Working with Devin AI: How to Stay in Control

Devin can plan, code, and ship autonomously. That’s the pitch, and it’s real. Hand it a task, walk away, come back to a pull request. For founders moving fast, that sounds like a dream.

It’s also risky.

Autonomous means Devin makes decisions you didn’t approve. It picks patterns you don’t recognize. It builds abstractions you didn’t ask for. It optimizes for completing the task, not for fitting into your codebase. And because it works fast, it can create a lot of damage before anyone notices.

None of this means you shouldn’t use Devin. It means you need to constrain its autonomy. The teams that get value from Devin aren’t the ones who hand it the keys and walk away. They’re the ones who treat it like what it is: a powerful tool that needs guardrails.

Here’s how to use Devin without losing control of your codebase.

Devin is a junior engineer with infinite energy

Let’s get the framing right. This isn’t an anti-Devin post. Devin is genuinely capable. It can read documentation, write code, run tests, debug failures, and iterate on solutions. For certain types of work, it’s faster than most human developers.

But capability isn’t the same as judgment.

Devin operates like a junior engineer who never gets tired, never pushes back, and never asks clarifying questions unless prompted. It will do exactly what you ask. The problem is that “what you ask” and “what you mean” are often different things. Experienced engineers fill that gap with context, intuition, and knowledge of the codebase. Devin fills it with reasonable-sounding guesses.

Junior engineers need explicit constraints. They need detailed code reviews. They need someone checking that their locally-correct decisions fit the larger picture. They need guardrails not because they’re bad, but because they’re new.

Same with Devin.

The teams that struggle with Devin are the ones who treat it like a senior engineer. They give it broad tasks, skip thorough reviews, and assume that “it compiled and the tests pass” means the code is good. That’s how you end up with a codebase that works but nobody understands.

The teams that succeed treat Devin like the most productive junior engineer they’ve ever hired. They give it clear boundaries, review everything, and maintain human ownership of architectural decisions. If you’re also working with tools like Claude Code or Manus AI, the same principle applies: the human stays in charge.

Seven rules for working with Devin

These aren’t theoretical. They come from watching teams use Devin well and watching teams use it badly. The difference is almost always about discipline, not capability.

1. Set explicit architectural constraints

Before Devin writes a single line of code, tell it exactly how your codebase works. Which patterns to use. Which libraries are allowed. How to structure files. Where to put business logic versus infrastructure code. How you handle errors. How you name things.

Don’t assume Devin will figure it out from context. It might. It might not. And when it doesn’t, you get code that works but doesn’t belong in your project. You get Express middleware in a Fastify codebase. You get class components in a hooks-based React app. You get a new ORM when you already have one.

Write a rules file or a prompt template that covers your conventions. Include examples of good code from your repo. Specify your directory structure explicitly. Update it as your codebase evolves. The more explicit you are, the better Devin’s output. Vague instructions produce vague results.

2. Require decision documentation

Every PR from Devin should include not just the code, but an explanation of why it chose each approach. Why this library and not that one. Why this data structure. Why this error handling strategy.

If Devin can’t explain its decisions clearly, that’s a red flag. It means the code might work by accident rather than by design. Decision documentation also makes code review faster because you can evaluate the reasoning, not just the syntax.

This practice has a second benefit: it creates a paper trail. When something breaks six months later and you need to understand why a particular approach was chosen, you’ll have the context. Without it, you’re archaeology-ing through git blame trying to reconstruct the thinking of an AI that doesn’t remember the conversation.

3. Review like a new hire’s PR

Every line. Every file. Every decision.

This is where most teams cut corners. The PR looks clean. The tests pass. The description sounds right. So they skim it and approve.

Don’t do that.

Read the code like you’re reviewing a new hire’s first PR. Check that it follows your patterns. Check that the error handling is complete. Check that it doesn’t introduce unnecessary dependencies. Check that the naming matches your conventions. Check that it doesn’t over-engineer simple problems.

Autonomous doesn’t mean trustworthy. It means unsupervised. Your review is the supervision.

4. Limit Devin to isolated tasks

Devin works best on bounded, independent work. A new API endpoint with clear inputs and outputs. A utility function with a well-defined spec. A migration script with explicit requirements. A UI component that doesn’t touch shared state.

Don’t give Devin features that cut across core systems. Don’t let it refactor your authentication flow. Don’t ask it to restructure your database schema. Don’t hand it tasks that require deep understanding of how your systems interact.

Think of it like this: if a task requires reading more than two or three files to understand the context, it’s probably too interconnected for Devin. If a task can be specified in a single paragraph with clear acceptance criteria, it’s a good Devin task.

Keep Devin on the edges. Keep humans at the center.

5. Run your full test suite after every PR

Devin writes tests. Sometimes they’re good. But Devin’s tests only cover what Devin thinks matters, and Devin doesn’t have your context.

After every Devin PR, run your entire test suite. Not just the tests Devin wrote. Not just the tests for the affected module. Everything. Integration tests, end-to-end tests, performance tests if you have them.

Devin optimizes for the task it was given. It doesn’t know about the edge case in your billing module that breaks when a certain API returns null. It doesn’t know about the race condition that only shows up under load. Your existing test suite does. Use it.

If Devin’s code breaks something it didn’t test for, you want to catch that before it merges, not after it ships.

