How to Audit AI-Generated Code: Architecture, Risks & Technical Debt
AI-generated software is changing not only how applications are built, but also how their quality should be assessed. Some of the signals that auditors and technical leaders relied on for years are becoming less reliable.
TL;DR
Preface
AI is now part of almost every codebase
What is common to all AI-generated codebases?
How AI can help with the architecture
What to review under the layer of commoditized code aesthetics
The difference between pure vibe coding and AI-assisted development
Using AI for code audit
When should an AI-generated project be audited?
An interesting conclusion
FAQs
TL;DR
- Code generated by AI often looks clean, well-structured, and thoroughly documented regardless of who created it.
- Traditional code review signals such as formatting, naming, and documentation are becoming less reliable indicators of software quality.
- The real risks are usually hidden in architecture, data models, module boundaries, and other fundamental design decisions.
- Auditing AI-generated code requires looking beyond code aesthetics and evaluating how the system will scale, evolve, and be maintained over time.
Preface
The custom software world changes very quickly.
Popular languages, frameworks, approaches towards architecture and code decomposition, technologies, infrastructure, etc. – these all come and go.
I have seen several “big” ideas that conquered the market and then disappeared. At their prime, these ideas were so powerful that every second legacy project would contain their implementation to a certain degree.
Do you remember microservices? We still have them in sight, of course, but there was a point in time when they were treated as the future of software development.
Do you remember other big words like IoT (Internet of things)? Blockchain? These were also big at some point. Startups were influenced by these technologies. Entrepreneurs’ minds were filled with these technologies.
Today, we have AI. AI is the biggest thing in the software industry at the moment. It makes an impact on every aspect of software development.
AI is now part of almost every codebase
We are not going to discuss whether AI is the actual future of custom software development or whether it will pass just like its other big predecessors. I would like to focus on a specific aspect of its influence we can feel right now.
The subject of this article is how usage of AI Agents impacted the code in general.
At Krononsoft, we have been performing software code audits since 2014. Over the years, we have reviewed around a hundred projects and learned certain patterns that allowed us to determine the most interesting spots within minutes. We knew styles inherent to development teams in different countries.
We could easily understand how the project evolved, what development patterns and pipelines were followed, and what consequences they would have.
We have now entered a new era of software development in which AI plays a significant role. And we found that our traditional patterns stopped being indicative.
More and more people are using AI Agents in software development. Good engineers use AI Agents. Bad engineers use AI Agents. Many non-technical people who are not engineers at all also use AI Agents.
AI-generated code has found its way into almost every codebase.
Industry signal
AI-generated code is rapidly becoming mainstream. In April 2026, Google reported that 75% of the company’s new code is generated by AI and then reviewed by engineers, up from 25% in 2024.
Google isn’t the only tech giant shifting to AI coding. Microsoft, Meta, and Snap are pushing hard in this direction, too. This trend suggests that AI-assisted development is no longer an experiment but an increasingly common way of building software.
What difference does it make to a software project audit?
What is common to all AI-generated codebases?
Below are a few general observations valid to almost every project developed with the help of LLMs.
AI-generated code is larger than manual work
Every project is significantly bigger today than it was a couple of years ago. It is safe to say that it is carelessly bigger.
AI Agents can easily produce 5 lines of code or 500 lines. Most people who use AI Agents do not pay attention to the amount of code produced because they are focused on the main advantage of AI, which is commonly understood as speed of delivery.
In the last two months, we received 5 projects for a review. AI assisted projects were significantly bigger. One of them was a 3-months old project that had around 500K lines of code and over 200 tables in the database – a scary size that raises questions about ways to support it.
What this matters for your business:
For the owner of such a project, this means higher maintenance costs, longer onboarding times for new developers, and a growing dependence on the people who originally built it.
AI-generated code contain excessive documentation
In the agile development world, we value a functioning and readable code over documentation. Documentation appears only where it is needed and describes what is needed for a real human for real purposes.
However, we have always had customers who required everything to be covered by documentation.
Today, their dream has come true. AI Agents are happy to write documentation and store it in the repository at any time.
However, the size of the content is often too big for a human, so people may just skip reading it.
What this matters for your business:
Documentation that nobody reads provides little business value, even if thousands of pages exist in the repository.
