DevTools MCP: The Protocol that Transforms AI Agents into Performance Optimizers for Web Apps

November 7, 2025
11 min
Updated on November 7, 2025

O DevTools MCP It emerges to redefine AI-assisted optimization and debugging, crucial for the performance of any web application.

For those building a profitable SaaS or professionals seeking promotion, loading speed and user experience are the backbone of success, and this protocol is key to standing out in the market.

O Chrome DevTools Model Context Protocol (MCP) It is a sophisticated interface that integrates the capabilities of Chrome DevTools directly into AI Agents (LLMs).

This protocol transforms AI into a debugging assistant capable of interacting in real time with an active Chrome browser.

The result is a level of analysis, diagnosis, and correction that previously required a senior developer, making this guide essential for applying this technology to your reality.

Conceptual diagram of DevTools MCP showing the communication between an AI Agent and Chrome DevTools.

What is DevTools MCP and how does it work?

The complexity of modern web applications, even those built on No-Code platforms, demands a degree of optimization that the human eye and manual audits can hardly keep up with.

O DevTools MCP It was created to fill this gap, providing bidirectional and semantic communication between an AI Agent and the current state of a web page.

This capability is crucial for those who use visual tools, because the underlying code, although automatically generated, still needs performance validation in a real production environment.

Traditionally, troubleshooting performance or layout issues involved manually navigating through the tabs in Chrome DevTools. Elements for DOM inspection, Network for request analysis and Performance for trace.

MCP automates this inspection, allowing AI not only to access this data, but also to understand it in the context of the task.

This means that the AI Agent can identify the CSS code that causes an unwanted "layout shift" or the script that is blocking the main thread even before the end user feels the impact on navigation.

This real-time performance analysis is the differentiating factor that transforms a functional MVP into a highly competitive market product.

Model Context Protocol (MCP): The Bridge Between LLMs and the Browser

The term Model Context Protocol (MCP) refers to the standardized interface language that allows LLMs to receive, process, and send commands to Chrome DevTools, as detailed in... Official Chrome DevTools (MCP) blog for its AI agent..

It essentially translates the AI Agent's intent — such as "analyze why loading time is slow" — into specific DevTools Protocol commands, such as collecting network metrics, running a Lighthouse audit, or simulating a slow 3G connection.

This ability to translate and execute directly is what enables AI-Assisted Debugging.

The language model, instead of merely suggesting a theoretical fix, can test it in a live environment by inspecting the DOM and measuring the immediate impact.

This ensures that the proposed optimizations are not only correct, but contextually applicable to your application.

The Great Pain Point for Founders and Employees: Performance Optimization and Scalability

The founder is under constant pressure. They need to validate the product quickly and cheaply (due to financial insecurity and lack of technical skills), but the lack of performance is driving away early adopters.

A slow web app, even if built with excellence in No-Code, is a factor in its failure.

O DevTools MCP It addresses this pain point by automating optimization to ensure a professional-level experience without the need to hire an expensive front-end specialist.

It allows the Founder to build an MVP with excellent performance and achieve scale.

Similarly, professionals (struggling with difficulty applying technologies and needing promotion) who want to stand out with automations and internal applications need their solutions to be robust and fast.

An automated process that stutters or an internal control panel that loads slowly is a negative point in your evaluation.

MCP provides him with the tool to innovate and automate without relying on the IT team., Proving the value of your deliverables with concrete optimization data (No-Code Solution for Businesses).

Chrome DevTools interface with the Performance dashboard being analyzed by an AI Agent.

The Architecture of DevTools MCP: Client, Server, and the Power of AI-Assisted Debugging

So that the DevTools MCP For it to work, it's necessary to understand its fundamental architecture, which is based on two primary components that interact through the already existing [unclear/unclear]. Chrome DevTools Protocol, which defines the API calls.

How an AI Agent Uses the MCP Client for Real-Time Debugging

O MCP Client It is the part that resides within the AI Agent or the LLM.

Its function is to receive a user request – such as “Find and tell me why the main image on the page is taking so long to load” – and transform it into a sequence of actionable commands that the Chrome browser can understand.

This MCP Client sends these commands to the DevTools Server via WebSocket.

The real magic of AI-Assisted Debugging happens when the Server responds. The MCP Client receives not only raw data (such as network logs), but a context rich. The LLM uses this context to:

Diagnose: Identify the problem (e.g., the image has an excessive file size).
Suggest the correction: Suggest a change (e.g., "reduce the image to 500kb and use the WebP format").
To check: Re-run the performance audit to confirm that the correction had the desired effect, validating the real-time optimization cycle, according to studies on... AI-Assisted Debugging with Performance Insights.

Setting up the DevTools Server: The First Step Towards Automation

O DevTools Server It is the Chrome browser instance (or an environment). headless (such as Puppeteer) which exposes the DevTools Protocol to be accessed.

Setting up this DevTools Server is relatively simple and, in the context of DevTools MCP, This is the point of contact that allows AI to take control of the inspection.

