The Autonomy Revolution: Understanding the Profound Difference Between Agent AI vs. Generative AI

October 24, 2025
11 min
Updated on October 24, 2025

The dispute between AI agent vs. generative AI This marks a new era in Artificial Intelligence. While previously the focus was on GenAI and its creative capabilities, today the emphasis is on autonomy and operational efficiency in business environments.

The confusion, however, persists: many startups leaders and product managers still treat AI Agent and Generative AI as synonyms or competing technologies.

For those seeking not only to optimize processes, but also reshaping entire business ecosystems, It is crucial to master the fundamental distinction in the clash. AI agent vs. generative AI.

The central thesis is unequivocal and strategic: Generative AI, while revolutionary for its ability to reactively produce text, code, or images, is a fundamental tool in itself. prompt Simply put, it is, in fact, a critical component who resides within of the architecture of an AI Agent.

Generative AI (GenAI): The Cognitive Engine of Creation

For entrepreneurs or CTOs, generative AI should be seen as the creation tool.

Its primary function is to Transforming input data into new and coherent output content., based on patterns it has learned from vast datasets (Details about GenAI can be found in IBM and AWS).

The success of models like GPT, LaMDA, or Bard lies precisely in their ability to generate solutions, whether by writing a persuasive email, drafting code, or creating a conceptual image from a textual description.

This generation capability has profoundly impacted the job market, optimizing creative and repetitive tasks on a large scale (Check out the...). Impact of AI on the Global Job Market).

Definition and Classic Use Cases

GenAI essentially operates in a mode reactive. She is waiting for an instruction (the prompt), processes it internally and returns the result.

Its architecture is centered on the Large Language Model (LLM) or diffusion models (for images), its intrinsic value being... fluidity and the coherence of production.

In a business context, use cases are mostly of digital asset production:

Content Creation: Generating blog articles, social media posts, or copy for advertising, significantly accelerating the marketing cycle, as detailed by Content Marketing Institute on GenAI.
Code Generation: Assistance in writing functions, language conversion or debugging, transforming the LLM into a development co-pilot.
Analysis and Summarization: Processing lengthy legal documents or financial reports, summarizing the key points concisely.

The Myth of Autonomy: Limitations of GenAI

The biggest misconception is expecting Generative AI to be capable of... act Alone in the real world, a pure GenAI cannot, for example, conduct market research, analyze the results, decide on the best launch strategy, and execute the social media post, all in sequence.

It lacks four fundamental elements that define autonomy:

Persistent and Contextual Memory: Generative models typically have a limited context window. They "forget" previous interactions unless explicitly fed with history.
Access to External Tools: GenAI, on its own, cannot navigate the internet in a structured way, interact with third-party APIs (such as a CRM or a payment platform), or use a code editor outside of the environment of [the relevant platform/service]. prompt.
Multi-Stage Planning: She is limited to responding to immediate task. If the goal is "to increase sales in 10% in the next quarter," GenAI needs to manually break down that goal into steps (research, analysis, creation, execution).
Feedback Loop: It lacks an inherent mechanism to self-evaluate the outcome of its actions in the environment and subsequently correct the plan.

This is where understanding AI infrastructure becomes crucial, as it forms the basis for scaling the capabilities of core models.

To delve deeper into the technical basis that underpins these tools, we recommend reading about... What is AI infrastructure and why is it essential?.

Architecture diagram showing Generative AI (LLM) nested within an AI Agent, illustrating that GenAI is a component and not the complete system.

Agent AI vs. Generative AI: Unveiling the Proactive Agent Architecture

The real disruption for the business world lies in AI Agency (or AI Agent), which represents a leap from creation for the action.

The Agent is, conceptually, a software system that perceives its environment through sensors, processes this perception, makes decisions, plans and executes actions through actuators (tools) (For a detailed definition, see the...) AWS's explanation of AI Agents.).

Agent-based AI vs. generative AI It's not a duel, but rather a symbiotic relationship.

While Generative AI is the muscle that performs the generation of complex content or reasoning, the AI Agent is the autonomous pilot that defines the route, monitors traffic, and adjusts the speed.

The Action Loop: Perception, Reasoning, Planning, and Action

The architecture of an AI Agent operates in a continuous cycle, known as the "Action Loop" or O-OODA (Observe, Orient, Decide, Act – Adapted for AI).

This cycle ensures autonomy and self-correction capabilities, elements that are lacking in pure GenAI:

Perception (Observe): The agent collects data from the environment (emails, sales data in a CRM, API notifications, web search results).
Reasoning (Orient & Decide): Using its LLM (an internal GenAI), the Agent processes the target and perceived data, generating a logical plan. This is where the generative engine comes in. translate The state of the world in a sequence of actions.
Planning (Plan): The agent breaks down the complex goal into executable subtasks (Example: "To achieve X, I first need to do A, then B, and only then C").
Action (Act): The agent uses external tools (APIs, browsers, databases) to execute the plan in the real world.
Adaptation (Feedback Loop): The agent evaluates the outcome of the action and uses the feedback to refine the next Perception/Reasoning cycle, ensuring learning and self-correction.

