Forget the 'AI Browser': Understand the 'Agent' Concept of OpenAI Atlas

November 11, 2025
12 min
Updated on November 12, 2025

A new "browser war" is underway, but this time, the battle isn't about rendering speed or memory consumption.

It's about intelligence, autonomy, and the fundamental ability to transform intention into action. Amid a wave of updates injecting AI into legacy browsers, the launch of OpenAI Atlas It signals something different, a change in category that most of the market is still misinterpreting.

We are witnessing the transition from assisted computing to agent computing.

The most common mistake, propagated even by initial discussions in the media., it's putting the OpenAI Atlas In the same box as Microsoft Edge with Copilot or Arc Max. That's a profound conceptual error.

These tools offer a "Browser" in "AI," where artificial intelligence acts as a co-pilot restricted to a sidebar.

O **OpenAI Atlas**, in turn, introduces the “Navigator” *Agent*”"(Agentic Browser) — a fundamentally new category, where AI is not a passenger, but the pilot itself.".

What is OpenAI Atlas? The Agent Browser vs. the AI Browser?

In practice, the difference is abysmal. The care model summarizes pages, answers questions, and generates text.

What is OpenAI Atlas? The Crucial Distinction Between “Agent” and “Assistant”

To understand the impact of OpenAI Atlas, We first need to unpack the conceptual error that dominates the current conversation.

In its eagerness to label the next big innovation, the market has lumped together two completely distinct product philosophies under the same umbrella of "AI Browser".

This confusion obscures the true revolution that is taking place.

The "AI Navigator" Paradigm: AI as an Assistant (Co-pilot)

The model that has become familiar is that of "Copilot." Tools such as Microsoft Edge (with Copilot), Arc Max, and Gemini integrations in Google Chrome operate under this paradigm.

Artificial intelligence is a supplement attached to an existing browser architecture.

The main focus of these tools is... Recovery and Synthesis information.

The user remains in full control of navigation, and the AI acts as a passive assistant. Typical functions include:

Summarize the content of a web page.
Answer questions about the visible text.
Generate emails or blog posts in a sidebar.
Find related information without the user having to open a new search tab.

In this model, the user question and the browser answer. The agency — the ability to act and make navigational decisions — remains in human hands.

AI cannot, on its own, decide to click a link, add an item to a shopping cart, or fill out a login form.

The “Agent Browser” Revolution: AI as Executor

This is where the OpenAI Atlas and its direct competitor, the Perplexity Comet, They diverge radically.

These are not assistance tools; they are tools of Automation and Execution.

O OpenAI Atlas he was designed from scratch as an “agent platform”. AI is not confined to a sidebar; it is "ubiquitous" and has "contextual awareness" of what is happening on the screen.

It can "see" the DOM (Document Object Model), interpret interface elements, and perform actions.

In this new paradigm, the user delegate and the browser executes. Their capabilities extend far beyond text synthesis:

“"Book a round-trip flight to Lisbon next week, with a budget of €500, using low-cost airlines."”
“"Access the LinkedIn profiles of my startup's top 10 competitors, extract their founders and number of employees, and put them into a spreadsheet."”
“"Log in to my admin panel, navigate to the reports section, and export the sales data for the last quarter."”

Why is this change fundamental to productivity?

The paradigm shift from "assistant" to "agent" is the most significant leap in human-computer interaction since the invention of the graphical user interface.

It's no longer about saving time. reading or writing, but rather to save time doing.

For entrepreneurs, founders, and technology professionals, the productivity bottleneck is rarely a lack of information; it's an excess of "digital manual work"—the dozens of clicks, logins, and repetitive tasks required to run a business.

O OpenAI Atlas It aims to eliminate precisely that friction. It transforms the browser from a passive viewing window into an active digital worker, capable of executing processes.

This is the true promise of artificial intelligence for business, Moving automation from the backend layer (such as APIs) to the front-end layer (the user interface).

Comparative diagram showing the difference between an AI Browser (Assistant, focused on Response) and OpenAI Atlas (Agent Browser, focused on Execution).

