Will AI create more technical debt than it solves?

Gartner projects that by 2028, AI will create more technical debt than it solves. Engineering teams can read that projection as a forecast for organizations that let generation outpace code verification, and the outcome for any one team still depends on what it does with the speed AI provides. This page explains what technical debt means in the AI era, where AI helps reduce it, how it compounds it, which leading indicators to watch, and how to build a management model that turns AI coding velocity into verified progress.

Why AI coding tools make existing technical debt problems worse

Many teams assume that faster code generation automatically reduces technical debt, because agents can refactor, document, and fix at a pace no team can match by hand. In practice, an AI agent accelerates whatever workflow it enters, including the parts that were already weak. When a team points an agent at a well-governed code remediation process with clear standards and validation, the agent clears debt quickly. When a team points the same agent at a codebase with thin review and unclear ownership, the agent adds unverified change just as quickly.

That is why two organizations can adopt the same tool and see opposite results. Each team gets a multiplier on the quality of its existing process, so the governance and verification around the agent decide whether debt goes up or down.

What is technical debt in the AI era?

Technical debt is the future cost created by expedient choices in code, architecture, process, and documentation that favor short-term speed over long-term maintainability. Teams pay that cost later through slower changes, higher defect rates, and more effort to understand and modify the system. The definition predates AI, but agents change how quickly debt can accumulate.

Technical debt takes two main forms. Code-level debt is the buildup of bugs, vulnerabilities, and code smells in individual files and methods, and it raises defect rates and the daily cognitive load on developers. Architectural debt builds up when developers or agents drift from the intended architecture of a system, through circular dependencies, high coupling, and modularity violations. Architectural debt compounds about 2.8 times faster than code-level debt, and Gartner predicts that by 2027 architectural debt will account for 80% of all technical debt. AI agents add to both forms, because they write quickly without built-in knowledge of a team's architecture or coding standards.

As agents write more of the code, teams also accumulate verification debt. Verification debt is the gap between the quality agents produce by default and the quality enterprise software requires, and it widens when teams generate more change than they can confidently review. An unverified change rarely looks like debt on the day it merges. It turns into debt when its hidden cost surfaces in production, in a later refactor, or in a security incident. Agent pull requests arrive larger and faster than human ones, so reviewers usually feel verification debt first, long before it appears in a debt report.

Why does this question matter now?

Carnegie Mellon researchers studying teams that adopted an AI coding tool found a 3x to 5x velocity spike in the first month that disappeared within three months, while issues rose 30% and code complexity rose 41%. The teams kept the added issues and complexity long after the speed gain faded.

Research from NBER shows a similar pattern at larger scale. Autonomous agents pushed lines of code up 2,450% while releases rose only 30%, which means much of that generated code sits in review, in rework, or in branches that never ship. Developers tell us they feel the strain directly: 88% report at least one negative impact of AI on technical debt, 96% do not fully trust that AI-generated code is functionally correct, and only 39% say AI-assisted code is always checked before commit.

Scale adds to the risk. Agents pass about 91% of tasks on codebases under 10,000 lines and about 15% on codebases over 50,000 lines (RepoMod-Bench, 2026), so a pilot on a small repository can make an agent look far more reliable than it will be on a large enterprise estate.

Where can AI help reduce technical debt?

AI helps reduce debt when teams point it at bounded, well-defined work and validate the results. Several applications deliver real gains:

• Codebase discovery and documentation. Agents read large, unfamiliar systems quickly and produce explanations and documentation that shorten the time developers spend reverse-engineering legacy code.
• Candidate tests for poorly covered areas. Agents draft tests for code that carries risk because no one has tested it, giving teams a starting point for raising coverage.
• Scoped refactoring and repetitive remediation. Agents apply a known fix pattern consistently across many files, which suits high-volume, low-ambiguity work that otherwise drains developer time.
• Legacy understanding and modernization paths. Agents help developers understand older systems and propose modernization approaches that would otherwise take weeks of analysis.

