Technical Debt in AI-Built Software: A Plain Guide

Technical debt is the hidden cost of shortcuts in software. The product works today, but each later change takes longer and breaks more. AI coding agents can build up debt quickly because they add code so fast. It pays to know what debt looks like and how to keep it small, even if you never read a line of code.

What is technical debt, in plain English?

Think of a kitchen in a busy restaurant. If every cook puts tools back anywhere, service still runs on day one. By month six, nobody can find anything, two cooks make the same sauce twice, and a simple new dish takes a whole afternoon to set up.

Software works the same way. "Debt" is the name for the mess that builds up when work is done quickly and not tidied. You borrow speed now and pay interest later, in these forms:

• New features take longer than they did at the start.
• A change in one place breaks something in another.
• Fixing one bug brings back an old one.
• Only the original builder, human or AI, seems to know how things fit together.

Debt is not always bad. A founder who ships a rough first version to test an idea is making a sensible loan. The trouble starts when nobody knows the loan exists.

Why can AI-built software pile up debt faster?

A coding agent is very good at adding things. Ask for a new screen and you get one, often in minutes. What it may not do on its own is stop and ask whether a similar screen already exists that it could reuse. It builds what you asked for, in the way that is easiest from where it stands.

Anthropic's guidance for Claude Code makes a related point. Some knowledge about a project cannot be seen from the code alone, and the guidance names examples: a deploy process that lives in someone's head, and a check that has to pass before anything counts as done. The agent cannot follow a rule it has never been shown. A rule such as "we always do payments this way" stays invisible until you write it down.

What does the research say?

Be careful with numbers here, and know what they do and do not show. LeadDev, in an article from February 2025, reported on research by GitClear, a company that analyses code. The research looked at 211 million changed lines of code from 2020 to 2024. It found that in 2024:

• Blocks of five or more duplicated lines rose eightfold in frequency.
• Copy-pasted lines outnumbered moved lines. Moving code is a sign that people are reusing and tidying, and GitClear treats it as a stand-in for reuse. That kind of movement declined from the year before.

The same article cites Google's 2024 DORA report. It linked a 25% rise in AI use with faster code reviews and better documentation, and also with a 7.2% drop in delivery stability. (DORA is Google's yearly research on how well software teams deliver.)

These are trends across many projects in a period when AI tools spread. They do not prove that any one project, or any one tool, produced the duplication. Treat them as a reason to check your own project, not as a verdict.

How do I spot technical debt without reading code?

You can see the effects even if you cannot see the cause. Watch for these signs:

1. Slowing delivery. The first ten features took a day each. The next ten take a week each, and nothing about them is harder.
2. Surprise breakage. A change to the invoice screen breaks the sign-up screen.
3. Repeat bugs. The same kind of fault keeps returning in different places. That can mean the same logic was copied in several places and only some copies were fixed.
4. Rising cost for similar work. If a similar task takes noticeably more time or money than it used to, ask why.
5. Nervous builders. If the agent or the team starts calling a change "risky" for no clear reason, ask what makes it so.

Keep a simple note each month: how long did a typical small change take, and how many things broke afterwards? A trend is worth more than any single reading.

How do I keep debt low when AI builds the software?

You do not need to be technical. You need to ask for the right things.

Give the agent a check it must pass

Anthropic's guidance lists "a check that has to pass before anything counts as done" among the things you set up yourself. Automated tests are the usual form: small programs that try the product and fail if something no longer works. When a change breaks something else, the test fails straight away, instead of weeks later when a customer finds it. Without checks, debt hides.

Ask for tidy-up work on purpose

Put clean-up on the plan as its own task. After a run of new features, ask for a task that finds repeated logic, merges it into one place, and removes parts nobody uses. Say that the behaviour must not change and that the existing checks must still pass. If you never ask, it rarely happens by itself.

Write down your rules

If there is a right way to do things in your product, such as how prices are rounded or how customer data is handled, write it into the instructions the agent is given. This is how a rule in your head becomes a rule the agent can follow.

Keep each request small and clear

Large, vague requests tend to produce large, improvised solutions. A clear request with a defined end point leaves the agent less room to invent a new way of doing something your product already does.

Have someone look at the whole, not just the change

The LeadDev article says an AI tool has a limited context window, meaning it can only hold so much of the project in view at once. Because of that, it says humans need to review the broader codebase, and it suggests people refactor repeated logic into reusable parts. In practice, that means a periodic review of the whole project by a person, or by a separate reviewer given the whole project. Reviewing one change at a time will not catch debt that builds up across many changes.

Is technical debt always a problem?

No. A prototype you will throw away in a month does not need to be tidy. A product you plan to run and extend for years does. The question to ask is how long the code will live. Tell your team or agent which one you are building, so shortcuts are chosen on purpose.

What does it cost to ignore it?

Debt costs show up as time and money. Features take longer, so each one costs more, whether that is engineer hours or use of your AI plan. Bugs reach customers more often. In the worst case, a rewrite becomes cheaper than patching, and that is a decision you want to make early, not under pressure.

How Atlas fits

Atlas is a team of AI engineers built on Claude Code. Today it keeps a catalogue of build standards for code structure, quality and security, each with its own check of the project's files, and it reviews every build as it finishes. Before a production release, everything going out is reviewed together. That catches some debt early.

Atlas does not yet show how duplication or tidy-up work trends across a whole project. That view is coming to Atlas, built in, so it needs no setup on your side. Until then, add a clean-up task to your plan after each run of features, and ask for the whole project to be reviewed now and then. You can read more on the Atlas home page.

Conclusion

Technical debt is a cost you can manage, not a mystery. AI agents add code fast, so the tidying has to be planned and not left to chance. Give the agent checks to pass, schedule clean-up, write down your rules, and watch how long small changes take over time. If that time keeps growing, you are paying interest, and it is time to pay some of the loan back.