How does software architecture save the vibe coder?
Software architecture is one of the most expensive and underappreciated strategic choices an organization can make. It determines how quickly systems can be developed, how securely they can be changed, and how much they will cost to maintain from year to year.
When systems are at the core of business operations, software architecture is directly linked to the profitability of investments, the speed of development work, and how flexibly the organization can respond to change.
The rapid development of artificial intelligence has taken software architecture to a whole new level. With code being generated faster than ever before and more and more people building software from different backgrounds, structural errors are also occurring more quickly if software architecture is not under control.
This is particularly evident in a phenomenon known as vibe coding. In this phenomenon, a human gives high-level instructions to an AI agent about what the system should do, and the AI produces a large part of the implementation. Tools such as Codex, Claude Code, GitHub Copilot and Lovable make it possible to turn an idea into a working application very quickly.
Speed is the biggest advantage of this model. At the same time, it makes software architecture more important than ever.
- What software architecture means in practice
- How software architecture manifests itself in everyday business operations
- The software architecture of the vibe-coded application must also be able to withstand future requirements.
- Architecture is created by default, even if you don’t request it.
- Five signs that your vibe-coded application needs architectural clarification
- Vibe speccing helps guide the agent so that the structure does not break down.
- Artificial intelligence also helps analyse software architecture
- Architecture validation with tools
- Why software architects decide whether artificial intelligence will be beneficial or detrimental
- When a vibe-coded application needs to be taken into production
- When is outside help sensible?
What software architecture means in practice
Software architecture may sound like an abstract concept, but in practice it involves very concrete matters. It describes how software is built and how it can be developed safely in the future.
Software can be thought of as a building. In a building, the foundations, load-bearing structures and roof are not created on a whim, but are designed to withstand use and changing conditions. The same principle applies to software. When the responsibilities of the system are clearly divided into different parts, the whole remains manageable even when it is expanded.
Software architecture describes, for example:
- how the different parts of the system are divided
- which part of the system is responsible for which function
- how data moves within the system
- the principles according to which the system will be developed and expanded
If user interface logic, database queries and business rules are all mixed up in the same place, it is difficult to modify the system safely. When requests, business logic, data processing and integrations are separated into clear entities, changes can be precisely limited.
The key benefits of good software architecture are systematicity and stability. Developers know where new logic belongs, the effects of changes can be assessed in advance, and testing can be targeted at the right parts of the system.
How software architecture manifests itself in everyday business operations
In traditional enterprise systems, structural problems usually arise slowly, sometimes over a period of years. In AI-assisted applications, the same phenomenon can occur much more quickly. When code is generated quickly and structural decisions are made on the fly, architecture-related problems can start to appear within weeks.
Many people think of software architecture as merely an internal structure of the system. In reality, its impact is directly visible in everyday business operations in terms of how systematically information flows through processes and whether it remains intact between systems. How long does it take to process an order, how many times does the same information have to be entered into different systems, how much time is spent resolving errors, and how quickly are reports produced?
When the software architecture is unclear, processes easily begin to build around technical limitations. The same information is entered in multiple places, exceptions are handled manually, and integrations break down in unexpected situations.
When the software architecture is clear, processes can be automated and changes remain under control. Development work speeds up, the number of errors decreases, and the system can be developed with greater confidence.
For this reason, software architecture has a direct impact on, for example:
- length of development cycles
- maintenance costs
- speed of error correction
- the reliability of integrations
When the software architecture works, changes do not cause surprises. When it does not work, even a small change can spread throughout the entire system.
The software architecture of the vibe-coded application must also be able to withstand future requirements.
What just a few years ago required a development team, sprints and months of work can now be created in a few days under the guidance of a single person. Tools such as Codex, Claude Code, GitHub Copilot and Lovable make it possible to quickly turn an idea into a functional user interface, database and initial integrations.
As a result, more and more projects are progressing in the same way. The application exists and works. It can already be used to solve a specific problem, and sometimes it even has users. At the same time, further development of the system is becoming more uncertain. Adding a new feature breaks old functions, fixing a bug creates new problems, and the effects of changes are difficult to predict.
The problem is not usually the idea or the amount of code. Most often, it is that the structure of the application has been created without conscious architectural design. In other words, the software architecture does not yet support what is actually required of the product.
The intention is not to downplay vibe coding – quite the contrary. It is a highly effective way to get started quickly and test ideas in practice. However, as more features, users and integrations are added to the application, rapid development needs to be accompanied by structure, decisions and product-level thinking.
Architecture is created by default, even if you don’t request it.
One of the most important realities of vibe coding is this: architecture always emerges, even if you don’t specifically design it. When you give an agent the task of building an application, it has to make decisions. It decides how files are organized, how interfaces are formed, where data is processed, and where logic is placed. In other words, it creates software architecture in any case.
The problem is that without precise guidance, agents often favour simplicity over best practices. This is entirely logical. The agent’s goal is to get the solution up and running quickly, not necessarily to build a system that will withstand years of further development. As a result, the end result is often solutions where everything is too close together: business logic, database queries, integrations and the user interface are located partly on the same layers or even in the same files.