AI-generated code looks better than it is
In the past, poor developers had that sloppy style that usually shows itself in small aspects like incorrect alignment, extra spaces and line breaks, inaccurate naming, etc.
Today, the style is much cleaner even when the project comes from low-profile software developers.
LLMs do not make mistakes of this kind. AI-generated code appears clean and polished.
Finding these little inaccuracies in the code is no longer enough to make conclusions about the developer’s quality. The problems are not on the surface.
But they are there. We just need to look deeper – into the architecture.
What this matters for your business:
Architectural problems with AI-generated code rarely appear during a demo. They usually surface later as delays, increasing development costs, and difficulty implementing new features.
AI does not design architecture automatically
Software architecture is a foundation on which all features are built. It includes:
- The data model: tables and relations
- Classes and methods
- Service classes and modules that contain the core business logic
- Background jobs
- Client-server decomposition of responsibilities
In other words, software architecture is a set of “big” choices that define how easy it will be to develop features, automate routines, and scale the project up.
It is important to know that LLMs do not design architecture automatically and they are not aware of future challenges. They only respond to user requests.
If the user skips the part where he creates a clean architecture and assumes that AI will do it naturally, this will not work.
If, however, the user knows how to define a problem or wants to build an architecture with AI, AI can help.
A 2026 systematic review of empirical studies on AI-generated code found that outcomes vary significantly depending on developer expertise, task specification, and prompt quality, with both quality improvements and quality risks reported across maintainability, correctness, and security.
How AI can help with the architecture
AI can produce decent code when the input contains a well-formed question. A well-formed question is the one that contains 50% of the answer.
Without this guidance, AI cannot provide a thought-through architecture. It just goes from task to task and collects technical debt.
What this matters for your business:
The business consequence is that every future change becomes more expensive than the previous one.
Therefore, a code audit of an AI-generated project requires the auditor to dive deeper, strip away the flakes, and check the “big decisions” made about the code decomposition, database structure, background jobs, and responsibilities of the client and the server.
This is one of the new practices in code audit that work after the old practices stopped working.
To go deeper and find substantial decisions under a misleadingly clean style.
This is where a human can be found. This is where conclusions can be drawn.
What to review under the layer of commoditized code aesthetics
Under the visible clean surface of LLM-generated code, there are architectural solutions that can be evaluated.
Here are several examples of reviewable aspects.
- Multi-tenancy. If a project is multi-tenant, one can check what approach towards multi-tenancy is taken. How many databases are there – one or multiple? What data model is implemented? Do all queries ensure data isolation?
- White labeling. How is it done? How does the system support multiple domain records? How does it isolate the domain-specific data?
- Internationalization. Where are the translations stored – in the code or in the database? How is translation performed? What happens if a translation is not found?
- API. How is it structured? Is there a map of routes or a large sheet of “if” statements? How easy is it to find a specific endpoint and the related module? Is the logic stored in service classes or is tightly coupled with the API? Is the API covered by automated tests?
- Reporting. Where is it placed? How is it arranged? Is there a hierarchy of reports implementing a common interface or just a set of random database queries?
- Other unique features. If the customer defines the key features of the project, how are they organized? Is every feature a separate module that can be potentially enabled/disabled via a feature flag?
Apart from that, it is also reasonable to look into the most complex modules and understand:
- If the code forms a linear structure or is a “nested doll”,
- How many logical conditions are used and how complex they are,
- How parameters are passed from function to function,
- How concise the core functions are,
- How easy the code is to scale and maintain.
This checklist is the best way to look through the illusion of software quality and find the leading force behind the code.
The difference between pure vibe coding and AI-assisted development
I have marked three types of AI-assisted projects to myself.
- Vibe-coded projects created by non-technical people
- Projects developed by weak developers who used AI Agents
- Projects developed by qualified technical people who used AI Agents
This list is a bit random. There are projects that fall out of this list of course. For example, I have seen a project developed by a non-technical person who still tried to control the outcome at every step – and this one was different.
Pure vibe-coded projects
Pure vibe-coded projects look like a painful learning curve. You can find it in the code and in the documentation.
Technical solutions are not present: views are mixed with raw SQL queries, backend functions return plain html, etc.
Files and directories are not structured.
There are long sheets of code that are however iced with proper indentation, naming, comments that make the whole thing look intentional.
