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What is No Code – All About No Code Development

what is in the code

Years ago, the only option Jeff Bezos and Mark Zuckerberg had to create Amazon and Facebook was to spend hours in their garage writing line after line of code.

Thankfully, technology evolves...

Today anyone can launch prototypes in 24 hours using no-code (zero code) tools, 1/10 of the traditional cost.

Have you ever had an app idea and given up because you didn't have a programmer? With no-code, you can launch it yourself without hiring a developer.

Or that day when something bothered you and you thought: if someone created a solution to this problem, it would make a lot of money

Maybe if you had access to this content earlier, that person could be you.

Thanks to technologies called no-code, you could have taken your idea from paper to reality without writing a single line of code.

Knowing what is in code will make you look at the world of programming with different eyes. 

If you like technology and development, but don't have an affinity for programming, understanding what no-code is will be the answer you've been looking for.

In this article you will understand a little more about it. 

After all, what is no-code?

No-code is visual development.You build screens, rules, and integrations using blocks, and publish to web/mobile without writing code. Tools like Bubble (web) and FlutterFlow (mobile/web) embody this model.

That is, no-code developers, instead of developing by writing lines of code themselves, use tools that allow visual development and develop based on logic that is much more understandable and similar to human language:

This is very much in line with the words of the CEO and founder of Bubble, one of the biggest no-code platforms today:

Why in today's world are people expected to learn and speak the language of computers?
Instead, shouldn't computers learn to speak our language?
?”
Emmanuel Straschnov – Founder and CEO of Bubble

  • Talent shortage: estimated global shortage of ~40M today is ~85M until 2030 (sources that compile data from BLS/Korn Ferry).
  • LC/NC Market: projection of US$ 187 billion by 2030, starting from ~US$ 10 billion in 2019.

I don't know about you, but to me this makes a lot of sense.

And how to do it? 

Tools like Bubble and FlutterFlow let you drag blocks, define logic, and publish (all from a single dashboard).

These tools facilitate the development process, breaking down barriers for those who want to work with application creation but don't know how to program. 

This opens up endless possibilities, as it allows anyone to develop their own application, bringing their idea to life without needing to hire a programmer. 

This greatly reduces development costs, as development time is shorter and also qualified IT professionals are often a very expensive workforce.

The no code movement

What is no-code

It started in the 90s with WYSIWYG website builders. The explosion came in 2018, when VCs poured millions into Bubble, Webflow and Airtable (accelerating enterprise adoption).

However, as you can imagine, at that time there was only a sketch of what it would be like today, with very limited platforms – but, for the time, it was a great advance. 

In 2018 there was a great expansion of the area and the tools used, causing the technological scenario to suffer a great revolution! 

That year there was a publication of extreme relevance for the medium, showing that several startups were investing in this development model – which is one of the reasons for their more agile solutions. 

The no-code is a simple, intuitive, efficient and agile solution that promotes a series of benefits for all companies (and even for people's daily lives). 

Why does the no-code matter today more than ever?

If you've been following the tech market a bit in recent years, you've probably heard about the difficulty companies have in hiring and retaining developers.

This is because, the exponential growth of companies using and creating their own applications and softwares lately, has greatly increased the demand for these professionals.

And since it takes time to make good developers, perfect chaos was created.

A high demand that doesn't stop growing (and won't stop growing for a long time), with a low supply that we have difficulty increasing due to the time it takes to train programmers.

What is the current deficit of devs in the world?

According to the US Labor Statistics the lack of developers amounted to 40 million workers in 2019. By 2030, the expectation is that this number will reach 85 million.

Generating a global loss of U$D8.4 trillion.

And companies have already felt the consequences of this strongly:

  • Salaries and benefits to be able to hire and retain such professionals have never been higher;
  • Application development costs keep growing;
  • Difficulty for small and medium-sized companies to enter this game;

There are thousands of companies with this pain, a world of opportunities.

And this is where no-code shines…

The advantage of no-code

  • Learn in months, not years
  • Reduce dev cost by up to 70 %
  • Cast 5-10× faster
  • Scale without hiring extra dev

This has drawn a lot of attention from entrepreneurs and companies, and as these companies have realized this, many have opted and preferred the no-code development.