Many implementations of Coding Agents, such as those available in The official Chrome DevTools MCP repository on GitHub., These solutions already include the DevTools Server setup as part of their basic infrastructure, making life easier for the No-Code user.

By configuring the DevTools Server, you are, in effect, granting your AI the ability to see and interact with your application exactly as a human user would, but with the precision of an expert.

It is essential for anyone dealing with AI infrastructure and seeks to automate complex maintenance tasks, transforming the AI Agent into a true technical assistant.

How to use DevTools MCP to optimize Core Web Vitals: Advanced Diagnostics with DevTools MCP

Core Web Vitals (CWV) optimization has become an indispensable ranking factor for Google, with LCP (Largest Contentful Paint) being a critical metric for speed perception.

Ignoring CWVs, especially LCP, is the same as leaving money on the table for the Founder and the same as losing relevance in deliverables for the professional.

O DevTools MCP It offers a powerful, automated way to master these metrics, aligned with Google's quality guidelines. Core Web Vitals.

Auditing the LCP (Largest Contentful Paint) and Other Critical Metrics

One of the most valuable applications of Chrome DevTools MCP It is the ability to perform complete Lighthouse and Core Web Vitals audits and analyze the results at a semantic level.

Instead of simply presenting a score, AI can identify the element that is the LCP, analyze the chain of dependencies that delay it, and suggest exactly how it should be optimized.

To understand deeply how the Largest Contentful Paint (LCP) This directly affects your conversion rate; see the... The ultimate guide to LCP and performance..

For example, an AI Agent using MCP can diagnose that a delay in LCP is not caused by an external resource, but rather by a CSS block that is blocking rendering.

It can then, through AI-Assisted DOM Inspection, suggest prioritization. inline This critical CSS is used to verify improvements in real time, providing the No-Code user with a ready-to-apply solution.

AI-Assisted DOM Inspection: Solving Complex Layout Problems

AI-Assisted DOM Inspection is the feature that allows AI to not only see the page structure, but also understand the geometry and positioning of elements.

This is especially useful for diagnosing complex layout problems, such as Cumulative Layout Shift (CLS).

An AI agent can:

Monitor the Layout: Observe the DOM Inspection during loading.
Identify Changes: To detect which element caused the shift (the visual change).
Suggest a Correction: Suggest including fixed dimensions or reserving space (placeholder) for the late loading element, correcting the CLS.

This level of granularity and the ability to simulate end-user behavior, all in an automated way, is what positions the domain of DevTools MCP as a highly valued skill in the market.

An AI Agent (LLM) correcting a layout or Core Web Vital error in real time, symbolizing AI-Assisted Debugging.

DevTools MCP vs. Playwright MCP: Which one to choose?

When exploring the ecosystem of coding and automation agents, you may encounter other protocols with the acronym MCP (such as Playwright MCP), which can be confusing.

It is crucial to understand that, although both use the concept of Model Context, they have fundamentally different focuses in the development cycle.

It's important to note, to avoid common confusion, that the acronym MCP (Microsoft Certified Professional) refers to a completely distinct certification within the Microsoft ecosystem, with Focus on career development and IT., unrelated to Chrome DevTools.

Focus on Deep Analysis vs. Automated Functional Testing

O Chrome DevTools MCP It is, above all, a tool of diagnosis and optimization. Their focus is on:

Performance Analysis: Measure loading times, memory consumption, network bottlenecks, and execution threads.
Semantic Debugging: Allowing AI to understand why The code is slow or broken, based on detailed data from Chrome DevTools.
Quality Improvement: Focus on optimizing metrics such as LCP and FCP (First Contentful Paint).

Already the Playwright MCP (focused on Playwright) tends to be a solution primarily aimed at automated functional tests.

Its main focus is to emulate user interactions, ensuring that the workflow (such as login, registration, form filling) functions as expected across different browsers, with an emphasis on stability and test coverage.

You can learn more about Playwright's test automation features by consulting the official documentation of the tool.

When to Use Each Protocol in Your Development Strategy

Use DevTools MCP: Whenever your priority is to speed, resource optimization and technical excellence.

It's the ideal tool for the Founder who needs their SaaS to be profitable. fast to retain clients, and for the CLT (Consolidation of Labor Laws) employee who needs to prove the efficiency of its internal application.
Use Testing Solutions (such as Playwright): Whenever your priority is to reliability of the user flow and the browser compatibility.

When working with No-Code solutions, you can (and should) use both approaches.

First, use the DevTools MCP to ensure that your application's baseline performance is top-notch.

Next, use functional testing tools to automate the verification that all user interactions work correctly.

Visual representation of a No Code application being debugged by AI, highlighting the loading speed.

The Path to High Performance: MCP DevTools Integrated with the Future of No-Code

The adoption of AI Agents and DevTools MCP This does not mean the end of technical knowledge, but rather a raising of the level of demand.

With repetitive debugging and auditing tasks automated, the No-Code professional (whether a Beginner Generalist, Founder, or Freelancer) can focus on what really matters: the solution to the business problem and the strategic architecture of the product..

