Vibe Coding and AI Agents: How to Transform Productivity into Strategy

Digital transformation in companies has ceased to be a long-term plan and has become a daily necessity for survival and operational efficiency. In the technology and B2B ecosystems, the convergence between Generative Artificial Intelligence (GenAI), low-code/no-code systems , and data orchestration has created a new development and management paradigm: the transition from traditional development to Vibe Coding and the deployment of autonomous AI Agents.

In a recent episode of the Builders by Skyone, Daniel Tutida (CEO of EUNERD) shared valuable insights on how the intersection of AI, enterprise resilience, and data-driven automation is reshaping business.

In this dense and analytical article, we will explore how infrastructure modernization, unified data integration (iPaaS + Lakehouse), and the practical use of agent workflows are redefining productivity standards in the B2B market.

What is Vibe Coding and why does it matter?

Historically, software creation required long development cycles, from database design and architecture to manual coding and regression testing. The concept of Vibe Coding emerges as a direct evolution of this scenario, where the barrier between business logic and technical implementation is drastically reduced through the assistance of Language Modelers (LLMs).

In Vibe Coding, the developer or technology founder stops writing iterative lines of code and starts acting as a director or orchestrator. Software building becomes a fluid dialogue based on prompts, continuous iteration, and real-time validation.

The evolution of Low-Code/No-Code tools for GenAI ecosystems

This approach does not eliminate the need for good architectural practices. On the contrary: for Vibe Coding to result in enterprise-ready applications, a robust governance platform is necessary. Legacy no-code tools often generated technical debt or "spaghetti code." Today, modern ecosystems, such as Skyone Studio Creator, combine ease of creation via natural language with:

1. Dynamic Routing of LLMs (LLM Router): simultaneous access to multiple models (GPT-40, Claude 3, Llama, DeepSeek) without the need to maintain separate subscriptions or integrations for each API.
2. Security by Design (Private Security First): strict corporate governance rules, preventing destructive actions (such as dropping of tables in databases).
3. Continuous Vulnerability Analysis: automated scans of AI-generated code before deployment to production.

The need for business as a driver of automation

Adopting Artificial Intelligence simply as a fad rarely generates a Return on Investment (ROI). Generative AI and intelligent workflows gain real traction when they arise from resource constraints, whether capital, time, or a skilled workforce.

During the Builders, Daniel Tutida shared how the restructuring of EUNERD (a marketplace platform connecting B2B support technicians to medium and large companies) boosted the deep adoption of AI in its operations:

“The need arose from a practical business pain point: I had no money and few people. The question was: 'How can I be more productive?' I started testing agents for internal automation until I evolved to the product's core intelligence.”

Data structuring: the critical layer before AI agents

A common mistake in innovation initiatives is trying to implement autonomous AI Agents on fragmented, unstructured, or decentralized databases (the famous "garbage in, garbage out").

For a tool or agent to make actionable decisions, the company needs to establish a unified data layer.

1. Integration via iPaaS (Integration Platform as a Service)

Eliminating data silos by natively connecting ERPs, CRMs, emails, and ticketing systems. Workflow automation ensures that information flows without the need for manual manipulation in isolated spreadsheets.

2. Lakehouse Architecture

Centralization of large volumes of data (structured and unstructured) with optimized query capabilities for Business Intelligence (BI) and Machine Learning.

3. Context Protocols (MCP) and Retrieval-Augmented Generation (RAG)

Connect specific knowledge bases (e.g., technical manuals in PDF format, service records) to AI agents.

Regarding the practical application of this architecture in field technical support, Daniel Tutida explained:

Practical use cases: where AI Agents are making an impact in B2B

Agent-based automation goes beyond simply responding to chatbots. It's about goal-oriented behavior, where the agent understands the intent, breaks down the problem into steps, selects the necessary tools, and executes the task.

Four main areas of application for AI Agents in corporate ecosystems stand out:

The role of infrastructure and cybersecurity in the age of AI

No data ecosystem or AI agent thrives on unstable or insecure infrastructure. Modernizing legacy applications and ERPs is the first step in ensuring that the company is "AI-.

Migrating monolithic systems to the cloud through solutions like Skyone Autosky allows companies to maintain the stability of their legacy systems without needing to rewrite code from scratch. From the moment the infrastructure runs in the cloud under a Zero Trust, with end-to-end encryption and Single Sign-On (SSO), the data becomes fully accessible to power integration buses (iPaaS) and AI models.

Corporate leadership must view cybersecurity and infrastructure not as operational costs, but as the foundation upon which data governance and autonomous agents operate.

The future of hybrid work between humans and AI

As discussed by Robson Del Fiol and Daniel Tutida, the era of artificial intelligence is not about replacing human intuition, but rather about increasing the analytical and execution capabilities of professionals and managers.

Technology leaders and managers who want to keep their organizations competitive should prioritize three pillars in their strategic planning:

1. Data sanitization and governance: getting your house in order before connecting predictive models.
2. Culture of safe experimentation: encourage Vibe Coding and the use of agents in a controlled environment (with sandboxes and anti-data leakage policies).
3. Focus on solving real problems: applying AI where there are clear bottlenecks in terms of scale, time, or manual processing.

Do you want to delve deeper into this conversation and see how real-time applications are built?

Listen to the full episode of the Builders by Skyone with Daniel Tutida (CEO of EUNERD) directly on Spotify! Discover stories about grassroots entrepreneurship, productivity hacks, and behind-the-scenes glimpses of the tech ecosystem.

Written by Skyone