The Architecture of "Omnipresence": How does OpenAI Atlas work?

So that the OpenAI Atlas In order to "act" autonomously, it requires a fundamentally different architecture from that of legacy browsers.

AI cannot be a simple plugin; she needs to be the core of the system. It was designed from the outset as a platform for agents, allowing a level of integration impossible to replicate simply by adding an extension to Chromium.

“"Contextual Awareness": AI that "sees" and "acts" on the screen.

The defining characteristic of OpenAI Atlas It is your "ubiquitous contextual awareness".

Unlike Copilot, which requires the user to copy and paste text or concentrate on a... sidebar, The Atlas AI model is constantly aware of the context of the active page.

This is achieved through computer vision models (similar to those powering GPT-40) that can "read" the screen., interpreting the structure of the DOM and understand the purpose of interactive elements such as buttons, form fields, and links.

When a user gives a command like “Buy this item”, Atlas isn’t just processing the language; it’s mapping that instruction to a series of UI actions: find_button('Add to Cart'), click(), find_page('Checkout'), Maps(), and so on.

The hybrid model: On-device processing vs. Cloud.

Performing such complex tasks in real time requires a delicate balance between speed and power. OpenAI Atlas It operates on a hybrid model:

Local (On-device) Processing: For immediate and privacy-sensitive actions (such as filling in a saved password or navigating between tabs), Atlas uses smaller AI models that run directly on the user's machine.

This ensures an instant response and that critical data never leaves the device.
Cloud Computing: For complex tasks that require deep reasoning (such as planning a multi-destination trip or conducting in-depth market research), Atlas leverages larger models in the cloud, such as GPT-40 or... future iterations such as GPT-5.

This hybrid architecture is essential. It allows the browser to be simultaneously fast for mundane tasks and powerful for complex automations, a significant challenge for AI infrastructure traditional.

Native integration with the OpenAI ecosystem (GPT-40, GPT-5 and Agents)

The most obvious strategic advantage of OpenAI Atlas It is to be the official "body" for OpenAI's most advanced "brains".

While competitors need to license or use APIs, Atlas has native integration.

This means that, as OpenAI's foundational models (such as GPT-40 and successors) become more powerful in reasoning, planning, and multimodality, the OpenAI Atlas instantly inherits these capabilities.

It's not just a browser; it's the primary delivery vehicle for cutting-edge AI research, transforming theoretical advances into practical automation capabilities.

OpenAI Atlas architecture flowchart showing the interaction between the user interface, the local AI agent (on device), and the AI models in the cloud (GPT 4 or GPT 5).

The New Browser Wars: Atlas vs. Google, Microsoft, and Perplexity

The launch of OpenAI Atlas It reconfigures the battlefield for browsers. The war is no longer about who renders JavaScript faster.; It's about who builds the most capable agent.

And in this new scenario, the established giants may be at a serious disadvantage.

The Battle for Automation: OpenAI Atlas vs. Perplexity Comet

The real competition at the forefront of "Agent Browsers" is not against Chrome, but against the Perplexity Comet.

Both companies understood that the future lies not in assisted search, but in task execution.

O Perplexity Comet It focuses intensely on research as an "action," going beyond simply providing links to synthesize answers and perform complex research tasks.
O OpenAI Atlas It appears to have a broader ambition: not just to conduct research, but to act as a general automation agent for any web-based task, integrating directly into the OpenAI development ecosystem.

This is the battle that will define the next decade: the best response engine (Perplexity) versus the best action platform (OpenAI).

The “Architectural Debt”: Why are Google Chrome and Edge at a disadvantage?

Google and Microsoft face a profound dilemma: "architectural debt".

Their browsers, Chrome and Edge, are based on Chromium, an architecture that has been around for decades and was designed for document viewing, not AI automation.

As pointed out in technical discussions, Attaching AI to these legacy structures is like trying to transform a gasoline car into an electric vehicle by simply changing the engine. The foundation wasn't built for that.

