From vibe coding to agentic software engineering: how AI is transforming development

Introduction

Describing what a program should do in natural language seems simple, but implementing that idea reliably is far more complex. The concept of vibe coding, which gained prominence in 2025, promises to lower the barrier to entry for programming by allowing developers to guide AI with commands and descriptions instead of writing every line of code manually.

What is vibe coding?

In vibe coding, a professional provides a description of what they want to create, receives code generated by AI, and continues to refine the result with new instructions. In many cases, it is possible to build a functional application without having a deep mastery of programming techniques.

This approach is effective for quickly testing ideas, but it falls short when the code needs to support production systems, handle large volumes of traffic, or integrate with other applications. Additionally, the traditional practice required intensive revisions and did not include essential steps such as planning, testing, deployment, and software governance, according to an article in Baguete.

In light of these limitations, a more structured methodology is beginning to gain traction: Agentic Software Engineering.

From conversation with AI to a complete process

The main change lies in the role AI plays during the development cycle and in the involvement of specialized agents at different stages.

An agent can receive a specification, design a solution, write the code, run tests, identify bugs, and apply fixes. Other agents can take on different roles within the same workflow, autonomously distributing responsibilities.

The human professional continues to define what needs to be built and evaluate whether the result meets the requirements, but now oversees a chain of tasks executed by AI with greater independence.

Governance and risks

The more steps are delegated to agents, the greater the risk that an error will propagate from one phase to another. Code that works in an isolated test may encounter problems when integrated into a larger system, and a seemingly simple change can affect components that were not initially in scope.

Therefore, AI-driven development must incorporate clear rules for testing, review, security, and access control. Autonomy does not eliminate the need for supervision; it simply redefines where that supervision takes place.

Agentic Software Engineering envisions a supervised autonomy, where agents plan, execute, test, and correct tasks while professionals monitor the process.

Impact for those working in technology

Knowing how to write code remains relevant, but mastery of requirements, architecture, testing, security, and system operation becomes even more important when part of the execution is delegated to AI.

This also creates new opportunities for professionals who can transform a business need into a clear specification and monitor technical execution without having to manually perform every step.

The democratization of programming, therefore, does not end when AI starts writing code. The next challenge is to ensure that this code is reliable, secure, and suitable for the environment in which it will be used.