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AI agents no longer just output a few lines of code: they immediately outline an implicit architecture. In a matter of seconds, a minimal prompt is enough to generate a complete scaffold, but one that lacks intent or systemic vision.
This speed akin to “vibe coding” risks locking in a default design that becomes the foundation of your production applications. The question is no longer whether the code works, but whether this architecture will hold up against growing usage, resilience requirements, and the constraints of a often-complex legacy environment.
Default Architecture of Vibe Coding
AI agents generate an architectural skeleton without explicit context. This default “scaffold” becomes the foundation of your application, even if it was never designed to endure.
Agent’s Implicit Decisions
When an AI agent receives a simple instruction, it doesn’t just write code: it chooses a framework, organizes folders, and defines data flows. These decisions rely on generic patterns rather than your specific needs, as the agent maximizes simplicity and coherence in the code it produces.
In the absence of precise instructions, it favors the most direct path, the so-called “happy path.” Any non-standard condition or edge case is often omitted, reinforcing the idea of an architecture tailored to an MVP rather than an enterprise-grade service.
The result: you get an initial project that “works,” but already includes organizational choices and dependencies ill-suited to modular evolution or strict governance.
Impact on the Initial Code
The code generated by “vibe coding” tends to concentrate business logic in routes or controllers, with no clear separation of responsibilities. This approach fosters a raw monolith, where each new feature naturally spills into the same file.
The lack of dedicated layers for services, persistence, or data validation complicates unit testing and continuous integration. Every refactor thus becomes an expensive undertaking, as it requires untangling a dense network of dependencies and side effects.
In practice, the initial speed comes at a high cost during subsequent evolutions: each extension or fix poses a risk of breaking the entire system.
Concrete Example of a Minimalist Blog REST API
An SME in the Swiss financial sector tested an AI agent to generate a REST API for managing blog posts. The initial code grouped HTTP routes, SQL queries, and validation logic into a single file. The project was ready in under five minutes, but the client quickly realized that adding a simple tagging feature broke the entire structure.
With no separate service layer or dedicated persistence modules, each developer risked overwriting critical code when adding business logic. This example shows that the scaffold generated by the agent held up for a prototype but not for a production project with multiple teams.
It illustrates how vibe coding “freezes” architecture in a default form, without anticipating a long lifecycle or multi-point collaboration.
Danger of Legacy on Architecture
Historical systems are full of implicit business rules and undocumented debt. An agent optimizing locally without full context risks breaking a critical flow.
Local Optimization vs System Understanding
Agents are designed to excel at “micro-tasks”: they identify a specific problem and propose a targeted solution. In a legacy ecosystem, each module is embedded in a web of undocumented interactions that don’t surface in the prompt.
When the agent modifies a component, it focuses on the function at hand without analyzing the impact on the entire system. Unit tests often lack sufficient coverage to catch these breaches, allowing systemic regressions to slip through.
The real challenge of legacy isn’t syntax or technology: it’s the historical context and dynamics that justify each workaround and dependency.
Risks of Modifying Legacy Systems
In a legacy context, the agent might “clean up” what appears to be superfluous code, even though those fragments were workarounds for technical or regulatory limitations. Removing a validation snippet can introduce a critical security vulnerability or break data integrity.
Similarly, the agent might introduce a new dependency without assessing its impact on existing deployment processes, creating a misalignment between CI/CD pipelines and compliance requirements.
These local modifications can trigger cascading incidents, as each micro-change disrupts a network of implicit rules accumulated over years.
Concrete Example of a Ten-Year-Old Platform
A major Swiss logistics company was running a platform developed over ten years ago. A poorly scoped prompt led an agent to replace a demographic data validation module with a more efficient version, without considering a batch script that relied on that module to enrich a data warehouse.
The result: an interface returned empty fields and caused errors in billing reports. This downtime immobilized multiple services for two days, demonstrating that local optimization without global vision can break a critical workflow.
This situation highlights the systemic risk when entrusting legacy modifications to agents without first conducting a comprehensive analysis phase.
Edana: strategic digital partner in Switzerland
We support companies and organizations in their digital transformation
3 Criteria for a Vibe-Coded App That’s Not Ready
A “vibe-coded” application often lacks scalability, resilience, and robust practices. These deficits indicate significant architectural debt even before the first user at scale.
Scalability
A vibe-coded project doesn’t always clearly separate the compute layer from the storage layer. Requests remain blocking, with no caching mechanisms or load-distribution strategies.
Under traffic spikes, processing concentrates on a single node, creating bottlenecks. The agent didn’t anticipate pagination, throttling, or data partitioning mechanisms.
The result is an application that performs adequately for a few users but collapses when usage peaks.
Resilience
Retry, timeout, and circuit-breaker mechanisms are often absent because the agent focuses on the “happy path.” Unexpected errors are at best handled by a basic try/catch block, with no fallback plan.
In production, a failing external call can block an entire thread, triggering a domino effect on other requests. The agent didn’t generate a fallback or a deferred retry system.
Without a resilience strategy, a simple external service interruption becomes a total application crash.
Missing Best Practices
A vibe-coded app limits data validation to simple sample checks, without building DTOs or enforcing a unified schema. Security is treated as an option rather than a prerequisite.
Logs often reduce to console.log statements, with no structure or trace ID correlation. It becomes impossible to quickly diagnose the root cause of an incident or trace a request end to end.
The absence of automated tests and robust CI/CD pipelines prevents rapid, secure scaling and leaves the door open to insidious regressions.
Architecture-First and the Control Loop
“Vibe speccing” means generating a specification before producing code. Coupling this approach with automated audits allows you to measure and correct architectural drift continuously.
Vibe Speccing Before Generation
Before requesting code, ask the agent to detail the layers, responsibilities, and non-functional requirements. This spec must include modules, interfaces, and the patterns to follow.
By explicitly requiring controllers, services, repositories, and a validation schema, you turn the prompt into an official architecture document ready for approval by your architects.
This speccing phase limits the agent’s implicit choices and ensures structural consistency before the first line of code.
Prompting Playbook
Create prompt templates that enforce non-functional requirements: timeouts, retries, structured logs, systematic validation, and standardized JSON responses. These instructions become your internal cookbook for every AI agent.
Add requirements for separation of concerns, modular file structure, and no circular dependencies. Encourage the agent to document each generated layer and provide a project tree.
The more precise your playbook, the better the agent can produce code aligned with your standards and IT governance.
Observability and Automated Audits
Integrate architectural analysis tools that extract the real-time structure of your applications and detect coupling, hotspots, and drift from the initial spec.
These audits should generate actionable TODOs, listing non-compliance issues and suggesting fixes to bring your code back in line with the intended architecture.
By closing the change → measure → correct loop, you limit debt and ensure controlled industrialization of your AI solutions.
Move from Vibe Coding to Efficient Architectural Governance
AI agents accelerate production, but without architectural guardrails, they lock in a default structure and industrialize technical debt. By replacing “vibe coding” with “vibe speccing” centered on defining layers, responsibilities, and non-functional requirements, you transform each prompt into a validated architecture document. Add automated audits to measure drift and trigger corrective actions, and you achieve an agile, controlled, and sustainable workflow.
Our experts support CIOs, CTOs, and IT managers in implementing this architecture-first approach. We help you craft prompts, deploy observability tools, and establish governance that guarantees performance, security, and scalability.