According to projections of GLOBE NEWSWIRE the lowcode and no-code market should grow from a measured size of U$D 10 Bi in 2019 and reach U$187 Bi by 2030.

At this point, I imagine that the opportunity is already very clear to you, but it may be that you are still wondering how you can actually benefit from this revolution.

How to benefit from no-code

Firstly, it is interesting to mention that the technology market has attracted the interest of many people in recent years.

  • Intrapreneurship — create apps internal and gain promotion
  • Freelance — charge R$ 2k–50k per project
  • Found — launch your own startup SaaS

This is mainly because this is a segment that offers many perks for professionals.

Salaries are high and with many benefits and the home office is already a standard practice in the market.

In addition to the fact that it is a market that does not stop growing and is practically anti-crisis, rain or shine it is a market that has demand.

That is, if companies are doing well and growing, they invest in technology and innovation to grow even more and differentiate themselves from competitors.

If they are in crisis, they invest in technology and innovation to cut costs and differentiate themselves from competitors.

And this sum of factors has attracted many people to this market.

However, this is also a segment that requires greater training of professionals and therefore many people have had difficulties in making this change.

Whether you are a person who had already thought about migrating to the tech market at some point, or if after this content you became interested in the segment, or even if you are already in this market, the no-code opens up a great opportunity.

Does this mean no-code is easy?

Quick answer: No.

You will still need to master several other aspects involving application development and fundamental technical understandings.

But no-code tools have a much lower learning curve

And with this new skill, the ways to make money are diverse.

You can work within a company, creating and improving your no-code applications. For example: transforming outdated excell spreadsheets into robust apps, with complete dashboards.

Powerful skill to secure a pay raise or promotions.

You can act providing services to other companies as a freelancer or creating your own agency.

Because apps are high value-added products, you can charge R$1000, R$2000, R$5000, R$10000, R$50000 and even much more for a project.

And because with this skill you can also get your app or business idea off the ground.

Create your own startup, your own SaaS (subscription app).

Anyway, the opportunities are countless.

What can we create with no-code

What is no code for?

As I already mentioned here, with the level where the tools are today, you really can create practically anything with no-code.

You can create:

The opportunities are truly endless, ecommerce, marketplaces, ERPs, SaaS, scheduling systems, delivery systems, social networks.

Copy Uber, Airbnb, Facebook, Instagram.

Use Artificial Intelligence, Web3 apps, Token Gated apps

and the list goes on.

Today, with tools that allow API integrations, the range of possibilities is truly endless.

But that's what we couldn't create with no-code.

And then the answer is really, it depends..

Everything I mentioned above can be created with no-code, but it depends on the tools you are using.

Therefore, one of the first steps is to understand your project's requirements and select a tool that fits it.

There's no need to select an extremely complex tool if your goal is to create a super simple app.

The same is true if the objective is to create a complex marketplace with several users, you need to use a tool that allows this.

Tip: Top 5 Tools no-code

Tool Main use Emphasis
Bubble Web applications Advanced logic + plugins
FlutterFlow Mobile apps Export native Flutter code
webflow CMS Websites Native SEO and pixel-perfect design
Xano Back-end / API Scale millions of requests
make up Automations +1,000 ready-made integrations

Some examples of the best no-code tools currently available are:

  1. Bubble - for creating web applications
  2. SAP Build Apps (formerly AppGyver) - for creating free and offline apps mobile apps.
  3. FlutterFlow - for creating complete apps mobile devices
  4. Webflow - for creating websites with a high degree of design.
  5. Xano - for robust backends

For more detail on the no-code tools, we have prepared a specific article on the subject here on our blog.

Also check out our free courses:
Free Bubble course for beginners
Free FlutterFlow course for beginners
Free SAP Build Apps course (formerly AppGyver) for beginners.

See the no-code in action

Do you want to better understand what this codeless development is? Let's go to a practical example so that you can abstract better. 

A seller always has a series of slips to generate after purchases. Are many emails, which generates hours of work! After passing through sales, it is still forwarded to the financial sector, generating more work for both sides. 

