Architecture Obsolete in 18 Months? Experts on Technical Debt

Software architecture was once designed with the expectation that it would support a business for years. That assumption is becoming harder to defend.

Cloud platforms change. Privacy requirements evolve. AI introduces new development patterns and dependencies. Business models move faster than the release cycles and data structures built to support them. The result is that technical debt can accumulate long before a system looks obviously outdated.

For CTOs, the more important question is no longer simply whether a system is old. It is whether the architecture can still absorb change without making every new feature slower, riskier, and more expensive.

That distinction runs through the experiences of technology leaders working across AI, customer platforms, enterprise systems, healthcare, compliance, and digital commerce. Their advice points to a different approach to architecture modernization: build systems with the expectation that parts of them will eventually need to change.

Not everything needs to be rewritten. But very little should be considered permanent.

When technical debt becomes an architectural problem?

A messy module is not necessarily a crisis. Neither is every piece of legacy code.

The more serious form of technical debt appears when problems spread beyond the team or component that created them. A small change begins affecting unrelated services. Releases require coordination across multiple teams. Engineers cannot modify one part of an application without worrying about what might break elsewhere.

For Harrison Tang, CEO and co-founder of Spokeo, the test is straightforward: “If every feature costs more than the last, it’s architectural. A messy module is just a sprint of cleanup.”

Sanjay Kukreja, Global Head of Technology, eClerx, makes a similar distinction through containment.

“My test is containment,” he says. “Debt that sits inside one silo, such as one team, one module or one codebase, is manageable: that team can see it, own it and fix it in its normal cycle.”

The dangerous kind, he argues, is debt that crosses organizational boundaries.

“Debt that cuts across silos is the dangerous kind. Fixing it needs several teams, shared data contracts and coordinated releases, so it keeps getting deferred and the interest keeps compounding.”

Dmitry Nazarevich, CTO at Innowise, describes the same problem from the perspective of system dependencies.

“If a piece of code is messy but you can fix it without touching the rest of the system, that’s manageable,” he says. “But when a small change in one module keeps breaking things elsewhere, or you can’t deploy one component without rebuilding the whole application, we’re dealing with something more serious.”

This is where architectural technical debt separates itself from ordinary code quality problems. The issue is not simply that the code needs improvement. The architecture has made change itself expensive.

For CTOs, one useful measure may be the number of existing components that must be touched to deliver something new. The larger that number becomes, the more likely the organization is dealing with a coupling problem rather than an isolated engineering issue.

Software architecture is aging because the business is moving faster

The speed at which architectures become obsolete is often blamed on technology. That is only part of the story.

Kukreja argues that architecture now has to keep up with changing business models as well as changing technology.“Architecture used to have to keep up with technology,” he says. “Now it also has to keep up with the business.”

He points to work with a B2B MRO client launching new products quickly while repeatedly changing the data required for those launches. An architecture built around fixed data models and fixed release cycles could not easily absorb that pace of change.“It does not fail because the technology aged,” Kukreja says. “It fails because the business moved faster than the design assumed.”

That observation has important implications for software architecture design. An architecture can be technically sound and still become a constraint if it was designed around assumptions the business no longer shares.AI is adding another layer of pressure.

Roland Jakob, founder of Praxica, says development velocity has outpaced architectural discipline.

“AI coding agents have made that gap even wider,” he says. “We can ship features in hours instead of days, but faster implementation doesn’t automatically mean better system design.”

That creates a new risk for CTOs. Organizations can now generate code, dependencies, integrations, and AI-enabled workflows faster than they can understand their long-term architectural consequences. Speed is valuable. But without strong boundaries, speed can simply accelerate the accumulation of technical debt.

Build boundaries before you need to replace something

The experts repeatedly return to one architectural principle: systems should be designed so that individual components can change without destabilizing the surrounding system.

Tang describes the approach simply:

“Clear boundaries. When each component sits behind a stable interface, we can swap it out without disturbing everything else.”

Jakob similarly favors modular components, explicit API contracts, separation of concerns, and well-defined service boundaries.

“The architectural metric I care about isn’t how quickly we can ship the next feature,” he says. “It’s how many existing components we have to touch to ship it.”

That is a useful challenge to the traditional obsession with feature velocity. A team may ship quickly today while quietly increasing the cost of every future change.

Kukreja highlights two software architecture patterns that have mattered most in his experience. The first is event-driven decoupling.

“When components communicate through events rather than direct dependencies, you can replace one without breaking the others,” he says. The second is keeping frequently changing business rules out of the core code.

“Rules change far more often than the platform underneath,” Kukreja says. “If they sit in configuration or a rules layer, the business can change them without an engineering release, and the core system stays stable.”

