Devin Built Your Feature. Your Team Can't Maintain It.

You pointed Devin at a feature request and it shipped code. Fast. Genuinely impressive. You watched it plan, write files, run tests, and open a PR — all without human intervention. The future of engineering, right there in your terminal.

Then your team opened the pull request.

“What is this?”

“Why did it do it this way?”

“I have no idea how to modify this.”

The autonomous AI engineer shipped code that works. It passes tests. The feature does what the ticket said. But no human engineer on your team wants to touch it. Nobody understands the decisions it made. Nobody recognizes the patterns it chose. Nobody can confidently change it without worrying about breaking something they don’t understand.

Now you have a feature that works today but can’t be maintained tomorrow. It can’t be extended. It can’t be debugged quickly when something goes wrong in production at 2 AM. You traded development speed for maintenance paralysis.

This isn’t a hypothetical. We’re seeing this pattern more and more. Founders use Devin to move fast, and they do move fast — until their human engineers need to work with the code Devin wrote. That’s when speed becomes a liability.

The good news: the code isn’t trash. It’s just foreign. And foreign code can be made familiar. But you need to understand the problem before you fix it.

Why Devin’s code is hard to maintain

Let’s be clear about something: Devin is genuinely impressive technology. It’s an autonomous AI software engineer that can plan multi-step tasks, write code across multiple files, run its own tests, debug failures, and deploy working features. It’s not a glorified autocomplete. It actually reasons about problems.

But autonomous means something specific. It means Devin makes decisions without asking you. It chooses architectures. It picks design patterns. It decides how to structure abstractions, where to put boundaries, and how to handle edge cases. All on its own.

And that’s the problem.

Your team has conventions. They have patterns they’ve agreed on — explicitly or implicitly. They have a shared understanding of how the codebase works, why certain decisions were made, and what the boundaries are. When a human engineer writes code, they’re working within that shared context. When they make an unusual choice, they explain it in the PR description or a code comment. When they’re unsure, they ask.

Devin doesn’t ask. It doesn’t know your team’s conventions because it learned from millions of repositories, not yours specifically. It doesn’t explain its unusual choices because it doesn’t know they’re unusual in your context. It builds what works, not what fits.

The result is code that’s technically correct but culturally foreign. It’s like hiring a contractor who speaks a different dialect of the same language. The words are right, but the idioms are off. The meaning gets lost. And in a codebase, lost meaning is where bugs hide.

This isn’t unique to Devin — we see similar patterns with other AI agent tools and AI coding assistants. But Devin’s full autonomy makes the problem more pronounced. The more decisions an AI makes independently, the more places your team’s understanding can break down.

Five problems with Devin-generated code

We’ve reviewed codebases where Devin contributed significant features. The same five problems show up consistently.

1. Opaque decision chains

To get from a feature request to working code, Devin makes dozens — sometimes hundreds — of small decisions. Which file to create. Which pattern to use. How to name things. Where to put the boundary between modules. How to handle errors. Each decision is reasonable in isolation. But none of them are documented.

Your team sees the final output. They don’t see the reasoning chain. When they ask “why is it done this way?”, there’s no answer. The commit messages say what changed, not why. The code has no comments explaining tradeoffs. The decision trail is gone.

2. Unconventional patterns

Devin draws from its training data — millions of open-source repositories with millions of different patterns. It might use a repository pattern when your team uses service objects. It might use an event-driven approach when your codebase is request-response. It might structure modules in a way that’s perfectly valid but completely inconsistent with everything else in your project.

One unconventional pattern is fine. Your team reads it, learns from it, moves on. But when an entire feature uses unfamiliar patterns throughout, every file becomes a puzzle. Cognitive load multiplies.

3. Incomplete context

Devin understands your code structure. It can read files, follow imports, understand types. But it doesn’t understand your business domain the way your team does. It doesn’t know that the billing module has weird edge cases because of a legacy migration. It doesn’t know that the notifications system is about to be rewritten. It doesn’t know that the product team decided against a certain approach last quarter.

So it builds solutions that are technically sound but contextually naive. It might create tight coupling to a system you’re about to deprecate. It might duplicate logic that exists elsewhere under a different name. It might solve the wrong problem elegantly.

4. Over-engineering

Devin tends to build for generality. It creates abstractions, configuration layers, and extension points that would make sense if the feature needed to handle a dozen use cases. But you needed it to handle one.

A human engineer on your team would’ve built the simple version first. They know the product roadmap. They know what’s likely to change and what isn’t. Devin doesn’t have that context, so it hedges. The result is more code, more indirection, and more surface area for bugs — all in service of flexibility you didn’t ask for.

5. Testing gaps

Devin writes tests. They pass. On paper, coverage looks good. But Devin tests what it built in isolation. It writes unit tests for the functions it created. It might write a basic integration test for the feature itself.

What it misses are the integration points with existing code. The boundary where Devin’s new feature meets your existing systems — that’s where the real bugs live. The tests don’t cover what happens when the new payment processing feature interacts with your existing webhook handler. They don’t cover the edge case where the new notification system conflicts with the existing rate limiter. These are the tests your team would have written instinctively, because they know where the bodies are buried.