ScenarioSales issues many payment slips, and Finance confirms them manually → rework.
Minimum stack: FlutterFlow (dashboard) + Supabase (tables: customers, orders, invoices) + Make/n8n (webhooks) + email/WhatsApp.
Step-by-step (7-day MVP):

  1. Order form (FlutterFlow) saves to orders.
  2. Automations: webhook in Make/n8n receives new_order → Creates payment slip via gateway API → Returns boleto_url and status.
  3. Notification to the customer (email/WhatsApp) with the payment slip link.
  4. Return (webhook): when payment is confirmed, the flow marks status="paid"“ and notify the seller.
    Deliverable: dashboard with status filter and CSV export.
    Success metric: reduce billing average handling time (order → invoice) from hours to minutes.

This and many other automations are included in the plan. No-code PRO.

Meanwhile, the company's IT team has many other demands, and resolving this workflow issue is not really their priority. How can this situation be resolved?

Even if the sales and finance teams are not familiar with codes and development, with the tools in code they would be able to develop a solution to improve this workflow, building automation and tools to facilitate these processes.

No-code turns non-technical citizens into technology creators!

No-code x low-code

What is the difference between low code and no-code?

When looking for information about what is no-code, you will probably come across the term low-code. But what would that be?

Well, according to the translation itself we can already have an idea about it. No code refers to the use of no code, while low code requires a little code. 

  • No-code: zero code, ideal for fast MVPs
  • Low-code: accepts snippets; good for complex integrations

When to use:

  • Needs to launch in days / non-technical team → start by no-code.
  • There are complex integrations, tax rules, or legacy issues.low-code Gives extra flexibility.

Care (for both): versioning, RBAC, logs, plugin/integration policy, and governance to avoid shadow IT.
Brand context/manifesto: the thesis of "teaching computers to speak our language" is the spirit of no-code.

Why invest in no-code?

Everyone who wonders about codeless development also wonders how they can take advantage of this revolution, and the possibilities are many.

No code in the corporate world 

This is closely related to the example we cited earlier. You are no longer at the mercy of IT staff or even third-party professionals to develop good in-house solutions.

Also, you don't need to be a programming master to do this. If you manage to solve an old problem with this type of solution, you can be sure that you will be very well noticed by all collaborators. 

This, by the way, is a great differential of a good employee. Knowing how to identify a problem, design a flow to solve it and translate all of this with the no code tools is, for sure, something very positive.

For your own investment

Another possibility is to use the no code tools to create applications for third parties or even to sell them on the internet.

From the moment you create a more generalist solution, then you can offer it to several companies or other customers. That is, you are monetizing the apps.

It's a great way to earn extra income or even make it your only source of income. 

Companies that want to stand out in the market do not hesitate to acquire solutions that will improve and optimize their respective processes. 

Many of these companies still don't know what the codeless movement is, so you'll be one step ahead. 

no-code Market Predictions

First a few more facts:

  • The no-code market has grown a lot and fast
  • Companies have recently received large contributions from investors.
    • Which only reinforces the predictions presented at the beginning of the video.
  • Market that is just beginning and is not yet widely known by the population and companies.

This leaves an even bigger window of opportunity for those just entering the no-code market.

Those who arrive first drink clean water.

Forecasts:

  • I dare say that within a few years, 99% of MVPs will be built with no-code.
  • I also believe that soon no-code will be the main way to create internal applications, dominating this market in the small and medium enterprise segments.
  • We will see more and more no-code being used in all types of companies for prototyping, by agile squads.
  • no-code software houses started to steal market from the traditional software houses and will have a good share of the segment, mainly in the Founders market, small and medium companies.

And yet, about some interesting branches that I have my eye on and I see a bright future ahead is the combination of:

  • No code + artificial intelligence
  • Nocode + Web3

No code tools have a very solid place in the market and the trend is for their use to grow more and more.

Knowing the possibilities of this codeless programming will open many doors for you, regardless of where you want to apply this knowledge. 

Now that you know what this movement is, how about exploring a little more of its tools and opportunities?

Identify a bottleneck in your work, choose a no-code tool and create the prototype in 48 h. Share the result with the team.