In a small demo, this is not a problem. However, in the first production version, it starts to take its toll. Each new feature adds dependencies between different parts of the system. Each exception increases the number of special cases. Each fix made in a hurry eats away at the clarity of the whole.
That’s why many vibe-coded applications seem very promising at first, but soon start to feel cumbersome to maintain.
Why does an AI-built application work at first but break down later?
Why does an application work well at first, but then become surprisingly difficult to develop later on? The answer is usually not “bad artificial intelligence” or “bad developer”. The answer is that the first version is usually built for a narrow problem, while later versions have to carry an increasing number of rules, users, integrations, and exceptions.
In the first phase, the agent performs local optimisations. It implements the form, saves the data and displays the result. Each step is a locally sensible solution. When the next feature is added to the system, the agent again makes a locally sensible solution. And the next one. And the next one.
The result could be a system in which:
- the same logic appears in several places
- components are tightly connected
- the effects of the changes are difficult to assess
- the scope of testing is growing rapidly
Initially, the system works well. Later, each new change feels a little more difficult than the previous one. At this point, it becomes apparent that the system has entered a phase where the software architecture is beginning to have a negative impact on the speed of development.
Five signs that your vibe-coded application needs architectural clarification
Many vibe coders recognise situations where the system works, but development is no longer as smooth as it was in the beginning.
- The first sign is that adding a new feature feels more difficult each time than the last.
- This usually indicates that the changes are no longer limited to a specific area but are spreading to several locations.
- Another sign is that you no longer trust the security of changes. Every release feels like a risk because you are not sure what the change might break.
- This is almost always the result of unclear dependencies.
- The third sign is that the same logic begins to appear in several places.
- Artificial intelligence quickly builds functional solutions, but without structural guidance, it also easily copies the same activity again instead of concentrating it in one place.
- The fourth sign is that integrations begin to dominate development. When payment, authentication, CRM, email, analytics or an old back-end system are added to an application, the internal structure of the system is put to the test.
- Without clear interfaces, the effects of integration spread too widely.
- The fifth sign is that you find it difficult to explain to another person how the system works.
- If the structure cannot be clearly described, it is also difficult to develop it further in a safe manner.
None of this means that the project has failed. It means that the project has moved into a phase where the product-level structure is becoming more important than the speed of the first version.
Vibe speccing helps guide the agent so that the structure does not break down.
The solution is not to abandon artificial intelligence, but to guide it better. This is where what could be called vibe speccing comes into play. The idea is simple: before artificial intelligence writes code, it is given a more precise picture of the system’s structure, responsibilities and quality criteria.
In practice, this is a transition from requesting features to controlling the system. Instead of requesting “order management,” we request that order management be built into a specific structure. Define the layers. Define data processing. Define error handling. Define logging. Define retry logic. Define interface principles.
A good prompt therefore describes not only the end result, but also how to achieve that result. This is an essential difference.
In practice, it is advisable to specify at least the following matters for the agent:
- which part of the code handles requests or the user interface
- which part contains the business logic
- which part is responsible for retrieving and storing data
- how errors are handled
- how logging and auditing are implemented
- how external integrations are separated from other logic
- how to manage dependencies
When these boundaries are clear, artificial intelligence produces significantly more sustainable code. This does not slow down development. On the contrary, it reduces the amount of subsequent repair work.
Artificial intelligence also helps analyse software architecture
When a vibe-coded application begins to accumulate more features and integrations, the biggest challenge is usually not writing new code but understanding the whole. A rapidly developed system often contains a lot of logic, dependencies and integrations whose actual structure is not immediately apparent from reading individual files. Decisions often have to be made based on an incomplete picture: where the code connects, what components are used, where performance is generated, and where the business logic is located.
Artificial intelligence can analyse the entire code base and reveal structural phenomena that would be slow to identify manually. Automatic analysis helps to form a technical picture of the system and reveals how the software architecture actually works.
The analysis can be used to identify, for example:
- interdependencies between components and the structure they form
- critical performance paths and load points in the system
- architectural anti-patterns, such as circular dependencies
- too broad responsibilities in individual components
- overlapping business logic in multiple locations
- actual use of interfaces and direction of data flows
When this kind of analysis is available, the discussion about software architecture changes significantly. The assessment is no longer based on guesswork about what might be a problem, but rather on a fact-based view of the system’s structure.
Architecture validation with tools
Simply reading the code is often not enough to evaluate the structure of the system, especially when a lot of the code is generated by artificial intelligence. That is why specialised analysis tools have been developed for evaluating software architecture.
For example, vFunction analyses the structure and runtime behaviour of an application and identifies technical debt in the software architecture. Such tools can reveal, for example:
- components that are too tightly connected
- domain boundary violations
- too broad categories or services
- code structures that complicate maintenance and changes
With analysis tools, refactoring is no longer a matter of guesswork. The result is often a prioritised list of structural problems, the correction of which will improve the software architecture the most. At its best, the analysis can be converted directly into tasks suitable for artificial intelligence or prompt frames, which can be used to refactor the code in a controlled manner towards a clearer structure.