The principle is especially important for systems that are expected to evolve around AI. Model providers, frameworks, tools, and integration requirements can change quickly. Designing those choices into every application component creates a large blast radius when one of them eventually changes.The alternative is to make replacement an expected part of the architecture.

Refactor, modernize, or replace? Start with the cost of doing nothing

The decision to refactor or replace a system is rarely as simple as comparing old technology with new technology.

Tang says he asks engineers what it will cost to leave something alone for another year.

“If that cost keeps rising, we act.”

Kukreja uses a different test: whether the component is a differentiator or a commodity.

“You invest in and evolve differentiators,” he says. “You replace commodities with whatever the market does well.”

That approach can prevent organizations from spending years modernizing systems that do not create meaningful competitive advantage.

Nazarevich is more cautious about complete rewrites.

“I don’t like rewriting from scratch, unless there’s a really strong reason, “he says. “Old code has years of business logic baked in, and a lot of it is edge cases nobody remembers or wrote down.”

Instead, he prefers understanding what can be safely separated and modernized incrementally.

That distinction matters in legacy system modernization. The age of the code is not always the strongest argument for replacement. A system may contain valuable business knowledge that has never been fully documented.

Jakob takes a similar approach.

If the underlying design is sound, incremental software refactoring may make more sense. If teams are constantly building workarounds around a component’s limitations, however, he would consider isolating it behind an interface and progressively replacing it.

The best modernization strategy may therefore be neither “keep everything” nor “rewrite everything.”

It is to reduce the blast radius, isolate what needs to change, and replace components in stages.

AI is creating a new category of technical debt

AI-assisted development introduces a problem that traditional technical debt management practices do not always capture.

Jakob warns that coding agents can produce working software while still creating architectural inconsistency.

“Coding agents can generate perfectly functional implementations while duplicating business logic, introducing incompatible abstractions, or bypassing established patterns,” he says.

The challenge is not simply whether the generated code works. It’s maintaining architectural consistency across successive iterations.”

Tang sees a similar risk.AI can write code faster than we can understand it,” he says. “We review it like any other code and require it to follow our existing conventions.”

But AI technical debt goes beyond generated code.

One expert contribution highlights a form of AI debt that can remain largely invisible to conventional code review: prompts, evaluation sets, model versions, and tool permissions with no clear ownership.

The recommendation is straightforward: every prompt, evaluation set, and permission should have a named owner and be versioned alongside the code. Evaluation and safety checks should be treated as part of the delivery process, with failures capable of blocking deployment just like other software tests.

This is a significant shift in how CTOs may need to think about technical debt in software development. An AI system can be architecturally fragile even when the application code itself looks clean. If the model changes, permissions expand, prompts evolve, or evaluation criteria disappear into undocumented workflows, the organization can accumulate operational debt that is much harder to see.

The architecture therefore needs to account for governance, observability, audit trails, and human oversight from the beginning rather than adding them after deployment.

Do not automate a broken workflow

One of the more immediate risks of AI adoption is adding intelligent automation on top of processes that should have been redesigned first.Kukreja describes the pattern he worries about most as “agent orchestration glued onto legacy workflows.”

“Teams put agents on top of processes that were designed for humans and older systems, and they end up automating the inefficiency along with the work,” he says. “It looks like progress, but it adds a new layer of brittle dependencies.”

His response is the STAIRS framework:

• Strategic Alignment: Is the workflow worth keeping at all?
• Technology Readiness: Are the data and systems underneath fit for agents?
• Agentic Adoption: Redesign the process for agents rather than bolting them on.
• Implementation and Validation: Prove it under real conditions.
• Roll-out and Scale: Expand only after the earlier stages hold.

“The discipline is to fix the workflow before you orchestrate it,” Kukreja says.

That lesson extends beyond AI. Architecture modernization can fail when organizations focus on replacing technology without reconsidering the assumptions embedded in the process around it.

A modern interface connected to an outdated workflow is not necessarily modernization. It may simply be a newer layer of complexity.

Technical debt management cannot wait for a cleanup quarter

Most technology leaders understand that technical debt needs attention. The harder problem is protecting the time to deal with it when product roadmaps are under pressure.Tang says his team protects “a steady slice of engineering time for modernization, because waiting for a big cleanup project never works.”

Jakob also rejects the idea of treating debt as a separate initiative that engineering addresses only when there is spare capacity.

“I’d incorporate debt reduction into the delivery cycle,” he says, prioritizing components with high change frequency or operational risk while reserving engineering capacity for better interfaces, testing, and observability.

Kukreja takes the same idea one step further: tie debt reduction directly to product initiatives.“Standalone tech debt sprints usually lose to the roadmap,” he says. “When remediation is part of a feature the business wants, it gets funded.”