What is a no-code language?

The term no-code means “without code”, being a movement in the technology area that brings the possibility of developing solutions in mobile or web format without needing a single line of code. 

What is low-code?

According to the translation itself, low-code refers to the little use of code needed to create applications.

Why invest in no-code?

No code is still a new market with a high growth projection due to the immense range of problems it solves, thus being a great market to invest in.

What is AI no-code?

IA no-code is about creating AI solutions without programming., using visual interfaces that combine pre-trained (or adjustable) models with Data and automation via connectors/APIs.

Quick examples:
Support chatbot with context from your FAQ.
Automatic summaries of meetings and sending them to the CRM.
Extracting data from PDFs and creating spreadsheets.
Lead classification with scoring and follow-up.

org

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Neto Camarano

Neto specialized in Bubble due to the need to create technologies quickly and cheaply for his startup, and since then he has been creating systems and automations with AI. At the Bubble Developer Summit 2023, he was listed as one of the world's leading Bubble mentors. In December, he was named the top member of the global NoCode community at the NoCode Awards 2023 and won first place in the best app competition organized by Bubble itself. Today, Neto focuses on creating AI Agent solutions and automations using N8N and OpenAI.

Also visit our Youtube channel

Learn how to create AI Applications, Agents and Automations without having to code

More Articles from No-Code Start-Up:

Straight talk: 2026 will be a game-changer for those who want to make money with... AI (Artificial Intelligence).
Opportunities exist, but not all are worth your time, and some promise much more than they deliver.

In this article, I've organized the main ways to monetize AI into clear categories, with pros, cons, and the actual level of effort involved.
The idea here is to help you choose a conscious path, without falling into illusory shortcuts.

AI applied to the workplace as an employee (career and security)

If you already work for a company, applying AI to your daily routine is one of the safest ways to start.
You learn, experiment, and build real projects without sacrificing financial stability.

It's possible to create internal automations, agents, and even softwares that increase efficiency, reduce costs, and generate a direct impact on the business.
When that happens, recognition tends to follow — provided you generate real results, and not just "use AI for the sake of using it".

AI applied to the workplace as an employee (career and security)

The key point to understand is that you are not building something that is your own.
Even so, for learning and professional growth, this is one of the best entry points.

AI for managers and business owners

AI for managers and business owners

For managers and business owners, AI perhaps represents the biggest financial opportunity of 2026.
Most companies are still lost, lacking method, strategy, and clarity on how to apply AI to their processes.

When applied correctly, AI improves performance, reduces bottlenecks, and accelerates results in sales, customer service, and operations.
The challenge lies in the excess of tools and the lack of a clear methodology for the team.

Whoever manages to organize this chaos and apply AI with a focus on results will capture a lot of value.
There's a lot of money on the table here, really.

AI-powered service delivery: an overview

AI-powered service delivery: an overview.

THE AI-powered service provision It's one of the fastest ways to generate income.
You solve real business problems using automation, agents, and intelligent systems.

This model unfolds into freelancer, freelancer for international clients, agency, and consultancy.
Each one has a different level of effort, return, and complexity, but all require execution.

This is where many people really start to "make the wheels turn.".

Freelancer working abroad (earning in dollars)

Freelancer working abroad (earning in dollars)

Freelancing for international companies is, without exaggeration, one of the best options for making money with AI.
Earning in dollars or euros completely changes the game.

You're still trading time for money, but with a much greater return.
The biggest challenge is the beginning: getting the first project and dealing with the language, even at a basic level.

After the first client arrives, referrals start to come in.
For those who want quick results and are willing to sell their own service, this path is extremely compelling.

Creating an AI agency

Creating an AI agency

AI agencies are the natural evolution of freelancing.
Here, you scale people, projects, and revenue.

The market is still immature; many people do everything wrong, and this creates opportunities for those who do the basics well.
You can close deals, build teams, and deliver complete solutions with AI.

The challenge then becomes management: people, deadlines, processes, and quality.
Even so, by 2026, it's one of the fastest ways to consistently monetize AI.

