😢 ¿Es el fin de n8n? Probando los flujos agénticos con google Antigravity

N8n vs. Agential Workflows: The Future of Automation

Introduction to Agential Workflows

• N8n is not dead but faces competition from agential workflows, which may replace its role in automation.

• The video aims to explain what agential workflows are and how they can benefit AI agency owners by providing more value to clients.

Speaker Background

• Kevin Belier introduces himself as the leader of Vive Community VIP, focused on generating income through AI.

• He owns Isha Automation Agency, which has transitioned into a Tech Factory model, selling solutions to other agencies rather than end clients.

Understanding N8n

• N8n allows for no-code programming using nodes, enabling users to connect various APIs and automate tasks like data storage and information transformation.

• It opened up opportunities for many entrepreneurs by simplifying the implementation of high-value services for businesses facing operational challenges.

What Are Agential Workflows?

• Agential workflows operate similarly to N8n but utilize natural language instead of nodes; users can simply state their needs for automation.

• This approach lowers the entry barrier significantly compared to N8n, eliminating the need for understanding its interface or architecture.

Impact on AI Agencies

• Companies present their automation needs (e.g., managing WhatsApp messages) to AI agencies that traditionally design solutions using tools like N8n.

• With agential workflows, AI can now map requirements and design architectures autonomously based on client input without human intervention in many steps.

Transitioning Roles in Automation

• Previously, agencies had to learn how to use tools like N8n manually; now they can describe needs while AI constructs solutions automatically.

• This shift could reduce the necessity for intermediaries in the process, allowing companies direct access to results without needing extensive knowledge about system architecture or tool usage.

Introduction to Agent Flows

Overview of Agent Flows

• The speaker acknowledges the audience's potential skepticism about theoretical concepts, emphasizing the importance of understanding system architecture and agent flows.

• Introduction of a new IDE called "Agent First," which has gained popularity for its user-friendly features and free credits upon Google account registration.

Getting Started with Agent First

• Instructions on downloading the tool are provided, along with a link in the video description for easy access. Users need to log in with their Google accounts.

• A brief overview of the interface is given, highlighting sections such as the file explorer on the left, code display in the center, and agent interaction options on the right.

Creating Your First Agent Flow

• The first step involves creating a new directory or folder using an "Open Folder" button within the IDE.

• The speaker demonstrates how to name this folder (e.g., "Prueba Flujos Agénticos") to initialize the development environment.

Importing Key Files

Essential File Setup

• An important file named "agents.md" will be shared via a WhatsApp group link in the video description; it serves as a system prompt defining agent behavior.

• Instructions are provided for importing this file into Agent First by creating a new file named "Agents.md," copying content from another source, and saving it in the previously created folder.

Understanding Core Concepts

Framework Structure

• The framework operates under three layers that separate responsibilities to enhance reliability within agent operations.

Deterministic vs. Stochastic Approaches

• Explanation of deterministic operation: consistent output from specific inputs (e.g., rolling a die that always shows 1).

• Contrast with stochastic operation: results vary based on probabilities (e.g., rolling a standard die).

The Nature of AI Operations

AI Behavior Insights

• Emphasis on AI functioning through probabilistic outcomes; examples include ChatGPT providing varied responses based on input.

Balancing Control and Flexibility

• The framework reconciles deterministic control with stochastic flexibility to prevent erratic AI behavior while allowing adaptability through self-programming and correction mechanisms.

Understanding the Three-Layer Architecture in Automation

Overview of the Three Layers

• The architecture consists of three layers:

• Directive Layer: Indicates what actions to take.

• Orchestration Layer: Involves decision-making influenced by artificial intelligence.

• Execution Layer: Responsible for carrying out tasks.

Framework Dough and Watt Approach

• This framework is referred to as "Dough," encompassing:

• Directives (D): Represent workflows.

• Orchestration (O): Refers to agents.

• Execution (E): Pertains to tools.

• Alternatively, it can be viewed through the Watt approach, where:

• W = Workflows

• A = Agents

• T = Tools

Practical Application Steps

• To implement this framework, follow these steps:

• Create a project folder.

• Place the specified file within that folder.

• Initialize the project accordingly.

Project Initialization and AI Modes

• The initialization command involves specifying operational modes for AI:

• Fast Mode: Executes tasks directly without prior planning.

(Selected for speed)

• Planning Mode allows users to review plans before execution, ensuring requirements are met.