We often receive requests from customers whose projects behave unpredictably or stop functioning after seemingly minor changes. They ask AI Agents to add a new feature – and part of the existing features disappear.
They try to demo their software, and it suddenly breaks where it never broke before.
This is lack of control.
The operator communicates with an AI Agent, but does not review what happens after the Agent makes the requested change.
As a result, the code looks intentional, but the software has a random behavior.
If the operator is non-technical but still tries to somehow control the output, there is a bigger chance for a better codebase.
What this matters for your business:
This is particularly dangerous for startups preparing investor demos or customer presentations where reliability matters more than development speed.
Weak developers plus AI Agents
This option is interesting. Weak developers make weak solutions with AI and without it.
But AI makes their weak solutions polished.
There is the same icing: proper indentation, naming, comments, code that looks intentional.
But they still make poor choices in building the data model, decomposing the flow into client and server parts, using background jobs, etc.
If this happens, AI Agents can multiply the problem by allowing them to produce more code.
The more code there is, the harder it is to fix.
What this matters for your business:
At some point, teams spend more time maintaining the system than improving it.
Strong developers and AI Agents
This is the most boring project type. When a project is developed by a strong team, you will not be able to identify whether they used AI Agents or not.
They will make AI Agents work for them and do what they want.
They will keep the code concise and properly structured, so you will not see long sheets with comments.
Most likely, they will delete all AI-generated comments as unnecessary and cover the solution by automated tests instead. By the way, Agents can write very nice tests if they are asked to.
You can only find some leftovers like pieces of generated documentation.
Using AI for code audit
AI Agents can help not only to develop a code but also to assess its quality.
Once again, you should ask the Agent a well-formed question that contains 50% of the answer.
Possible options are:
- Please explain the structure of this project and explain the stack of technologies.
- Identify which modules are used in the main application and which are redundant.
- Please check possible security issues on the backend.
- This project is developed in Claude AI. Is it normal for Claude to generate this type of output or is it the customer’s choice?
- Please describe a process of how a human can take over this code.
- What is a possible way to write tests for this module?
These are the actual questions I used to ask AI and I got meaningful answers.
But it is worth remembering that AI is designed to be positive and agreeable. It will try to cheer you up and rationalize the solutions.
I haven’t seen an AI cursing another AI yet. It will keep politely explaining the solution even when we know it is a throwaway.
It will only suggest something like, ‘Do you want me to prepare a patch that will fix the security issue?’
There is only one scenario in which AI calls the code a garbage – if it realizes that this is what you want to hear.
But you cannot rely on this conclusion because this is not a real opinion.
Ask AI about facts and make conclusions yourself.
When should an AI-generated project be audited?
There are several situations in which an audit of an AI-assisted project becomes particularly useful.
One is when the project reaches the point where new features become harder to implement than expected. This often indicates that technical debt has accumulated faster than the visible functionality.
Another situation is when the founders plan to hand the project over to a development team. Many AI-built projects are initially created by a single person who understands every decision made along the way. The transition becomes more difficult if the architecture, responsibilities, and module boundaries are unclear.
An audit is also useful before scaling a product that has already found its first users. At the MVP stage, many architectural decisions remain hidden because the load is low and the number of workflows is limited. As the project grows, these decisions start influencing performance, maintainability, and development speed.
The same applies when a company prepares for investment, acquisition, or a major technical transition. Understanding how the system is structured and what risks are hidden beneath the surface helps make informed decisions.
The interesting part is that many AI-generated projects look healthy from the outside. They have documentation, comments, tests, and a polished user interface. The challenge is that these visible signals do not necessarily reveal the quality of the underlying architectural decisions.
This is why auditing AI-generated code often requires looking beyond the code itself and focusing on the choices that determine how the project will evolve over time.
An interesting conclusion
There are two practical suggestions for code reviewers that come from the analysis above.
- Traditional audit heuristics are losing predictive power. The coding style no longer reveals quality. Reviewing AI-generated code requires going deeper into the architecture and other fundamental parts.
- We should keep educating customers about what a good code actually is.
Why is this education important?
I have been following on how AI changed projects and came to an interesting conclusion.
AI fulfills people’s dreams. You see, customers always wanted:
- Big and complex projects built in a short time.