👉 Join the AI Coding Training Learn how to create complete prompts, automations, and AI-powered applications—going from scratch to real-world projects in just a few days.

AI consulting for businesses

AI consulting for businesses

Consulting is an extremely lucrative model, but It's not a starting point..
It requires practical experience, process understanding, and diagnostic skills.

The financial return is usually high relative to the time invested.
On the other hand, you need to have authority, a track record, and a real portfolio of projects.

For those who have experience in agencies, product development, or large-scale implementations, this is an excellent career path.
For beginners, it doesn't make sense yet.

Founder: Creating AI-powered apps

Founder creating AI-powered apps

Creating AI-powered apps has never been more accessible.
Tools like Lovable, Cursor and integrations with Supabase They make this possible even without a technical background.

The financial potential is high, but so is the difficulty.
Creating technology is no longer the differentiating factor — today, the challenge lies in marketing, distribution, finance, and validation.

It's a path of great learning, but with a high error rate at the beginning.
It's worth it if you're willing to make mistakes, learn, and iterate.

Micro SaaS with AI (pros and cons)

Micro SaaS with AI (pros and cons)

O Micro SaaS It solves a specific problem for a specific niche.
This reduces competition and increases the clarity of the offer.

It doesn't scale like a traditional SaaS, but it can generate a consistent and sustainable income.
The challenge remains the same: marketing, sales, and management.

It's not easy, it's not quick, but it can be a great side business.
Here, I classify it as an "okay" path, as long as you have patience.

Traditional SaaS with AI

Traditional SaaS with AI

O SaaS traditional It has greater potential for scaling, but also greater competition.
You solve broader problems and compete in larger markets.

This requires more time, more emotional capital, and greater execution capacity.
Therefore, the Micro SaaS often ends up being a smarter choice at the beginning.

SaaS is powerful, but it's definitely not the easiest path.

AI-powered education: courses and digital products

AI-powered education courses and digital products

AI-powered education is extremely scalable.
Once the product is ready, delivery is almost automatic.

The problem is time.
Building an audience, producing content, and establishing authority takes months—sometimes years.

Here in NoCode Startup, It took us quite a while for the project to become truly financially relevant.
It works, but it requires consistency and a long-term vision.

AI Communities

AI Communities

Communities generate networking, repeat business, and authority.
But they also require constant presence, events, support, and a lot of energy.

It's a powerful, yet laborious model.
I don't recommend it as a first step for those who are just starting out.

With experience and an audience, it can become an incredible asset.

Templates, ebooks, and simple products powered by AI.

Templates, ebooks, and simple products with AI.

Templates and ebooks are easy to create and scale.
That's precisely why competition is fierce and perceived value tends to be low.

Today, if something can be solved with a question in ChatGPT, It's difficult to sell only information.
These products work best as a complement, not as a main business.

To make real money with AI, deliver execution and result That's what makes the difference.

Next step

Next step

There's no such thing as easy money with AI.
What exists is More access, more tools, and more possibilities. for those who perform well.

The most solid paths involve providing services, well-positioned products, and building authority.
The easier something seems, the greater the competition tends to be.

If you want to learn AI in a practical, structured way, focused on real-world projects, check out... AI Coding Training.

Technology is undergoing a historic transition: from passive softwares to autonomous systems. Understanding the types of AI agents It's about discovering tools capable of perceiving, reasoning, and acting independently to achieve complex goals, without the need for micromanagement.

This evolution has transformed the market. For professionals who want to lead the AI infrastructure, Mastering the taxonomy of these agents is no longer optional.

It's the exact competitive differentiator between launching a basic chatbot or orchestrating a complete digital workforce.

In this definitive guide, we'll dissect the anatomy of agents, exploring everything from classic classifications to modern LLM-based architectures that are revolutionizing the No-Code and High-Code worlds.

Diagram illustrating the perception, reasoning, and action loop of different types of AI agents in a digital environment.
Diagram illustrating the perception, reasoning, and action loop of different types of AI agents in a digital environment.

What exactly defines an AI agent?

Before we explore the types, it's crucial to draw a clear line in the sand. An artificial intelligence agent is not merely a language model or a machine learning algorithm.