This may be one of the most practical answers to how to manage technical debt.

Technical debt often loses because it is presented as an internal engineering concern. It becomes easier to prioritize when the organization can see its connection to product delivery, customer capacity, operating costs, or business growth. Kukreja experienced this directly during a shift from a ser’vices-led model toward one built around technology and intellectual property.

“Debt stopped being an internal engineering cost,” he says. “It became a product liability that directly affects how fast we can ship and how many clients we can serve.”

Composable architecture can reduce the cost of change

The strongest examples of successful modernization in the expert contributions share one characteristic: they avoided a single, high-risk overhaul.

Tang describes putting “a stable interface in front of a core data component instead of rewriting it,” and then gradually replacing the pieces behind it.

“We avoided a feature freeze and a risky cutover,” he says.

Kukreja offers a larger example through Market360, a digital shelf platform designed as a composable architecture rather than a monolithic product.

Data collection operates through a reusable connector repository, with each retailer, marketplace, or quick-commerce site handled through a separate connector. When a client needs another source, the team can add a connector rather than change the entire platform.

When the platform moved toward an agentic model, the work was separated into four layers: Detect, Diagnose, Decide, and Deploy.

Each layer can evolve independently.

“The decision logic, including pricing guardrails, sits in configurable rules rather than in code,” Kukreja explains.

That allows different actions to be automated or escalated to humans based on defined rules. New capabilities can be added as endpoints in the deployment layer without requiring the organization to redesign the entire system.

Nazarevich describes a similar incremental approach with a healthcare client whose monolith was struggling with performance and scalability. The team mapped dependencies, identified components that could be safely separated, and moved them into independently deployable microservices.

The key was not the fashionable use of microservices architecture. It was the ability to separate and scale the parts of the system that needed to change without repeatedly redeploying the entire application.

That distinction matters. Microservices are not automatically the answer to technical debt. But a modular architecture can make future change less disruptive when the boundaries are genuinely useful.

Make model choice replaceable

AI systems introduce another reason to design for disposability. Models change quickly. Vendors change. Costs, latency, capabilities, and governance requirements can all shift.

One expert contribution recommends keeping models behind a narrow gateway interface so application code does not call individual vendors directly. Model selection can then be treated as configuration rather than a complete re-platforming project.

The same architectural approach supports fallbacks, per-model cost attribution, and the ability to test new models without rewriting the application.But replaceability does not eliminate governance.

A model or vendor change may be technically simple while still changing the behavior of the broader system. For that reason, model changes should be re-tested before deployment and reviewed as part of the architecture and operating process. The goal is not to assume that every component will remain stable. It is to ensure that changing one component does not force the organization to rebuild everything around it.

The CTO’s architecture question is changing

The traditional question was: How quickly can we build this? The emerging question may be: How easily can we change it later?That is the underlying theme across these expert perspectives on technical debt, AI, and software architecture.

A system becomes dangerous when its cost of change rises faster than the value it creates. The warning signs may appear as growing coordination requirements, repeated regressions, rising maintenance costs, increasingly fragile integrations, or teams avoiding necessary changes because they fear what will break.CTOs cannot eliminate technical debt entirely. Nor should they try to create an architecture that never changes.The better goal is to build for controlled replacement.

That means stable interfaces, clear service boundaries, modular components, explicit ownership, strong testing, observable systems, and governance that evolves alongside the technology.It also means challenging the assumption that modernization must arrive as one large transformation.

The most durable systems may not be the ones designed to last forever. They may be the ones designed to let important parts of the architecture become obsolete without taking the rest of the business down with them.

In brief

• Measure the cost of change, not just the age of the technology. If every new feature requires more teams and more coordination, architectural technical debt may be accumulating.
• Distinguish isolated problems from systemic coupling. Manageable technical debt can usually be contained. Architectural debt spreads across services, teams, and workflows.
• Modernize incrementally where possible. Stable interfaces can allow organizations to replace components without freezing product development.
• Treat AI technical debt as more than generated code. Prompts, model versions, evaluation sets, permissions, and tool integrations also need ownership and governance.
• Fix workflows before automating them. AI can scale inefficiency just as effectively as it can scale productivity.
• Build debt reduction into product delivery. Technical debt management is more likely to survive roadmap pressure when it supports a business initiative.
• Design for replacement. Models, vendors, frameworks, and individual services will change. A modular architecture can reduce the cost and risk of adapting when they do.

For CTOs, disposable systems do not mean disposable engineering. They mean building software architecture with a realistic understanding of technology: the components will change, the business will change, and eventually, some of today’s best decisions will need to be replaced.