The Key Components of an AI Agent (LLM, Memory, Tools)

To function, an AI Agent needs more than just a powerful LLM (GenAI). It requires a robust data structure and functionality (Source: Google Cloud Generative AI Glossary).

Large Language Model (LLM): Acts as the brain of the system, responsible for reasoning, planning, and generating the language that guides actions and interactions. It is the generative motor. The constant evolution of these models (such as the Grok, Gemini or ClaudeThis is what drives the Agents' power.
Memory (Buffer/Persistent): It stores the history of the interaction (short and long term) and the state of the world that the agent perceived. This prevents repetition and ensures the continuity of planning.
Tools/Plugins: These are the interfaces to the outside world. They can be APIs, specific code functions, or the ability to interact with No-Code platforms to, for example, update a table in a database or send a notification via Slack. Frameworks such as LangChain and CrewAI They are crucial to this orchestration.

Strategic Integration: Why GenAI is Essential for the Agent

The main difference between AI agent vs. generative AI It's not technological, but architectural and functional. GenAI is the engine. The Agent is the complete orchestra that uses that engine.

LLM, with its generative capabilities, is what transforms the AI Agent into an intelligent system, and not just an automaton based on rigid rules.

The power of the LLM lies in its ability to reasoning in natural language.

LLM as the 'Brain' of Reasoning (Plan Generation Mechanism)

When an AI Agent receives a goal (example: “Find 5 leads in the sector of Fintech in São Paulo and generate a contact report”), the internal LLM is called in for the reasoning phase.

He not only creates a text, but Create the action plan. which leads to the goal, using language as its means of calculation.

The LLM thinks:

I need the 'Web Search' tool to find contact information for Fintech companies in São Paulo.
I need the 'Data Validator' tool to filter out valid emails.
I need the 'Report Generator' tool (also GenAI) to format the final report.

It is generative capacity to produce this logical and operational chain that differentiates the AI Agent from a chatbot common or simple workflow automation. Studies on Agent-Based Reasoning on ResearchGate They demonstrate this power.

The complexity of reasoning in generative AI is a field of intense academic study (Read more at SciELO on the topic).

No-Code Automation with AI Agents: From Theory to Practice

For the No Code Startup community, the adoption of AI Agents is a game-changer. Traditionally, No-Code/Low-Code simplified the creating interfaces and flows.

With Agency AI, the focus shifts to creating autonomous intelligence which uses these flows intelligently.

Consider a Customer Service Agent. They not only generate responses (GenAI's task), but also:

Understand The customer's message (via the chat API).
Think Regarding intent (LLM).
Plan the action (Ex.: If it is bug, Create a ticket in Trello; if it is sale, (send payment link).
Action (Interacts with Trello API and Stripe API).

This level of autonomy, built upon the foundation of AI agent vs. generative AI (Understanding GenAI as an engine), it allows startups to develop complex functionalities without writing hundreds of lines of code.

It is the union of AI Coding Training: Create Apps with AI and Low-Code with the power of agentic frameworks, allowing the construction to be focused on business logic and not on syntax.

Interface of a No Code development environment showing the configuration of an AI Agent with blocks for "Perceive", "Reason (LLM)" and "Action (API)".

The Future: Real-World Use Cases and the Market Shift

The market trend is clear: Agency AI will be the main driver of exponential growth in the coming years, moving the market value of... tools creation for systems execution.

The difference between AI agent vs. generative AI It's the difference between having a powerful engine and having a self-driving car.

Applied Examples in Startups

The value of AI agents becomes apparent in contexts where task complexity and the need for interaction with the real world are high:

Proactive Data Analyst: Instead of just responding to one prompt Regarding the data (“What was the profit last month?”), a proactive AI Agent has the goal of “Optimizing Customer Acquisition Cost (CAC)”.

It can monitor ad spending in real time and automatically analyze the... funnels conversion tracking, detecting anomalies (using LLM for reasoning), and autonomously pausing low-performing campaigns via the ad platform's API.
Independent Sales Agent: An agent who receives a list of leads, uses GenAI to customize the pitch The contact system sends the email via a No-Code system, monitors the open rate, and, if there is interest, automatically schedules a meeting in the salesperson's calendar, updating the status in the CRM.

For more details on the application in sales, see the Gartner's analysis of Autonomous Selling with AI.
Content Sourcing Agent: The agent monitors industry news, uses GenAI to summarize the content and rank it by relevance, and then autonomously publishes a summary in the internal community or on the blog (after human review), keeping the ecosystem always up-to-date.

How to Start Building Agents with Low-Code/No-Code

Adopting AI Agents doesn't require a team of PhDs in Machine Learning. The synergy between Low-Code/No-Code and LLM APIs (the generative engine) makes building agents accessible.