The most rigorous definition, accepted both in academia and industry, as in the course Stanford CS221, describes an agent as a computational entity situated in an environment, capable of perceiving it through sensors and acting upon it through actuators to maximize its chances of success.

The Crucial Difference: AI Model vs. AI Agent

Many beginners confuse the engine with the car.

  • AI model (ex: GPT-4, Llama 3): It's the passive brain. If you don't send a prompt, it does nothing. It has knowledge, but no agency.
  • AI Agent: It's the complete system. It has the model as its core reasoning tool, but it also has memory, access to tools (databases, APIs, browsers), and, crucially, a goal.

An agent uses the model's predictions to make sequential decisions, manage states, and correct the course of its actions.

It's the difference between asking ChatGPT "how to send an email" (Template) and having a software that autonomously writes, schedules, and sends the email to your contact list (Agent).

The 5 Classic Types of AI Agents

To build robust solutions, we need to revisit the theoretical foundation established by Stuart Russell and Peter Norvig, the fathers of modern AI.

The complexity of an agent is determined by its ability to handle uncertainties and maintain internal states.

Here are the 5 types of AI agents hierarchical structures that form the basis of any intelligent automation:

1. Simple Reactive Agents

This is the most basic level of intelligence. Simple reactive agents operate on the "if-then" principle.

They only respond to the current input, completely ignoring history or past states.

  • How it works: If the sensor detects "X", the actuator does "Y".
  • Example: A smart thermostat or a basic spam filter. If the temperature exceeds 25ºC, it turns on the air conditioning.
  • Limitation: They fail in complex environments where the decision depends on a historical context.

2. Model-Based Reactive Agents

Taking it a step further, these agents maintain an internal state — a kind of short-term memory.

They don't just look at the "now," but consider how the world evolves independently of their actions.

This is vital for tasks where the environment is not fully observable. For example, in a self-driving car, the agent needs to remember that there was a pedestrian on the sidewalk 2 seconds ago, even if a truck momentarily blocked its view.

3. Goal-Based Agents

True intelligence begins here. Goal-oriented agents don't just react; they plan.

They have a clear description of a "desirable" state (the goal) and evaluate different sequences of actions to achieve it.

This introduces search and planning capabilities. If the goal is to "optimize the database," the agent can simulate various paths before executing the final command, something essential for those working with... AI for data analysis.

4. Utility-Based Agents

Often, achieving the goal is not enough; it is necessary to achieve it in the best possible way. Utility-based agents use a utility function (score) to measure preference between different states.

If a logistics agent aims to deliver a package, the utility agent will calculate not only the route that gets there, but the fastest route, using the least amount of fuel and with the greatest safety. It's about maximizing efficiency.

5. Agents with Learning

At the top of the classic hierarchy are the agents capable of evolving. They have a learning component that analyzes feedback from their past actions to improve their future performance.

They start with basic knowledge and, through exploration of the environment, adjust their own decision rules. This is the principle behind advanced recommendation systems and adaptive robotics.

Infographic comparing the complexity and autonomy of five classic AI agent types, from simple reactive to learning agents.
Infographic comparing the complexity and autonomy of five classic AI agent types, from simple reactive to learning agents.

What are generative agents based on LLMs? 

Classical taxonomy has evolved. With the arrival of the Big Language Models (LLMs), a new category has emerged that dominates current discussions: Generative Agents.

In these systems, the LLM acts as the central controller or "brain," using its vast knowledge base to reason about problems that were not explicitly programmed, as detailed in the seminal paper on... Generative Agents.

Reasoning Frameworks: ReAct and CoT

For an LLM to function as an effective agent, we utilize techniques of prompt engineering advanced principles that structure the model's thinking:

  1. Chain-of-Thought (CoT): The agent is instructed to break down complex problems into intermediate steps of logical reasoning ("Let's think step by step"). Research indicates that this technique... It stimulates complex reasoning. in large models.

  2. ReAct (Reason + Act): This is the most popular architecture currently. The agent generates a thought (Reason), executes an action on an external tool (Act), and observes the result (Observation). This loop, described in the paper... ReAct: Synergizing Reasoning and Acting, This allows it to interact with APIs, read documentation, or execute Python code in real time.