Each of these benefits depends on clear objectives, explicit constraints, and validation that confirms the output is correct before it lands. Without those conditions, an agent produces plausible changes that someone still has to check, and the debt moves to the review queue instead of leaving the codebase.

How can AI compound technical debt?

The same capabilities that reduce debt under governance compound it when governance is missing. These failure modes are where agent-driven development accumulates debt fastest:

• Code that works locally but breaks the larger system. An agent produces a change that solves the requested problem and passes its own tests while violating architectural boundaries, established patterns, or operational requirements it never had context for. A single pull request rarely shows this kind of architectural drift, so it tends to pass review and accumulate quietly.
• Change volume beyond review capacity. Agents generate more and larger pull requests than review, testing, and maintenance processes built for human pace can absorb, so unverified changes slip through.
• Inconsistent implementations across teams. When prompts and context differ from team to team, agents solve the same problem several different ways, and the inconsistency itself becomes debt.
• Plausible output that masks weak understanding. Agent-generated code reads as correct while resting on a shallow grasp of a legacy system, so the flaw stays hidden until it surfaces downstream.
• Brittle patterns and insecure dependencies that spread fast. An agent replicates whatever patterns it sees, so a fragile pattern or risky dependency can propagate across a codebase faster than teams can detect it.

Each failure mode traces back to the same root cause. Teams scale output faster than they scale verification, and they lack a consistent check between generation and merge.

What are the leading indicators to watch?

AI-driven technical debt shows up in engineering signals well before it shows up in outages. These indicators tell teams which direction debt is moving while there is still time to act:

• Rising code-quality findings in recently changed code. A growing issue backlog concentrated in new changes, or a technical debt ratio and remediation effort that keep climbing release over release, means debt is entering faster than teams are clearing it.
• More deviations from the intended architecture. A rising count of disallowed dependencies, circular tangles, and other deviations shows that architectural drift is building up even when individual changes look fine.
• More review revisions, rejected changes, and reopened tickets. Longer review cycles and more rework signal that generated code is missing the bar on the first pass.
• Declining test confidence. Flaky CI, falling coverage on new code, and a higher rollback rate indicate that verification is losing ground.
• Growing policy exceptions and unclear ownership. More exceptions, and uncertainty about who owns generated code, show that governance is eroding.
• A widening gap between generated and accepted changes. When the volume of generated change pulls away from what reaches production, verification debt is building up in between.

Teams that track these signals together can turn an abstract risk into a measurable trend and correct course early.

How do you build an AI-aware technical debt management model?

Many organizations still manage technical debt through periodic audits and retrospective reports, which were designed for a world where people wrote code at human speed. Agents now open pull requests far larger than human ones, so teams need a model that governs debt continuously inside the developer and agent workflow. Five elements hold that model together:

• One standard for human and AI-authored code. Apply the same quality, security, testing, and architecture standards to every change, whoever or whatever wrote it. Lighter rules for generated code give debt an easy path into the codebase.
• Controls early in the workflow. Run code-quality, security, testing, and architecture checks as code is written and before it merges, where issues cost the least to fix. Architects and team leads can define the intended architecture once, so every change, from a developer or an agent, is checked against the same structure.
• Risk-based prioritization. Rank remediation by risk, business criticality, change frequency, and maintenance cost, so teams spend effort where debt does the most damage.
• A visible, owned backlog. Keep a debt backlog with clear owners, due dates, and measured progress, and track metrics such as technical debt ratio and remediation effort over time, so leaders can manage debt as a number instead of a feeling engineers have to justify every planning cycle.
• Feedback into agent guidance. Feed recurring findings back into agent context and engineering standards, so each new round of generated code carries fewer of the same issues.

With this model in place, teams catch debt where it is created, before it reaches production, and keep pace with how fast agents now write.

How do you put AI to work on debt deliberately?

