Technical debt is a sneaky hang-up that organizations and teams often ignore until it becomes unmanageable. It creeps in slowly, undermines your team’s productivity, and builds up until your product is hard to maintain and scale. Software systems and development cycles are increasingly being driven and supported by AI, continuous delivery, and cloud-native environments, making it critical to adopt a clear strategy for identifying and reducing technical debt.
In this guide, we’ll address what technical debt looks like for today’s organization, how it ends up embedded in your systems, and how to implement a practical, step-by-step roadmap to reduce it across your codebase and infrastructure.
Technical debt is a lot like other forms of debt . . . except for software. It refers to the different long-term costs of choosing an easy or fast software solution (or workaround) that ends up causing issues down the line.
This debt might come in the form of messy code, manual test protocols, poor documentation, or clunky system design that sucks up time, manpower, and resources. And just like financial debt, it accumulates interest over time — making future development slower, more error-prone, and more expensive, dragging down the needs of your business.
In the early days of software development, technical debt mostly referred to code quality as engineers built small hacks and workarounds to get the job done faster. But its scope has expanded over time as companies have grown, and it now includes legacy services no one wants to touch, cloud infrastructure built without infrastructure-as-code, AI-generated code that lacks human oversight, and even organizational practices that discourage cross-team collaboration.
Whether you're building monoliths, microservices, or APIs that orchestrate across environments, technical debt doesn’t discriminate by size or scale. It can sneak in anywhere, and it almost always grinds organizational productivity down, sometimes even to a halt.
No one sets out to create technical debt intentionally, yet nearly every software team accrues it in one way or another. It often begins innocently, either with a tight deadline requiring a shortcut or a team short on resources when architecting a new service. The problem is that these trade-offs compound such that the problem worsens quickly.
One of the most common culprits of technical debt is prioritizing speed over quality and sustainability. When teams are under pressure from higher-ups to meet product deadlines and quotas or to prove immediate value to customers and investors, they may resort to cutting corners to ship faster. While that might be justifiable in the short term, even to executive teams, if there's no plan to circle back and clean up the code and processes, those short-term gains turn into long-term pains.
Predictably, the next key factor is change. Companies grow, business requirements and timelines evolve, and the code can’t always keep up. A simple data model that once fit the MVP perfectly becomes problematic as features expand and use cases grow more complex. What’s more, this then forces future teams, who may not have the institutional knowledge or documentation to reference, to build on shaky foundations.
Lack of consistent engineering practices can also create a patchwork of code styles and architectural approaches, especially in larger organizations. If teams don’t follow shared design principles or airtight documentation practices, they’re susceptible to duplicating work or creating systems that are difficult to integrate.
And then there’s maintenance — or the lack thereof. Upgrading permissions, rewriting deprecated APIs, replacing outdated libraries: all of these tasks tend to get pushed to the bottom of the backlog, which snowballs into technical debt that can lead to serious security vulnerabilities and system outages.
Lastly, AI adds a whole new dimension to the issue by accelerating development tenfold, making verbose or inconsistent code a larger-looming threat.
So to reduce technical debt, should organizations just keep an extra vigilant eye on their code? Yes, but it’s not quite that simple. Read on to learn about some of the different kinds of technical debt.
Not all technical debt is built the same. Some of it is palpable and frustrating — like brittle code that breaks with every update or saps up engineers’ time — while some lies dormant until a crisis brings it to light. Understanding the different types of technical debt can help organizations identify, plan for it, and tackle it more effectively.
Let’s start with the type of technical debt we already mentioned: code-level debt. Sometimes in the early stages of a company’s development, engineering teams will build quick code hacks with duplicated logic, inconsistent naming conventions, or lack of modularity. Code-level debt is often the end result when teams are experimenting or under pressure to ship quickly, and while it might not cause problems immediately, it tends to make future work harder and more time-consuming. For example, a function with thousands of lines of nested logic might technically “work,” but making changes to it down the line is risky, difficult to test, and easy to lose track of.
Architectural debt shows up when the foundational structure of an application no longer fits the scale or complexity of your business. Maybe your monolithic application is holding back your teams’ autonomy, or your microservices are too fragmented and hard to coordinate. Over time, architectural debt increases latency, reduces resilience — especially without a enterprise ready service mesh in place, and adds cognitive load for developers.
Infrastructure debt is also common, especially in cloud environments. This type of debt can include servers that are configured manually instead of via Infrastructure-as-Code, CI/CD pipelines that rely on tribal knowledge, or outdated dependencies and libraries. These issues might not break your application right away, but they slow down deployments while increasing security risk and making onboardings and trainings more painful.
One of the more obscure types is test debt, which is often overlooked. A lack of automated tests — or tests that are unreliable — can dramatically reduce teams’ confidence in making changes or updates. They end up spending more time debugging or reverting changes than they do writing new features. This kind of debt directly impacts velocity.