Tools like AutoGPT and BabyAGI They popularized the concept of autonomous agents that create their own task lists based on these frameworks.

You can explore the original code of AutoGPT on GitHub or from BabyAGI to understand the implementation.

Tip in Specialist: For those who wish to delve deeper into the technical design of these systems, our AI Coding Training It explores exactly how to orchestrate these frameworks to create intelligent softwares.

Architectures: Single Agent vs. Multi-Agent Systems

When developing a solution for your company, you will face a critical architectural choice: should you use a super agent that does everything or multiple specialists?

What is the difference between Single Agent and Multi-Agent Systems?

The difference lies in form of organization of intelligence.
One Single Agent It concentrates all the logic and execution into a single entity, making it simpler, faster, and easier to maintain, ideal for straightforward tasks with a well-defined scope.

Already the Multi-Agent Systems They distribute the work among specialized agents, each responsible for a specific function.

This approach increases the ability to solve complex problems, improves the quality of results, and facilitates the scalability of the solution.

When should you use a Single Agent?

A single agent is ideal for linear, narrow-scope tasks. If the goal is "summarize this PDF and send it by email," a single agent with the right tools is efficient and easy to maintain.

Latency is lower and development complexity is reduced.

The Power of Multi-Agent Orchestration

For complex problems, the industry is migrating to Multi-Agent Systems (MAS). Imagine a digital agency: you don't want the copywriter to do the design and approve the budget.

Recent technical discussions, such as this one Single vs Multi-Agent debate, They show that specialization trumps generalization.

In a multi-agent architecture, you create:

  • A "Researcher" agent that searches for data on the web.
  • An "Analyst" agent that processes the data.
  • An agent called "Writer" who creates the final report.
  • A "Critical" agent who reviews the work before delivery.

This specialization mimics human organizational structures and tends to produce higher quality results.

Modern frameworks facilitate this orchestration, such as LangGraph for complex flow control, the CrewAI for teams of role-based agents, and even lighter libraries such as Hugging Face smolagents.

Visual representation of a multi-agent system where specialized agents collaborate to solve a complex business problem.
Visual representation of a multi-agent system where specialized agents collaborate to solve a complex business problem.

Practical Applications and No-Code Tools

The theory is fascinating, but how does this translate into real value? Different types of AI agents are already operating behind the scenes of large, agile startups operations.

Coding and Development Agents

Autonomous agents such as Devin or open-source implementations such as OpenDevin They utilize planning architectures and tools to write, debug, and deploy entire codebases.

In the No-Code environment, tools such as FlutterFlow and Bubble They are integrating agents that assist in building complex interfaces and logic using only text commands.

Data Analytics Agents

Instead of relying on analysts to generate manual SQL reports, utility- and goal-oriented agents can connect to your data warehouse, formulate queries, analyze trends, and generate proactive insights.

This democratizes access to high-level data.

Solutions for Businesses

For the corporate sector, the implementation of AI-powered automation solutions It focuses on operational efficiency.

Customer service agents (Customer ExperienceAgents who not only answer questions but also access the CRM to process reimbursements or change plans are examples of goal-oriented agents that generate immediate ROI.

Companies like Zapier and the Salesforce They already offer dedicated platforms for creating these corporate assistants.

Interface of a business dashboard displaying performance metrics optimized by autonomous AI agents.
Interface of a business dashboard displaying performance metrics optimized by autonomous AI agents.

Frequently Asked Questions about AI Agents

Here are the most common questions we receive from the community, which dominate searches on Google and in forums like... Reddit:

What is the difference between a chatbot and an AI agent?

A traditional chatbot typically follows a rigid script or simply responds based on trained text.

An AI agent has autonomy: it can use tools (such as a calculator, calendar, email) to perform real-world tasks, not just converse.

What are autonomous agents?

These are systems that can operate without constant human intervention. You define a broad objective (e.g., "Discover the 5 best SEO tools and create a comparison table"), and the autonomous agent decides which websites to visit, what data to extract, and how to format the results on its own.

Do I need to know how to program to create an AI Agent?