Leveraging Solutions like FlutterFlow with Intelligent Debugging

High-performance No-Code platforms, such as FlutterFlow, generate clean code, but optimizing delivery and browser context remains a challenge.

The use of DevTools MCP This is especially relevant here because it allows the performance of mobile and web applications (built on FlutterFlow) to be audited with the same depth as Chrome DevTools, enhancing our learning process. FlutterFlow Course.

An AI Agent with MCP can simulate loading on a mobile device with a slow 4G connection and suggest asset loading optimizations or the use of web workers to maintain a fluid interface.

This transforms performance optimization from a manual trial-and-error task into a scientific, AI-assisted process.

The Next Step: Integrating the AI Coding Training from No Code Startup

To thoroughly understand the philosophy behind DevTools MCP And with other tools that integrate artificial intelligence into the development process, it is essential to seek an education that combines expertise in AI with the practicality of Low-Code.

The Model Context Protocol is a clear example of how AI can interact with complex environments, requiring professionals to have the necessary knowledge base to architect and manage these AI Agents.

THE AI Coding Training The No Code Startup focuses precisely on preparing professionals to work in this new scenario, where creating softwares is not just about dragging and dropping, but about commanding AI Agents with technical precision to guarantee high-performance and scalable products.

This is the future of development: where AI is the engine and its knowledge is the map.

An icon representing the domain of web optimization and performance.

FAQ: Frequently Asked Questions

What is the Model Context Protocol (MCP) and how does it apply to Chrome DevTools?

The Model Context Protocol (MCP) is a communication standard that allows Large Language Models (LLMs) to interact directly with Chrome DevTools.

It serves as a translator, transforming the AI Agent's instructions and questions about performance or layout into DevTools protocol commands, enabling real-time AI-assisted debugging.

How can an AI Agent use Chrome DevTools MCP for real-time code debugging?

The AI Agent acts as an MCP Client, sending requests to the DevTools Server.

He can ask the server to run a Lighthouse Audit, inspect a specific element, or monitor CPU usage.

The context-rich response allows the AI to diagnose the problem (e.g., render blocking resourceSuggest a fix and check if the solution worked, all in a matter of seconds.

What specific tasks does DevTools MCP allow you to automate, such as performance audits or DOM inspection?

DevTools MCP enables the automation of various technical tasks. This includes complete Lighthouse audits to verify accessibility and performance.

It also performs Real-Time Performance Analysis to identify network or JavaScript bottlenecks.

Furthermore, AI-assisted DOM Inspection can diagnose layout problems (such as CLS), and the protocol even allows for the simulation of different network conditions to optimize loading.

What are the main differences between Chrome DevTools MCP and Playwright MCP?

The Chrome DevTools MCP is focused on diagnosis, optimization, and deep technical debugging, using the capabilities of Chrome DevTools to analyze performance.

Playwright MCP, on the other hand, focuses more on Functional testing and user flow automation Across multiple browsers, ensuring interactions function as expected, with an emphasis on stability.

How can I use DevTools MCP to diagnose and fix complex layout problems?

The protocol allows the AI Agent to monitor the style and geometry properties of the elements.

If a layout problem (such as CLS, Cumulative Layout ShiftIf detected, AI-Assisted DOM Inspection can isolate the problematic element, identify the lack of space reservation, and suggest adding fixed dimensions to the code, automatically correcting the visual stability issue.

Alternative text: Visualization of the No-Code Start Up ecosystem, focusing on high performance and strategic solutions. The performance optimization revolution is no longer restricted to elite developers.

With the emergence of protocols such as DevTools MCP, The barrier to entry for technical excellence has been broken down.

He indicates that the future lies in the synergy between the AI Agent and specialized knowledge.

Mastering the art of managing this intelligence is what will set successful builders apart.

The time to learn how to use AI as your debugging and optimization co-pilot is now. Take the Next Step: Enroll in the AI Coding Training and Build the Future of Software!

Known as “Castelo” (Castle), he discovered the power of technology by creating his first startup without writing a single line of code and has since dedicated himself to showing how AI can transform ideas into real products. Today, he is recognized as one of the leading names in Brazil in the creation of AI projects applied to business, automation, and softwares (One Top 5 Tools), helping thousands of people launch their own technological solutions from scratch. With an engaging teaching style and a focus on making technology accessible, he was elected Educator of the Year by Flutterflow and became an Official Lovable Ambassador in Brazil. Today, his focus is on creating applications, SaaSs, and AI agents using the best No-Code tools, empowering people to innovate without technical barriers.

Also visit our Youtube channel

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

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

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

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)

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

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

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

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)

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

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 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

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

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.

December 18, 2025
6 min

Neto Camarano

Beyond Chatbots: Understanding the 5 Types of AI Agents and Their Architectures

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.

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.

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:

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.
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.

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.

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.

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.

December 12, 2025
8 min

Matheus Castelo

If you want to create advanced apps with Vibe Coding, follow these 3 tips.

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

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.

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

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.

December 11, 2025
4 min