AI technical debt keeps growing

Why AI technical debt matters now

The role of AI in development is changing rapidly. Coding assistants are increasingly supplemented or replaced by agents that work through several steps independently: analysing an issue, changing multiple files, running tests and preparing a pull request.

Sonar’s January 2026 survey of more than 1,100 developers found that AI assisted 42% of all committed code. Yet only 48% of developers always checked AI-assisted output before committing it.

How do you ensure an agent follows the same standards as your development team? What context does it need to make good choices? And how do you check growing volumes of generated code without making review a bottleneck?

In October 2026, Atlassian reported that 94% of engineering leaders surveyed were already using AI, while only 6% had systems in place to scale and govern it across the software development lifecycle. Context is crucial: an agent needs information about architecture, standards and earlier technical decisions to make sound choices independently within an existing codebase.

Architecture and context for AI coding agents

The latest coding agents understand a codebase much better than earlier generations of AI coding tools. They are increasingly able to account for existing patterns and structures.

The available context has a major influence on the result. Architectural knowledge is often scattered across documentation, tickets, pull requests, Slack conversations and developers’ own experience. An agent with repository access alone misses some of that information.

This matters more as agents gain autonomy. A poor choice in one function is usually straightforward to fix. A flawed structural pattern repeated dozens of times can create substantially more work later.

Gartner explicitly addresses architectural technical debt from AI coding agents. When technical decisions are left ungoverned, extensive use of coding agents can contribute directly to architectural debt.

Perhaps counterintuitively, AI makes architecture more relevant again. Architectural Decision Records, coding standards, security policies and clear domain boundaries gain another purpose. They help developers understand a system and give coding agents direction.

From code review to verification debt

Another emerging term is AI code verification debt. Sonar uses it to describe the gap between the amount of AI code produced and the amount that can actually be checked carefully.

That gap grows as agents make larger changes. A pull request containing hundreds of generated lines still requires an understanding of the consequences for the product, architecture and security.

Code review is changing alongside AI coding. Human review remains essential, while automated quality controls play a greater role. Static analysis, automated tests, security checks and quality gates can carry out part of the verification continuously.

The focus of reviews is shifting too. As code is produced faster, it becomes more important to check that a change fits the product’s structure and principles. A technically correct solution can still introduce unnecessary complexity or implement existing functionality in an unnecessarily different way.

Technical debt in the AI layer itself

AI technical debt also arises in products where AI is part of the functionality itself. An additional technical layer increasingly develops around models, prompts, context, retrieval, evaluations and model providers.

This layer usually evolves quickly. An initial AI feature might start with one model and a handful of prompts. Later, more use cases, fallback mechanisms, data sources and models follow. Without a clear structure, it becomes harder to understand why a particular choice was made.

A prompt can remain in production code for years while it becomes unclear which version belongs to which feature. Switching models can affect existing output unexpectedly. A RAG solution — retrieval-augmented generation, which improves a language model’s answers by retrieving information from your own sources or business data — may rely on data whose relevance or quality has changed.

Evaluation matters too. Much of traditional software can be checked against predefined test cases. The quality of AI output often depends on several factors at once. Teams therefore need evals, test datasets and clear quality criteria to judge whether a change actually improves the result.

Without that knowledge, teams become dependent on components that are increasingly difficult to replace or adapt. That, however quietly it develops, is also a form of AI technical debt.

How to prevent AI technical debt from accumulating

Preventing AI technical debt starts with the engineering principles that apply to all software development. How teams apply them changes as agents produce more of the code.

• Make architecture explicit. Document important technical decisions and system boundaries in a way both developers and agents can use.
• Apply consistent quality gates. Run generated code through the same automated checks as code written entirely by hand.
• Manage context as part of the development environment. Define what an agent needs to know about the product, architecture and standards.
• Version-control AI components. Record which models, prompts, data sources and configurations belong to each feature.
• Build evaluation into AI functionality. Use datasets and criteria that let you measure changes in output.
• Keep maintaining code and architecture. Refactoring and removing outdated patterns remain part of product development.

The implementation naturally differs by product. A relatively small SaaS application has different requirements from a business-critical platform with many integrations, users and sensitive data. As software grows more complex, technical context and governance around AI coding become more important.