Teams get the most from AI on debt reduction when they scope the work tightly. A few practices make the difference:

• Start with bounded initiatives. A goal like "reduce complexity in the payments module below this threshold" produces results a team can check, while an open-ended "clean up the codebase" prompt produces unscoped change that no one can validate.
• Write task contracts. Define the scope, non-negotiable behavior, required tests, and acceptance criteria before the agent starts, so everyone knows what success looks like.
• Keep changes small and revalidate each one. Keep each remediation change small enough to review, and run verification again after every change instead of accepting a large batch at the end.
• Compare against a baseline. Measure AI-assisted remediation against baseline effort and quality outcomes, so the team knows whether the agent is helping.

When teams scope work this way, AI becomes a reliable way to burn down debt, and its fixes hold up over time.

How do SonarQube and Gitar keep AI from compounding technical debt?

AI on its own neither reduces nor creates technical debt. Governance and verification determine the direction, and SonarQube and Gitar give teams two complementary layers of verification for every change, independent of whichever agent or model wrote the code.

SonarQube provides the deterministic layer. Its analysis covers 7,500+ issue types across reliability, maintainability, and security with an industry-leading low false positive rate, and it returns the same verdict every run, so developers, reviewers, and agents all work from a consistent signal. Quality gates apply configurable pass/fail conditions that stop new debt from entering the main branch, and they hold human-authored and AI-authored changes to the same standard. Technical debt ratio and remediation effort show how much debt exists, where it concentrates, and how it trends over time, so teams can prioritize with data. Architecture management builds a living map of the current architecture on every scan and lets architects and team leads define the intended architecture. When a developer or an agent introduces a deviation, SonarQube surfaces it as a maintainability issue in the developer workflow, and the quality gate fails with code-level guidance on how to fix it before merge. Developers see the same feedback in their IDE through SonarQube for IDE, so they can resolve issues without leaving their tools. Sonar is named a Leader in the 2026 Gartner Magic Quadrant for Technical Debt Management Tools.

Gitar provides the agentic layer at the pull request. Gitar is a contextual AI code review solution that works alongside SonarQube. It reviews every pull request in the context of the surrounding code, reasons about logic and intent, checks each change against the ticket it was meant to deliver, and flags breaking changes across related pull requests and repositories. When CI fails, Gitar diagnoses the failure and applies fixes under rules the team writes in plain English, iterating until the build is green. Gitar catches the context-dependent change risks that a deterministic check is not designed to see, and SonarQube catches the repeatable quality and security issues that an LLM reviewer may flag inconsistently from one run to the next.

For the debt teams already carry, SonarQube Remediation Agent retires backlog issues in the background on a schedule the team sets, and it can also resolve quality gate blockers on a pull request when a developer asks it to. It validates each fix with closed-loop verification against Sonar's analysis engine, so only verified fixes become pull requests for developers to review. One enterprise fintech customer cleared 1,000 hours of technical debt in 45 days with SonarQube Remediation Agent, and Cisco cleared more than 20,000 technical debt issues in three months with Sonar. To close the loop on the feedback element of the model above, Sonar Vortex gives coding agents the project's context and constraints, including its coding standards and current and intended architecture, before they write, so fewer of the same issues reach review in the first place. Across guiding agents, verifying every change, and fixing what verification finds, Sonar governs technical debt throughout the development cycle, and what teams learn from each fix feeds back into the guidance agents receive next time.

Next steps

Start with one agent-assisted workflow. Baseline its debt and verification signals, including findings in new code, review revisions, CI stability, and the gap between generated and accepted changes. Then introduce SonarQube and Gitar as complementary layers, and track whether those signals improve. The goal is AI-generated changes that are faster to produce and safer to keep.

• Start a free SonarQube trial and set a quality gate that every change must pass before merge.
• Try Gitar on your pull requests, standalone or with SonarQube.
• Measure and prevent technical debt across AI-generated code with SonarQube.
• Read What is technical debt? for the fundamentals of debt accumulation and remediation.