Not necessarily. While knowledge of logic is vital, modern platforms and No-Code frameworks allow the creation of powerful agents through visual interfaces and natural language.

For advanced customizations, however, understanding the logic of AI Coding That's a huge advantage.

Futuristic concept of human-AI collaboration, where developers orchestrate multiple types of AI agents in a digital work environment.
Futuristic concept of human-AI collaboration, where developers orchestrate multiple types of AI agents in a digital work environment.

The Future is Agentic — And It Requires Architects, Not Just Users

Understanding the types of agents AI It's the first step in moving from being a consumer of technology to being a creator of solutions.

Whether it's a simple reactive agent for email triage or a complex multi-agent system for managing e-commerce operations, digital autonomy is the new frontier of productivity.

The market is no longer just looking for those who know how to use ChatGPT, but those who know... designing workflows that ChatGPT (and other models) will execute.

If you want to move beyond theory and master building these tools, the ideal next step is to learn about our... AI Agent Manager Training. The era of agents has only just begun — and you could be in charge of it.

If you are looking to create more advanced projects, with better security, greater scalability, and more professionalism using the tools of Vibe Coding, This guide is for you.

In this article, I've outlined three very important tips that will guide you from beginner to advanced and truly professional projects.

We need to go beyond a simple visual interface and build a solid architecture. Let's go!

Why combine Lovable, N8N, and Supabase?

Tip 1: Starting by focusing on the main pain point

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My first piece of advice is to start with Lovable, but focus on simpler, more direct projects, addressing the pain points you want to solve with technology.

Be a SaaS, one Micro SaaS Whether it's an app or an application, find out what the main pain point is for your end user.

It's crucial to avoid the mistake of including "a million features, a million metrics," and complex business rules right from the start. This confuses the user and will almost certainly cause the project to fail.

Focus on creating in Lovable He creates very beautiful and visually appealing apps interfaces. Solve the main pain point first, and only then can you make the project more complex.

Case

best vibe coding apps​ (2)

A very interesting example, and one of Lovable's main case studies, is... Plink.

Basically, it's a platform where women can check if their boyfriend has had any run-ins with the police or has a history of aggression.

The creator, Sabrina, became famous because she created the app without knowing any code, focused on the main pain point, and the app simply "exploded.".

In just two months, the project was already projecting $2.2 million in revenue. She validated the idea on Lovable, proving that market focus is what makes a project successful.

Another example is an AI agent management application. We always start with the interface in Lovable and only then migrate the project to [the other platform/tool]. Cursor to make it more advanced and complex.

Master Supabase, the heart of advanced projects.

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The second tip, and the most important for security and scalability, is to thoroughly learn the Supabase component. This encompasses data modeling and all back-end functions.

To create AI projects, you'll need the front-end (the user interface, like in Lovable) and the back-end (the intelligence, data, security, and scalability).

The back-end uses the N8N for automation and AI agents, but it is the Supabase which will be the heart of your project.

If you want a highly secure and scalable project, the secret is to master Supabase.

Courses for Beginners:

The great advantage is that, if the interface created by Lovable has a problem, since you already have the core of your project well structured, you can simply remove Lovable and plug the data into another interface, such as Cursor.

You don't need to be a technician, but you need to understand the... MacroHow data modeling, security (RLS), and data connection work.

Understanding these basics is crucial for you to be able to request and manage AI effectively. For this, I recommend our course. Supabase Course in the PRO subscription.

Tip 3: When to move on to Cursor/AI-powered code editors

best vibe coding apps

The third tip is about taking the next step: migrating to AI-powered code tools and editors, such as... Cursor or Cloud Code.

It's very important to start with Lovable in a simplified way, but if you want to make your project more advanced, robust, and scalable, you'll need to combine the organization of your back-end in Supabase with the greater control offered by these tools.

However, it is essential to understand that knowing well the Supabase It's a prerequisite before jumping into the... Cursor, Because you need to have the database and architecture very well organized.

For complex projects, this union is key to having complete control over the code and structure.

Get to know the AI Coding TrainingMaster prompt creation, build advanced agents, and launch complete applications in record time.

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