YAGNI and Building Good Practices into Your Architecture

In software development, the mantra “You Aren’t Gonna Need It” (YAGNI) serves to keep projects lean, avoiding features or complexity until absolutely necessary. But over my 16 years in development, I’ve noticed that when it comes to architecture, this mindset often backfires. Certain patterns - like asynchronous programming, CQRS, event sourcing, or CI/CD - are not features we can add when we “need” them. Instead, they’re foundational practices that enable systems to handle growth and complexity gracefully.

In this post, I’ll explore why certain architectural paradigms are worth implementing early and how these preventative measures can spare teams from major refactoring later on. Along the way, I’ll introduce residuality theory, a mathematical concept that supports the idea that preventative architecture isn’t over-engineering - it’s simply good design.

The YAGNI Mentality and Its Limitations

The YAGNI principle is based on deferring the implementation of features or architecture until a demonstrated need arises. While this can be a helpful approach for speculative features, applying it too broadly to core architectural patterns like asynchronous programming, event-driven design, or CI/CD can lead to missed opportunities for building scalability and resilience.

When we postpone the adoption of practical, commonly needed practices, we often face technical debt, complex retrofits, and brittle systems that become difficult to maintain. I’ve seen countless projects where asynchronous capabilities or real-time features were added too late, causing more disruption than if they’d been built in early.

In other words, YAGNI is often misapplied. Foundational practices aren’t speculative; they’re strategic. By building them in early, we’re not over-engineering - we’re laying the groundwork for sustainable growth.

Patterns Worth Building in Early

Let’s take a closer look at a few architectural practices that, while sometimes dismissed as “premature optimization,” pay dividends in the long run.

Asynchronous Programming

In languages like Python, async programming is often introduced only when performance issues arise. However, by implementing async early, applications are better prepared to handle high volumes and avoid blocking operations from the start.

Benefit: Async programming brings not only performance gains but also positive habits, as it reinforces a mindset of handling concurrent operations in ways that scale well.

Event-Driven Architectures and Two-Way Communication

Adding real-time features such as server-sent events or websockets is often delayed until the business sees the need for real-time interactions. By implementing event-driven design from the outset, systems can easily adapt to real-time requirements, sidestepping costly retrofits.

Benefit: An event-driven model anticipates growth and helps maintain low latency, enabling the system to meet user demands for interactive, real-time experiences as they emerge.

Event Sourcing and CQRS

Event sourcing and CQRS are often labeled “over-engineering” because they add complexity to data management. But for applications where state changes need to be tracked (e.g., auditing or financial apps), these patterns simplify state reconstruction and provide valuable historical context.

Benefit: Event sourcing reduces future complexity by logging state changes, creating a clear, reliable audit trail, and making state evolution more manageable. CQRS helps separate write and read paths, providing a cleaner approach to managing complex data requirements as they arise.

CI/CD

Continuous Integration and Continuous Deployment (CI/CD) are sometimes put off until deployment frequency increases. However, establishing a CI/CD pipeline from day one fosters a culture of quality, rapid iteration, and automated testing.

Benefit: CI/CD is not just a productivity boost; it’s an enabler of early feedback, consistent releases, and reduced risk. By setting this up early, teams get into the habit of delivering reliable code continuously.

Preventative Design vs Speculative Design and Residuality Theory

While implementing architectural patterns early may seem like over-engineering, it’s critical to distinguish between speculative and preventative design. Speculative design makes architectural decisions based on hypothetical needs, while preventative design incorporates solutions that address inevitable, recurring demands. This is where Barry O’Reilly’s concept of residuality theory offers practical guidance.

Residuality theory, as refined by O’Reilly, builds on principles from complexity science, focusing on the “residues” left in a system after it undergoes stress. O’Reilly proposes that, rather than minimizing complexity by breaking systems down into traditional components, software architects should embrace complexity by designing for how a system will realistically be used and stressed over time. His approach views a system’s architecture as an adaptable collection of “residuals” or remaining structures, which handle common stresses in real-world environments—such as high user loads, real-time requirements, or evolving business needs.

Applying residuality theory to preventative design, we can justify implementing certain patterns early, not because they might be useful but because they address the kinds of challenges systems typically face as they grow. For instance, asynchronous programming or event-driven architecture minimizes “residual” complexity, allowing the system to scale without introducing excessive technical debt. This preventative focus aligns architecture with known complexities, creating adaptive structures that endure over time.

In this way, residuality theory supports the argument that “good engineering” is not over-engineering. It is a strategic choice to reduce the residual complexity we know will surface as systems evolve.

Addressing the Fear of Over-Engineering

The term “over-engineering” is often applied to these practices by developers who haven’t experienced the issues they’re designed to address. While true over-engineering - adding complexity without purpose - should be avoided, foundational patterns like async programming, event sourcing, and CI/CD are different. They reduce technical debt, streamline development, and support scalability, making them necessary components of good architecture, not extravagances.

Instead of dismissing these practices as “overkill,” it’s better to view them as preventive measures that reduce the likelihood of having to retrofit complex solutions later. When adopted thoughtfully, these patterns don’t complicate a system—they ensure that the system can adapt to future needs without leaving behind residual problems.

A Practical Guide: When to Use YAGNI and When to Ignore It

To balance the need for solid architecture with the need to avoid over-engineering, here’s a simple rule:

Apply YAGNI for speculative features that aren’t integral to the system’s core function. Avoid complex abstractions or optimizations unless there’s a clear business case.
Ignore YAGNI for Foundational Practices like async programming, event sourcing, CQRS, and CI/CD. These patterns are resilient by design, and they support future scalability and flexibility, making them a wise investment.
Apply YAGNI for prototypes and MVPs, as long as you are willing to pause development and address the technical debt as soon as the prototype and MVP have served their purpose. Alternative, resolve to throw away the product and rewrite properly once it's demonstrated.

This approach helps us make informed choices about what to build now, ensuring our systems are prepared for growth without adding unnecessary complexity.

Architecting for Tomorrow, Not Just Today

YAGNI is a valuable principle, but it shouldn’t overshadow the importance of building resilient, future-proof systems. Asynchronous programming; event-driven architecture; reactive systems, or conversely, choreographed systems; event sourcing; CQRS; and CI/CD are paradigms that future-proof applications, ensuring that they evolve gracefully without accumulating excess complexity.

With residuality theory in mind, it’s clear that preventative architecture isn’t about over-engineering - it’s about building systems that can scale with minimal technical debt. By making these architectural investments early, we set ourselves up for smoother development cycles, fewer retrofits, and systems that are ready to grow as user demands increase. Good engineering is not just what we need today but what will serve us tomorrow.