several attempts.

Therefore, don't look at the Loop as just a technical concept.

Instead, consider it a way of thinking.

Execute.

Review.

Improve.

Then repeat.

Whether you are working with AI...

Or working alone.

5. Don't stop at using tools... understand why they work

Dozens of new tools might appear today.

And tomorrow half of them might disappear.

But the principles they depend on will remain.

If you understand why these tools depend on goals, memory, context, tools, and Loops...

You won't have to start from scratch every time a new tool appears.

You will learn the tool...

Because you understood the idea.

💡

Tools will change... and models will develop... but understanding the principles will remain the best investment.

In the end, don't make your goal to become a person who is good at using ChatGPT.

Make your goal to become a person who knows how to get things done using AI.

Because the difference between the two will increase in clarity with every new generation of tools.

Conclusion: The question that changed... changed everything

At the beginning of this article, we started with a simple question.

What is the best Prompt?

And this was a logical question.

Because we were dealing with AI as a tool for answering questions.

But with the development of tools, this is no longer the most important question.

The question worth asking today is:

"How do I build a system that can achieve the goal with the least intervention from me?"

This is the essence of the shift we are living.

If you wanted to summarize the article in one sentence, maybe this would be the best formulation:

We didn't move from Prompt to AI Agents... we moved from giving commands to achieving goals.

For this reason, the prompt is no longer the end of the process.

Instead, it has become its beginning.

After it comes the plan.

Then execution.

Then review.

Then correction.

Then repetition.

Until the result is achieved.

The names of the tools might change.

And stronger models might appear.

And tools we use today might disappear.

But the basic idea will remain as it is.

Every new generation of AI tools will try to ask you for fewer instructions...

And provide you with more results.

For this reason, the best investment you can make today is not collecting hundreds of prompts.

But understanding how these systems work, how they think, how they make their decisions, and how they transform from a model that answers questions to a system that completes tasks.

💡

The prompt is not dead... but it is no longer the hero. The real hero today is the goal.

When the goal is clear...

And AI has the right context...

And the right tools...

And can review its work and retry...

The question won't be: "What is the best Prompt?"

Instead, it will be:

What do I want to achieve?

Maybe this is the most important change in the way of using AI since its appearance.

📌 If you found that this article changed your way of thinking, bookmark it.

Not because it contains a set of prompts...

But because it explains the idea on which most modern AI tools are built, an idea that will remain useful even with the appearance of new models and tools.

And if you have a different opinion, or see that there is another shift I didn't address, tell me in the comments. I will be happy to read and discuss it.

Prepared and written by: Adel Ahmed

X:@AdelDeveloperX

💙 If you benefited from the article, don't forget to bookmark it and share it with your friends, as this might be the first guide to help them enter the world of AI Agents in a practical and correct way.