Interaction with the System

• Users will interact with various buttons during automation processes, allowing them to manage task completion effectively.

• The system generates folders for directives and execution but does not create one for orchestration since it operates via AI models managing both layers.

Project Structure and Environment Variables

Directory Structure Insights

• Upon successful initialization, a structure is created including:

• A directory for operations and guidelines in Markdown format.

(Currently contains a README file)

• An .env file is generated to store environment variables such as API keys necessary for operation. This replaces credentials used in N8n systems.

Defining Automation Tasks

• Users can specify automation requests without needing detailed flow construction; they simply provide clear requirements.

• The AI handles complexity in developing automation solutions based on user specifications rather than manual configurations.

Utilizing Appify for Web Scraping

Introduction to Appify Actor Usage

• An actor from Appify is employed for web scraping tasks specifically targeting Google Places data.

(Appify provides initial credits upon registration)

Targeting Leads in Specific Regions

• The goal is to find leads interested in automation services within Mexico's Bajío region while collecting their email addresses for marketing purposes.

This structured overview captures key insights from the transcript while providing timestamps linked directly to relevant sections of the video content, facilitating easy navigation and study efficiency.

Implementing Apify Token for Scraping

Setting Up the Apify Token

• The interaction with the AI concludes, prompting the user to implement their Apify token, which is essential for operation. This token can be retrieved from the settings under "API and integration."

• After copying and pasting the token into the required field, it’s crucial to save changes to ensure proper functionality before starting the scraping process.

Monitoring Scraping Process

• As scraping begins, users can observe credit consumption on their Apify platform. It's important to note that tokens have limits, especially when transitioning from testing to production.

• Users are warned about potential depletion of free Google tokens during extensive operations; thus, a financial investment may be necessary for continued use.

Results and Data Collection

• The system indicates that files are generated in a designated TMP folder where results are stored. In this instance, leads related to medical contacts were successfully scraped.

• A total of 70 leads were collected; however, no direct email addresses were found due to limitations in data availability from Google Maps.

Addressing Limitations and Next Steps

• The AI suggests executing a second phase using collected website data to extract specific email addresses from contact pages. It also offers an option to expand searches into other cities if initial results are satisfactory.

Evaluating N8n vs. Agent Workflows

Comparing Framework Capabilities

• Discussion arises regarding whether agent workflows signify the end of N8n's utility. While effective for lead scraping and content generation, there remains uncertainty about its applicability across all automation scenarios.

Visual Interface vs. Code-Based Automation

• N8n's strength lies in its visual interface which aids understanding of workflows compared to code-based agents that lack tangible representation.

Triggers and Execution Logs

• Despite agent workflows being triggered through N8n, they do not provide the same level of visibility as N8n’s logs and execution records offer within its graphical interface.

N8N and the Future of Automation: Will It Survive?

Competition and Infrastructure Challenges

• N8N is already standardized in many companies, providing a reliable infrastructure for automation processes.

• The challenge lies in competing with established infrastructures and security measures that N8N offers, as opposed to less standardized approaches like agent-based flows.

Complementing Approaches: Deterministic vs Stochastic

• There is potential to combine N8N's deterministic flow approach with stochastic, probabilistic methods from agent-based flows. This could enhance automation capabilities.

Community Engagement and Future Exploration

• The speaker invites audience feedback on whether they believe N8N will disappear or evolve alongside new technologies, indicating ongoing exploration of this topic in future videos.

• Emphasis is placed on studying real-world use cases rather than just theoretical examples available online.

Lowering Barriers to Entry by 2026

• By 2026, the entry barriers for creating sophisticated products will decrease significantly, allowing even those without technical knowledge to develop impressive solutions using natural language interfaces.

Increased Competition and Market Dynamics

• As barriers lower, competition will naturally increase; creating applications and prototypes will become easier for everyone involved in tech development. This shift means success won't solely depend on tool proficiency but also on effective business strategies.

Building Solutions for Profitability

• The importance of not just developing skills but also focusing on monetizing solutions through sales channels is highlighted; practical application matters more than mere technical ability.

New Initiatives for Freelancers

• A new initiative called "Ruta 1K" aims to help freelancers earn their first $1,000 monthly using AI services; further routes are planned for scaling agencies in the future.

Focused Learning Environment

• The community offers networking opportunities between technical developers and marketers, along with classes focused on both technical skills and sales strategies to foster collaboration and growth within the industry. Sessions include workshops on sales techniques and automation security practices.