Tammuz Dubnov, Founder and CTO of AutonomyAI, explores the evolution from unstructured “vibe coding” with AI to systematic “vibe engineering” practices that embed AI within proper constraints and architectural standards.

The Problem with Vibe Coding

The age of “vibe coding” has arrived, where developers use AI to rapidly generate code through informal prompts and intuitive interactions. While this approach thrives in early project phases—with no existing code to integrate, no test suites to break, and no edge cases to worry about—it quickly becomes problematic in production environments.

Vibe coding creates a seductive cycle: drop in a prompt, get runnable code, and ship. However, this approach leads to predictable failures:

  • Security vulnerabilities: Generated logic may lack proper authorization checks
  • Architectural inconsistencies: Naming conventions vary, business logic becomes tangled with UI code
  • Code duplication: Reusable components are rewritten from scratch
  • Maintenance nightmares: One patch breaks another, confidence drops, and trust in AI workflows erodes

The Shift to Vibe Engineering

The solution isn’t to reject AI but to evolve how we use it. Vibe engineering retains the generative power of AI while embedding it within structure, intent, and constraints. This approach transforms developers from code authors to system orchestrators.

Instead of prompting “write a billing function”, developers guide AI with specific context: “Extend the existing processInvoice() logic to support usage-based tiers. Use formatCurrency() from utils. Apply the same access checks used in subscriptions.ts.”

This structured approach ensures AI operates within boundaries, with proper context and accountability. The result is code that not only works but fits seamlessly into the existing architecture.

The Developer’s Evolving Role

To succeed as a system orchestrator, developers must adopt five critical practices:

1. Think Beyond Tasks—Think Systems

Don’t just solve tickets; consider what part of the system needs to evolve to support changes cleanly and durably. Before starting work, ask: “What concept or layer is missing that would make this feature natural to support?”

2. Codify Rules for AI

Define architectural standards, style conventions, and workflow expectations before generating code. Document the rules your codebase follows and turn them into constraints that AI can work with, ensuring it writes code like a team member.

3. Direct AI Toward Reuse and Improvement

Guide AI toward current, clean parts of your codebase while excluding legacy code. Reference reusable components and utilities explicitly. Encourage AI to evaluate and improve what it touches, identifying duplication, tight coupling, or outdated logic.

4. Adopt Test-Driven Development

Make test-first workflows your default. Ask AI to write tests based on your intent before implementation begins. This surfaces assumptions, contradictions, and vague logic early in the process, ensuring alignment between human intent and AI interpretation.

5. Stay Synchronized with Codebase Evolution

Use AI tools that summarize pull requests and track code evolution. Maintain architectural awareness as AI speeds up development. Subscribe to merged PR summaries and regularly review system changes to make informed decisions and avoid drift.

The Future of AI-Assisted Development

Modern software orchestrators think in systems, define intent clearly, use AI as a collaborator rather than just a code producer, and build with future-readiness in mind. This represents a fundamental shift from improvisation to orchestration.

Vibe engineering means working with AI agents that parse repositories, understand architecture, and reuse components intelligently. Code doesn’t just work—it fits. Tests are integrated from the start, secure defaults are assumed, and architectural patterns are respected.

The result is a development process that scales without breaking, producing code that can be confidently dropped into pull requests, reasoned about, and extended. This evolution from “vibe coding” to “vibe engineering” represents the maturation of AI-assisted development from a prototyping tool to a systematic engineering practice.

The message is clear: less riffing, more orchestrating. That’s a future we can actually maintain.

This post appeared first on The New Stack. Read the entire article here.