6. Human reviewer for every merge

Never auto-merge Devin’s PRs. Never. Not even if you’ve set up CI checks that all pass. Not even if Devin’s last ten PRs were perfect.

A human reviews every PR. A human approves every merge. This is non-negotiable.

The moment you start auto-merging is the moment you lose visibility into what’s happening in your codebase. And once you lose visibility, you lose control. Getting it back is expensive. We’ve seen teams that auto-merged for two months and then spent six weeks untangling the results.

CI checks verify that code compiles and tests pass. They don’t verify that the approach makes sense. They don’t verify that the code is maintainable. They don’t catch unnecessary complexity. That’s what humans are for.

7. Keep Devin out of auth, payments, and compliance

Some code paths are too important for autonomous agents. Authentication. Payment processing. Data encryption. HIPAA compliance logic. GDPR data handling. Anything where a subtle bug means a security breach, a financial loss, or a regulatory violation.

These paths need human ownership. Period. The cost of getting them wrong is too high to delegate to any autonomous system, no matter how capable.

Use Devin for the work that surrounds these systems. The admin dashboards. The reporting features. The integrations. The internal tools. Keep humans on the critical paths. The distinction is simple: if a bug in this code could make the news, a human writes it.

What happens without guardrails

We’ve seen what happens when teams skip these rules. The patterns are consistent enough to be predictable.

One team gave Devin a database optimization task. Devin refactored the entire data access layer using a repository pattern nobody on the team had seen before. The code worked. Tests passed. But when the team needed to modify the data layer two weeks later, nobody could figure out how it worked. It took a full week of reverse-engineering before anyone felt confident making changes. The “optimization” that took Devin an afternoon created a week of lost productivity.

Another team used Devin for a feature that included both API endpoints and frontend components. Devin wrote thorough unit tests for every function. Coverage looked great. But there wasn’t a single integration test. The API returned data in a format the frontend didn’t expect. It shipped, broke in production, and took a day to diagnose because the unit tests all passed.

A third team asked Devin to add a notification system. Instead of adding a function to the existing service, Devin created an entirely new microservice with its own database, message queue, and deployment configuration. Technically impressive. Completely unnecessary. The team spent more time removing the over-engineering than they would have spent writing the feature themselves.

These aren’t failures of capability. Devin did what it thought was right in each case. They’re failures of oversight. Nobody set constraints. Nobody caught the problems in review. Nobody asked whether the approach made sense for the project.

The common thread is over-trust. Teams assumed that because Devin could complete the task, it would complete it well. Completion and quality are different things. A junior engineer can also complete most tasks. That doesn’t mean you skip the code review.

If you’ve already run into issues like these, we wrote a guide on fixing projects that Devin built. But prevention is cheaper than repair.

When to adjust your Devin workflow

Watch for these signals. They mean your guardrails need tightening.

PRs take longer to review than they would to write manually. If you’re spending two hours reviewing a PR that a human engineer could have written in one hour, you’re not saving time. You’re shifting effort from writing to reviewing, and reviewing is harder because you’re working with someone else’s decisions.

Team members can’t explain what Devin’s code does. If you ask an engineer to walk through a Devin-authored module and they can’t, you have a maintainability problem. Code that nobody understands is code that nobody can fix. It doesn’t matter how well it works today.

Devin’s architecture diverges from your codebase. If Devin’s code starts looking like it belongs to a different project, your constraints aren’t tight enough. Consistency matters more than cleverness. A codebase with two architectural styles is harder to work with than a codebase with one mediocre style applied consistently.

You’re spending time undoing Devin’s decisions. If you find yourself routinely reverting Devin’s abstractions, renaming its variables to match your conventions, or restructuring its code to fit your patterns, the ROI isn’t there. Either tighten the constraints or reduce Devin’s scope.

Onboarding new engineers is getting harder. If new team members struggle to understand code because half of it follows human patterns and half follows Devin patterns, your codebase has a consistency problem. Two architectural styles in one project is worse than one imperfect style applied everywhere.

When these signals show up, don’t abandon Devin. Adjust. Narrow the scope of tasks. Add more detail to your constraints. Spend more time on prompt engineering. Consider pairing Devin with a different review process, or limiting it to specific types of work where you’ve seen consistent quality.

And if you need help building a workflow that actually works, our full-stack development team has done this for multiple teams. The right setup takes a few days. The wrong setup costs months.

Stay in control

Devin is a genuinely useful tool. Like every powerful tool, it rewards discipline and punishes carelessness. The seven rules above aren’t about limiting what Devin can do. They’re about making sure what it does actually helps your team ship better software.

The founders who get the most from Devin are the ones who invest upfront in constraints, review processes, and clear boundaries. They treat the setup as engineering work, not an afterthought. And they adjust their approach as they learn what works and what doesn’t.

Set constraints. Review everything. Keep humans on critical paths. Treat Devin like the brilliant, tireless, context-free junior engineer it is. Don’t give it trust it hasn’t earned. And when it does earn trust on a specific type of task, document that too, so your team knows where Devin is reliable and where it still needs oversight.

If you’re integrating Devin or other AI agents into your development workflow and want to make sure you’re getting value without accumulating risk, let’s talk. We’ve helped teams set up guardrails that work, so they can move fast without losing control.

Working with Devin or other AI agents? Variant Systems helps teams integrate AI tools without losing control of code quality.