Graduating into more advanced forms of debt, there's documentation debt, which includes missing or outdated API docs, architecture diagrams, onboarding guides, and internal runbooks. When knowledge lives only in someone’s head, it creates bottlenecks and makes cross-functional collaboration much harder.
Then there’s a newer kind of debt that’s emerged with the rise of AI in what some teams refer to as generated code debt. This happens when AI tools are used to rapidly write code that technically works and can get the job done, but isn’t optimized, documented, or aligned with organizational processes and conventions. AI can write quick code, but without refinement and oversight, it may be quietly adding to your stock of technical debt.
Lastly, process debt, which is one of the most harmful, occurs when workflows, team practices, or communication protocols are misaligned, outdated, or even just lost in translation. Your teams may skip code reviews for speed or neglect the tech cleanup during sprint planning. No matter what the reason, these gaps create productivity drag even when everything seems to be fixed.
Technical debt is like a leaky pipe: you can only patch up so many holes before the whole thing bursts. Now that you might have a better idea of what you’re facing, you may be wondering what’s actually worth fixing — and why.
Reducing technical debt starts with making it a priority. It may sound obvious, but many organizations push it down the list of to-dos until it becomes unmanageable. That's why agile remains one of the most effective frameworks for addressing debt + it encourages teams to regularly prioritize, inspect, and adapt.
Another critical step is making technical debt visible. You can't fix what you don't acknowledge, and you definitely can't fix what you don't know. If there's a chaotic subsystem slowing you down or a flaky set of tests breaking every other build, it needs to be tracked alongside feature work in the backlog.
To start making progress, many teams:
As you chip away at the debt, use it as an opportunity to improve your systems and engineering practices. Encourage your team to:
Ultimately, reducing technical debt is about building a culture that values long-term quality just as much as shipping velocity. Engineers should feel empowered to advocate for better code, and leadership should recognize how technical investments support faster, safer, and more sustainable delivery over time.
Reducing technical debt often means rethinking how services are built, connected, and maintained. Kong helps teams address some of the more common causes of debt by providing a consistent, centralized way to manage APIs and microservices. For example:
By giving teams better visibility and control over their service architecture, Kong can help reduce the complexity and inconsistency that often lead to technical debt — while also making it easier to pay that debt down over time.
By now, it should be clear that reducing technical debt isn’t a one-and-done project, but a strategic organizational effort and culture shift. Here’s an example of a high-level approach that many successful engineering teams use.
First, identify your major gaps and problem areas using examinations like static code analysis, architecture reviews, and one-on-one interviews with developers. Tools like SonarQube or DeepSource can help identify areas in your codebase with high complexity, low test coverage, or repeated bugs. And pay attention to that qualitative feedback: Where do pull requests get stuck? What’s brittle in production? Which parts of the system require deep institutional knowledge?
Some debt is architectural, like clunky infrastructure or a database schema that’s hard to change. Other debt is operational, such as manual deployments or lack of documentation, or purely related to the quality of the code. Categorizing it makes it easier to prioritize and align cleanup efforts with broader engineering and organizational goals.
Not all debt is created equal. Focus on the parts of your system that are front-facing, frequently changed, or that directly affect critical business workflows. Debt that slows down developers during every sprint is a more pressing concern than debt that’s just inconvenient. Try scoring each issue by its business impact, developer friction, and potential risk to help you decide what to fix now and what to defer.
As you begin work to correct the technical debt, try to make it methodical and build it into your existing organizational processes, documenting as you go. While this may be a months or even years-long project, it’s important that debt reduction becomes a “lifestyle change.”
Of course, you want to be sure that your efforts are working. You might try tracking the mean time to change (or how long it takes to modify existing code), onboarding time for new developers, and the number of existing bug fixes. Seeing improvement in these metrics is usually a strong signal that you’re moving in the right direction and preventing more technical debt from accumulating.
If you’re starting this journey from scratch, don’t try to tackle everything at once or expect it to resolve within a matter of weeks. Pick one or two pain points that have the most impact since fixing tangible issues can help build momentum and make it easier to get buy-in for broader efforts. You should also consider tools that support automated quality checks and visibility. Code scanning tools can help identify patterns and regressions, while CI/CD platforms can automate testing and deployment processes. For teams working with APIs or microservices, API gateways (like Kong Gateway) can play a big role in reducing operational and architectural debt. Centralizing API management, auto-enforcing security policies, and providing consistent observability can eliminate a lot of custom, one-off integrations that tend to accumulate debt over time. Finally, lean into a culture of quality, transparency, and accountability. Debt reduction helps empower engineers to take ownership of the systems they build and maintain while improving an organization’s delivery and resiliency.
Technical debt is a natural byproduct of scaling for any organization, and it’s nothing to be ashamed of as you’re learning and growing. But when left unmanaged, it starts to act as an anchor that can seriously hold you down. With the help of agile practices, a clear plan, and the right tools, you can both reduce your technical debt and change your organization’s culture around shipping quality software — turning a known liability into an opportunity for growth and improvement.
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