Modern No-Code platforms already offer connectors and tools to create the loop of perception and action:

Define the Goal (and the KPI): Start with a clear and measurable goal (e.g., Reduce the average support response time in 20%).
Identify the Tools: Map out the systems that the Agent needs to use (email, Slack, database, Trello).

The integration of AI with Robotic Process Automation (RPA) is an accelerating factor (See The role of RPA in the era of Agency AI.).
Use LLM as a Reasoner: Configure LLM (GenAI) to translate the world state and the goal into a flat logical way of using the tools.

The focus should be on rapid and iterative implementation, a central characteristic of the No-Code Startup philosophy.

Graphical representation of AI advancement, showing a timeline where Generative AI (Creation) precedes and enables the AI Agent (Autonomous Execution).

Strategic Implementation for Startups: The No Code Startup Path

The decision to invest in an AI Agent is fundamentally a strategic decision regarding the allocation of time and resources.

For the No Code Startup, the question agent of pathway or generative pathway It is crucial for optimizing business processes with AI.

GenAI optimizes production. The AI Agent optimizes the value stream complete.

Agent Adoption and Process Optimization

The successful implementation of Agency AI begins with identifying process bottlenecks that are too complex for simple rule-based automations (If This, Then That), but they still consume human time.

The crucial difference of an AI Agent is that it can adapt to unforeseen scenarios within an overall goal.

For example, in the Human Resources sector, an Agent can:

Analyze resumes (GenAI).
Compare with the job description (LLM reasoning).
Schedule interviews (Action via Calendar API).
Send technical tests (Action via testing platform).
And if the candidate does not respond, send a reminder (Adaptation based on feedback loop).

No Code Start Up offers robust solutions for companies seeking this level of proactive automation, through AI and Automation Agents: No-Code Solution for Businesses.

Challenges and Governance of Agency AI

Despite its potential, Agency AI presents unique challenges, primarily related to control and security.

Autonomy means that the agent can, on rare occasions, generate unintentional actions ("action hallucinations").

Governance should focus on:

Sandboxing: Limit the scope of tools that the Agent can access and use.
Human Supervision: Ensure that the Agent requests "permission" for high-risk actions (e.g., making a financial transaction or sending mass communications to clients).
Transparency of Reasoning: The agent must be able to explain the why of their actions (the chain of thought (generated by the LLM), facilitating auditing and correction. The ethical debate surrounding AI autonomy is central (Read about Ethics in Autonomous AI Systems).

Agency AI is a journey, not a destination. Its implementation should be phased, starting with low-risk processes and gradually expanding as trust in the system and maturity increase. agency AI architecture They increase.

Representation of a gear with a brain in the center, symbolizing the autonomous and reasoning system of Agency AI.

Frequently Asked Questions (FAQ)

What does Agency AI mean?

Agency AI refers to Artificial Intelligence systems that have the ability to perceive an environment, make autonomous decisions, plan a sequence of actions, and execute them in the real world (usually via APIs and tools).

Unlike reactive generative AI, the AI Agent is proactive and works continuously towards a long-term goal, adapting its plan based on the results of its actions.

Will Generative AI be replaced by AI Agents?

No. Generative AI will not be replaced, as it is a essential component The AI Agent. GenAI, specifically the LLMs, acts as the Agent's reasoning and communication engine, being responsible for interpreting data, creating action plans, and generating the interface text or code necessary for the tasks.

GenAI is the cognitive engine; the AI Agent is the autonomous execution system.

What are the main practical differences in usage between the two AIs?

The practical difference is that GenAI requires a prompt for each step and cannot interact with external systems without manual intervention.

The AI Agent can be given a high-level goal (e.g., "Monitor Twitter and notify me of brand crises"), and it will autonomously execute all the steps: research, analysis, classification (using GenAI), and notification (using external tools).

GenAI is a creation tool, while the AI Agent is a autonomous automation system. For further information, see the Practical difference between GenAI and Agents

Where can I build an AI agent without needing complex code?

You can build proactive AI systems using Low-Code and No-Code platforms that offer direct integrations with LLM APIs (OpenAI, Google, Anthropic) and connectors for business tools (CRM, ERP, databases).

These platforms allow you to visually map the loop of Perception, Reasoning (GenAI) and Action, focusing on business logic, not programming complexity. The era of simple content generation is ending, giving way to the era of... autonomous execution.

Understanding the hierarchy AI agent vs. generative AI It is the compass for any leader who wants to build a product or optimize an operation in a scalable way.

Generative AI is incredibly powerful, but it's only half the equation; it needs the agentic architecture to interact, adapt, and, fundamentally, deliver value continuously in the complex environment of a business.

The future doesn't belong to those who only know how to generate content, but rather to those who know how to build intelligent systems that... act for the business.

To take the next step and transform this architecture into real, scalable, and functional products, we invite you to explore the... AI Coding Training From No Code Startup, master the art of creating AI solutions without code. who think and act.

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

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