- The ability to have their strangest requirements implemented without thinking about technical details.
- A detailed and comprehensive documentation.
- To watch how a cool UI appears in front of them.
They also wanted software development to be cheaper. But I am not aware if they spend less on AI services than they used to spend on custom software development in the past.
AI has made this dream come true!
However, the results of AI-assisted work is not always satisfactory and very often is completely discouraging.
Why?
Probably, because the dream itself was not the right one. Customers do not dream about conciseness of the code, or about its scalability, or performance – they assume it will all come over naturally.
It will not.
Unless… you start dreaming about it.
If your MVP or live product was built fully or partially with AI tools, an independent code audit can help identify architectural issues, technical debt, scalability risks, and security concerns before they become expensive problems.
FAQs about auditing AI-generated code
My AI-built MVP works. Why should I worry about code quality?
Many architectural problems in AI-generated code only become visible as a project grows.
An MVP may perform well with a small number of users and workflows. However, once new features, integrations, customers, and developers are added, weaknesses in the architecture begin to affect development speed, reliability, and maintainability.
The goal of a code audit is not to determine whether the software works today, but whether it can continue evolving efficiently tomorrow.
Is AI-generated code always worse than human-written code?
No.
The quality of AI-generated code depends heavily on the person directing the AI and the engineering decisions made throughout the project.
Strong developers often use AI to increase productivity while maintaining architectural discipline and code quality. Problems usually arise when AI is used without sufficient technical oversight or when architectural decisions are made implicitly rather than intentionally.
What are the biggest risks in AI-generated software?
The most common risks of AI-generated code are not related to code formatting or naming conventions.
They usually involve:
- Poor database design.
- Excessive technical debt.
- Weak module boundaries.
- Missing automated tests.
- Incorrect responsibility distribution between frontend and backend.
- Security issues.
- Scalability limitations.
These problems often remain hidden behind clean-looking code.
Can a non-technical founder successfully build software with AI?
Yes, but maintaining control over the outcome becomes increasingly important as the project grows.
Many successful founders use AI tools to validate ideas, build prototypes, and create MVPs. The challenge comes later, when the software needs to be maintained, extended, audited, or transferred to a professional development team.
The earlier architectural issues are identified, the easier and less expensive they are to address.
How do I know whether my AI-generated project can be handed over to another development team?
This is one of the most common concerns we see in AI-assisted projects.
A healthy project should allow new developers to understand:
- How the system is structured.
- Where the business logic is located.
- How modules interact.
- How deployments work.
- How tests are executed.
If transferring knowledge depends primarily on the person who originally built the system, the project may have significant maintainability risks.
What should be reviewed during an audit of AI-generated code?
A meaningful audit goes beyond coding style and documentation.
Typical review areas include:
- Software architecture.
- Database structure.
- Scalability.
- Security.
- Automated test coverage.
- Technical debt.
- Deployment processes.
- Third-party dependencies.
- Maintainability and onboarding complexity.
The objective is to understand whether the project can support future development without accumulating disproportionate costs and risks.
Is it worth auditing software built with tools like Cursor, Claude, Lovable, Bolt, Windsurf, or ChatGPT?
Yes, especially if the project is already being used by customers or is expected to continue growing.
The specific tool matters less than the engineering decisions made during development. Different AI tools may generate different code styles, but the long-term success of the project is usually determined by architecture, maintainability, security, and scalability rather than by the AI tool itself.
Should founders or investors request a code audit before fundraising, acquisition, or due diligence?
In many cases, yes.
AI-generated software can create the impression of rapid progress. A product may have a polished interface, extensive documentation, and a large codebase developed in a relatively short period of time. However, these visible signals do not always reflect the quality of the underlying architecture.
For founders, an independent code audit can help identify technical risks before they become obstacles during fundraising or scaling.
For investors and acquirers, a code audit provides a more objective view of the software asset they are evaluating. It can reveal architectural weaknesses, technical debt, scalability limitations, security concerns, and maintainability issues that may not be visible during a product demonstration.
The goal is not to determine whether the software works today, but to understand how sustainable, transferable, and scalable it will be as the business grows.
Vadim Kondratiev, team lead and customer liaison at Krononsoft, specializes in guiding complex software initiatives and aligning technology with business growth.