However, tools do not make architectural decisions. Analysis reveals problems and their scope, but the decision to make changes still rests with the person who understands the system’s objectives, technical constraints and long-term development direction.
Why software architects decide whether artificial intelligence will be beneficial or detrimental
This is where many people make a mistake. They think that artificial intelligence is an automatic solution. In reality, artificial intelligence only works well when it is given an architectural context and clear boundary conditions. Without these, AI produces locally sensible solutions that can weaken the whole: increasing layer permeability, copying logic, bypassing common interface principles, or building new dependencies that should not arise.
The role of a software architect is not to slow down development, but to build a model for development in which speed does not compromise structure. In practice, this means that at least the layer structure and division of responsibilities, domain boundaries, integration principles, non-functional requirements and dependency rules must be defined clearly enough.
Once these are in place, AI speeds up development without disrupting the software architecture. It also helps keep the architecture consistent: documentation stays more up to date, recurring structures can be standardised, and the impact of changes can be better assessed.
From the perspective of a vibe coder, this does not mean that they should become an enterprise architect. It means that at some point, the project will need product-level thinking alongside it. At this point, an experienced software architect or product team can be a decisive help.
When a vibe-coded application needs to be taken into production
An application that works in the trial phase does not necessarily mean that it is ready for production. The first version built with vibe coding often solves one problem effectively, but production use brings with it completely new requirements.
When an application moves from testing to actual use, functional code alone is no longer enough. The system must also meet the requirements of production use. The system must function reliably in situations where there are many users, integrations fail, data grows rapidly, and changes are made continuously.
At the production level, software must generally withstand, for example:
- growing number of users and load
- external integration disruptions and timeouts
- information security requirements and access rights management
- locking, auditing and error traceability
- continuous development without breaking old features
Many vibe-coded applications work well in the first version, but start to break down in these situations. In most cases, this is because the software architecture does not yet support production use.
Therefore, the transition to production is often a phase that requires deeper architectural expertise. The work usually involves clarifying the current structure, separating critical responsibilities, removing dependencies, and ensuring that the system can withstand growth.
In practice, the work may involve, for example:
- breaking down the monolith into clear domain entities
- standardisation of interfaces and differentiation of integrations
- separation of business logic from infrastructure
- improving testability and system performance monitoring
- breaking down dependencies that slow down development
Artificial intelligence can significantly speed up analysis and refactoring. It helps identify overlaps, clarify code and generate documentation. However, direction does not arise automatically. The structure must be based on a clear software architecture that defines how the system will develop in the coming years.
When is outside help sensible?
A vibe coder does not need to know everything on their own. The whole idea behind AI-assisted development is that everything is no longer built from scratch by hand. The same logic also applies to the stage of software development where it is turned into a real, maintainable product.
An external software architect or experienced product team is often a sensible option when the application is already working but its further development is starting to slow down. Or when moving to production feels too risky. Or when the system structure has grown to a point where it is difficult to reliably understand the whole.
In such a situation, the most sensible solution is usually to understand the current system well enough, fix its most critical structural problems, and build a functional, maintainable product on top of it.
If you recognise your own application in this situation, we will be happy to help you evaluate your system’s software architecture and identify the next sensible steps for development.
Software architecture refers to the highest-level structure of a software system and the principles that keep the system understandable, extensible and maintainable. It describes the components of the system, how the components interact with each other, and the rules governing development.
Software architecture typically includes:
– components and their responsibilities
– interfaces, integrations and data flows
– layer structure and dependency rules
– non-functional requirements (security, performance, availability)
– principles guiding development to avoid architectural decay
A good software architect ensures that these do not remain on paper, but guide the work every week.
An architectural model is a common way of structuring a system. There is no single “best” model; the choice depends on the objectives of the system, the capabilities of the team and the amount of change. Software architecture can also combine several models.
The most common architecture models are:
Layered architecture
Layered architecture divides the system into, for example, a presentation layer, application logic and a data/integration layer. The model is popular because it is clear and facilitates the division of responsibilities. The challenge arises if the layers are constantly being breached and logic leaks into the wrong place.
Monolithic architecture
In a monolith, the system is a single entity that is deployed as a single package. A monolith can be very effective if it is modular internally and the boundaries are clear. The problem with monoliths is that they grow without clear boundaries and dependencies become entangled.
Microservice architecture
In microservices, the system is divided into independent services that are developed and published separately. This can bring scalability and team autonomy, but requires strong integration and observability skills. If poorly implemented, microservices increase management costs.
Event-driven architecture In
an event-driven model, components communicate through events, often asynchronously. This is particularly suitable for situations where you want to decouple parts of the system and improve flexibility. The model requires careful planning of event ownership and data consistency.
Hexagonal / Clean architecture In
these models, domain logic is kept separate from infrastructure. The goal is flexibility and testability: technology can be changed more easily when business logic is not tied to the details of the framework. This is often a strong choice for long-lived systems.
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AI agents as part of processes