O Microsoft Edge integrated Copilot in a profound way., However, AI still operates "alongside" the browser, not "like" the browser.
O Google Chrome He is joining Gemini, but faces an existential challenge: Google's business model is based on clicks on ads., This is a model that the "Agent Browser" threatens to make obsolete. If the browser performs the task, the user never sees the search page.

Building from scratch, like the OpenAI Atlas This allows for the creation of a native AI architecture, optimized for contextual awareness and action execution — an advantage that may be insurmountable for incumbents.

The "Ecosystem" factor of Microsoft and Google

The advantage that the giants still possess is distribution. Google can force Chrome on all Android devices, and Microsoft can embed Edge in all Windows installations.

O OpenAI Atlas It starts with zero market share.

However, its advantage lies in its vertical integration with the AI model that defines the market.

Developers and power users They will migrate to where the most capable tool is located, and the OpenAI Atlas It is positioned to be the browser of choice for those who see AI not as a toy, but as a productive tool.

Comparative table highlighting the differences in capabilities between OpenAI Atlas (Executor Agent), Perplexity Comet (Search Agent), Google Chrome (Assistant), and Microsoft Edge (Assistant).

Practical Applications: What Can OpenAI Atlas Do for No-Code Businesses?

Leaving aside strategic theory, what is the tangible impact of OpenAI Atlas For an entrepreneur or No-Code developer? Its value lies in taking high-friction, low-value tasks (digital manual work) and automating them through natural language.

Example 1: Market Research and Lead Generation Automation

Imagine replacing hours of manual research with a single command. A founder can instruct the... OpenAI Atlas:

Prompt: “"Search the top 10 'AI for data analytics' startups on Crunchbase that have raised capital in the last 6 months.".

For each one, find the CEO on LinkedIn, the main website, and the pricing model. Consolidate everything into a table.”

Atlas would perform this multi-tab and multi-site task, delivering an actionable result. This transforms the browser into an active tool for... lead generation and market intelligence.

Example 2: Atlas as a QA (Quality Assurance) Tool for Developers

For No-Code and Low-Code developers, regression testing is a time-consuming manual process. OpenAI Atlas It can act as an automated QA tester.

Prompt: “Access the staging version of my app at https://www.flutterflow.io/. Log in with the test credentials [username/password].

Navigate to the checkout page. Try these 5 discount coupons: 'DESCONTO10', 'FRETEGRATIS', 'TESTE123', 'PROMOBUG', 'VERAO20'. Report which coupons failed and capture a screenshot of the error message.‘

This allows creators who use platforms such as FlutterFlow Validate your applications quickly and robustly, without writing a single test script.

Example 3: Data Consolidation and Automated Reporting

Managing a digital business involves monitoring multiple control panels. OpenAI Atlas This data can be consolidated.

Prompt: “"Open my Google Analytics, my dashboard Stripe and mine HubSpot CRM. Extract the total number of unique visitors, gross revenue, and the number of new MQL leads from the last week. Present a summary.”

This ability to synthesize data from multiple sources transforms the browser into a dashboard A dynamic executive, saving management time and enabling faster decisions.

Illustration of a task automation workflow being executed by OpenAI Atlas, showing navigation between multiple tabs (LinkedIn, Google Analytics, App) to complete a task.

The End of Google Search? The Impact of OpenAI Atlas on Search and SEO

If the browser completes the entire task, the user stops to search for the intermediate links.

This is the most profound consequence of OpenAI AtlasIt threatens not only Chrome's dominance as a browser, but Google's dominance as the gateway to the internet.

The web's business model, based on traffic and advertising, is at risk.

From "Search by Links" to "Search by Execution"“

User behavior is fundamentally changing. Google has trained us to "look for links" to find information.

Tools like OpenAI Atlas Perplexity and other programs are training us to "delegate executions.".

Users no longer want the "10 blue links" to find out how to book a hotel; they want the hotel to be booked. The future of search is execution., And the browser agent is the vehicle for that.

The “Invisible Web”: Could Atlas kill blog traffic?