What autonomous AI code looks like in practice

Here’s what we’ve seen firsthand. A startup used Devin for three features over two months. All three shipped. All three worked. The founder was thrilled — they’d moved faster than they thought possible.

Then reality set in.

A developer needed to modify the first feature to handle a new edge case. They spent five days understanding the architecture Devin had chosen before they could make a one-day change. The feature used a state machine pattern that was technically elegant but completely unlike anything else in the codebase. Nobody on the team had worked with that pattern before.

The second feature used a dependency injection approach that the team found in a .NET tutorial during their research. The codebase was Python. The pattern wasn’t wrong, but it was deeply unfamiliar. Every time someone needed to trace a function call, they’d get lost in the indirection.

The third feature had 94% test coverage. Impressive. But every test was a unit test for individual functions. When a bug appeared at the integration boundary between the new feature and the existing API layer, none of the tests caught it. The team spent three days tracking down a bug that proper integration tests would have caught immediately.

Three features. All working. All creating drag on every engineer who touched them afterward. The speed Devin provided upfront was being paid back with interest on every subsequent sprint.

The founder’s math was simple. Devin saved two weeks of development time across the three features. But in the four months since, the team had spent over six weeks of cumulative engineering time navigating, understanding, and working around the code Devin produced. The ROI had flipped negative. And it was getting worse, not better, because every new feature the team built had to interact with the parts Devin had written.

This isn’t an argument against using Devin. It’s an argument for treating Devin’s output as a draft, not a finished product. Drafts need editing. AI-generated code needs refactoring.

Your options

You’ve got Devin-generated code in your codebase and your team is struggling with it. Here’s what you can do.

Option 1: Accept and adapt. Your team learns Devin’s patterns. They study the code until they understand it. This works, but it’s slow and frustrating. You’re asking humans to adapt to a machine’s preferences instead of the other way around. It also means your codebase now has two sets of conventions — your team’s and Devin’s. That inconsistency compounds over time.

Option 2: Rewrite from scratch. Throw away what Devin built and have your team rebuild it their way. Clean, consistent, understood. But expensive. You’re paying twice for the same feature. And if the feature works fine, rewriting feels wasteful. Most startups can’t afford the time or the morale hit of telling engineers to rebuild something that already works.

Option 3: Targeted refactoring. This is usually the right answer. Don’t rewrite everything. Don’t accept everything. Instead, identify the specific points where Devin’s code diverges from your team’s conventions and refactor those points. Align the patterns. Add the missing documentation. Fill the testing gaps. Keep what works, fix what doesn’t fit.

The goal isn’t to eliminate AI-generated code. It’s to make AI-generated code your team’s code. Code they understand, can maintain, and can extend with confidence.

How we make AI-agent code maintainable

We’ve developed a process specifically for cleaning up code generated by autonomous AI agents like Devin. Here’s how it works.

Pattern audit. We review the AI-generated code alongside your existing codebase. We identify every place where Devin’s patterns diverge from your team’s conventions. Not every divergence is a problem — sometimes Devin chose a better pattern. But we document all of them so decisions can be made deliberately, not by default.

Convention alignment. For the patterns that need to change, we refactor the code to match your team’s standards. Same naming conventions. Same file structure. Same architectural patterns. Same error handling approach. The logic stays the same. The implementation becomes familiar. Your engineers open a file and it looks like code they could have written themselves.

Decision documentation. For the decisions Devin made that are worth keeping, we add the documentation Devin didn’t. Why this pattern? What’s the tradeoff? What should a future developer know before modifying this? We turn opaque decision chains into explicit, documented choices.

Integration testing. We write the tests Devin missed — the ones at the boundaries between new and existing code. The integration tests. The edge cases that only matter in the context of your specific system. This is where bugs actually live, and this is where technical debt cleanup has the highest ROI.

Knowledge transfer. We walk your team through every significant change. Not a handoff document that nobody reads — actual pairing sessions where your engineers ask questions and build understanding. When we’re done, your team owns the code completely.

The result: code that works exactly like it did before, but that your team can read, maintain, debug, and extend. No more “what is this?” moments. No more week-long understanding sessions before a one-day change.

If you’re curious about getting better results from Devin upfront, check out our guide on Devin AI best practices to reduce these problems before they start.

Make it your team’s code

Devin is a powerful tool. But tools produce raw material, not finished products. The code Devin generates needs to be shaped, refined, and integrated into your team’s way of working.

If your team is spending more time understanding AI-generated code than writing new features, that’s a problem with a clear solution. Don’t throw away the code. Don’t force your team to live with it. Make it yours.

We’ll audit what Devin built, identify what needs to change, and do the refactoring so your team can move forward confidently. Start with a code audit to map what Devin generated, then our vibe code cleanup process restructures it into code your team owns. The feature keeps working. Your team gets code they actually understand. And the next time you use Devin — because you should — you’ll know exactly how to integrate its output into your codebase without losing momentum.

Let’s talk about your codebase →

Devin shipped code your team can’t maintain? Variant Systems helps teams make AI-generated code readable, maintainable, and truly yours.