The debate is intense: if Atlas extracts the information, Once the data is consolidated and delivered to the user (or used to perform an action), the click on the originating website disappears.

For content creators, bloggers, and SEO-based businesses, this represents an existential crisis.

Organic traffic may drop drastically as AI agents become the primary intermediaries of information.

The web, as we know it, could become "invisible," consumed by agents instead of being read by humans.

New Opportunities: Optimizing for “Agents” (AEO)

However, where one optimization dies, another is born. The future of SEO may be... AEO (Agent Engine Optimization).

Instead of optimizing content for human reading and crawlers In terms of search, optimization will focus on making website data readable and actionable by AI agents.

AEO involves focusing on:

Structured Data (Schema): Clear and clearly marked information (such as prices, schedules, locations) that an agent can extract without ambiguity.
Clear APIs: Allow agents to interact with your services in a programmatic and reliable way.
"Agent-Readable" content: Direct and factual texts that facilitate data extraction, instead of overly flowery prose.

Keeping up to date on these trends, such as those discussed in No-Code Start-Up Blog, This will be vital for digital survival.

Chart showing the shift from traditional search (SEO focused on humans and traffic) to search by execution (AEO focused on agents and structured data) driven by OpenAI Atlas.

Quick Answers about OpenAI Atlas

Will OpenAI Atlas replace Google Chrome?

In the short term, no. Google Chrome holds a massive market share And it is deeply rooted in business workflows.

However, in the long term, the OpenAI Atlas This represents the greatest existential threat to Chrome, not only because it is a competing browser, but because it attacks Google's fundamental business model (search and advertising). Replace search with execution..

Is OpenAI Atlas free?

At the time of launch (October 2025), the OpenAI Atlas It was made available in a limited way.

It is highly likely that it will follow a model. freemium, Similar to ChatGPT, basic browsing functions will be free, but advanced "Agent" features, which consume significant computing power (like the GPT-40 or GPT-5 models), They will likely be tied to a paid subscription., such as ChatGPT Plus or a new plan focused on automation.

Do I need to know how to program to use OpenAI Atlas automation?

Absolutely not. That is the central point of the "Agent Browser" revolution. The goal of OpenAI Atlas It's about democratizing automation, allowing any user to perform complex tasks through natural language commands (prompts).

It aligns perfectly with the No-Code philosophy., which focuses on empowering business users to build and automate without writing code.

Is OpenAI Atlas safe to use with passwords and banking information?

This is the biggest barrier to mass adoption. For users to trust an agent to "log in" or "buy things," security needs to be foolproof.

O OpenAI Atlas It addresses this through its hybrid architecture: the processing on-device (local) It is used for sensitive tasks, such as password management, ensuring that critical data never leaves the user's machine.

However, OpenAI will face intense scrutiny to prove the robustness of its security before achieving widespread trust.

The Future is an Agent: Preparing for the Autonomous Web

The launch of OpenAI Atlas It's not just another product in a saturated market. It's a seismic event, a clear sign of the next era of computing.

We left the web of information (dominated by Google) for the web of assistance (the Copilot attempt) and now we officially enter the web of execution.

O OpenAI Atlas It is the first mature vehicle for this new reality.

For professionals, entrepreneurs, and developers, this change is both an opportunity and a warning.

Mastering these "agent" tools will soon be the difference between running a business manually and scaling processes intelligently.

Automation is moving away from tools that backend complex systems (like Make or Zapier) and merging with the most fundamental layer of our interaction with the internet: the browser.

The future will not be about "surfing" the web; it will be about "delegating" tasks to it.

O OpenAI Atlas It is a "Browser Agent," and the ability to create, manage, and optimize for AI agents It will undoubtedly be the most valuable skill of the next decade.

For those who wish not only to use these tools, but also to build the next generation of applications upon them, mastering the fundamentals is crucial.

It's time to move beyond No-Code and into AI-Coding, mastering the... AI Coding Training: Create Apps with AI and Low-Code.

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

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

best ai app builder vibe coding platform

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