How to Use Devin AI for Building Applications

Building an app goes way beyond writing and shipping code.

The process is packed with understanding the repo, making a plan, changing the right files, running tests, debugging failures, and opening a clean PR that someone can actually review.

That is the gap Devin AI is meant to close.

Devin is designed to take on real engineering work end to end, from ticket to plan to tests to pull requests, using a sandboxed dev environment and common developer tools.

In this blog, you will learn how to use Devin AI to build applications, including scoping work in Ask mode, turning that scope into an agent session, reviewing what it proposes, and getting to a PR you can ship with confidence.

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What Is Devin AI?

Devin AI is an autonomous AI software engineer created by Cognition Labs. Unlike code-completion tools that suggest snippets, Devin acts as a full-fledged agent that independently plans, writes, tests, and debugs code to complete entire development tasks.

Further, Devin operates in its own secure, sandboxed environment equipped with a shell, code editor, and browser. This allows it to execute entire workflows from start to finish.

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What Devin AI Does Best for Application Development

Here are the use cases where Devin provides the most value: ✨

• Full-stack prototyping: Give it a prompt, and Devin can scaffold an entire application, handling the frontend, backend, and database setup to get a new project off the ground in minutes
• Codebase migrations: It can take on the tedious work of updating dependencies, refactoring legacy code to modern standards, or migrating an entire application to a new framework
• Bug fixing and debugging: Point it to a bug report or an issue, and Devin can trace the problem, write the necessary code to fix it, and run tests to verify the solution works
• Writing tests and documentation: It can generate unit tests, integration tests, and inline documentation for existing code, improving your codebase’s health and maintainability
• Repetitive feature implementation: It excels at building out standard features like CRUD (Create, Read, Update, Delete) operations, new API endpoints, and form handling

While Devin is a powerful executor, human oversight remains essential. You’ll still need your team’s expertise for nuanced user experience decisions and complex system architecture. Devin follows instructions but doesn’t make strategic tradeoffs on its own.

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How Devin AI Works

Devin operates on an ‘agent loop.’ It receives a task and creates a step-by-step plan.

Then it executes those steps in its sandboxed environment and analyzes the results to inform its next move. This planning-then-execution cycle allows it to handle complex, multi-step development work.

To get the job done, Devin uses three core tools:

• Shell: It can run terminal commands to install dependencies, execute scripts, and manage its environment
• IDE: It has a fully functional code editor to read, write, and modify files in your project
• Browser: It can access the internet to look up documentation, read API specs, and gather information needed to complete a task

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How to Set Up Devin AI

Before you can build your first application, you need to complete three setup steps: creating an account, connecting your repository, and configuring your workspace.

Make sure you have a GitHub account, a repository to work with (a new or existing one is fine), and a first task in mind.

1. Sign up and choose a plan

First, head to Devin’s website to create your account and complete the onboarding process. Your plan will determine your session limits and available features. Teams can easily start with individual accounts and scale up as their usage grows.

2. Connect your GitHub repository

Next, you’ll connect your GitHub account. Devin uses OAuth, a standard and secure authorization protocol, to request access to your repositories. This allows the tool to read your code, create new branches, and open PRs with the code it generates. For your first few experiments, it’s a good idea to start with a test repository or a non-critical project.

3. Configure your workspace

Finally, configure your workspace settings. Here, you can define environment variables, specify preferred frameworks, and set coding standards. Devin can easily learn your project’s specific conventions, especially if you provide context in your initial prompt or include a README.md file in your repository.

4. Start your first Devin session

Once repos are indexed/onboarded:

1. Create a new session and give Devin a task prompt (bug fix, test writing, refactor, small feature)

2. Ask Devin to propose a plan first, then execute
3. Review the changes and have Devin open a PR when ready

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How to Build Your First Application With Devin AI

Building with Devin is a collaborative process, not a one-shot command. Expect to iterate, and you’ll get the best results.

1. Write an effective prompt

The quality of your prompt directly determines the quality of Devin AI’s output. The more precise your instructions, the fewer iterations you’ll need.

Use this framework for a powerful prompt:

• Be specific about the outcome: Instead of ‘Build an app,’ try ‘Build a task management API with user authentication using Node.js and PostgreSQL’
• Include technical constraints: Specify the programming language, framework, database, or styling libraries you want it to use
• Define acceptance criteria: Be explicit about the deliverables. Specify the endpoints, UI features, test coverage, and the commands that should run successfully
• Provide context: If you have existing code, documentation, or a broader project plan, link to it. The more the context, the better

2. Review Devin’s plan

Before it writes a single line of code, Devin will generate a step-by-step plan. This is your most important checkpoint. Review the plan to ensure it addresses all of your requirements and that no steps seem misguided.

Catching a misunderstanding here saves hours of code review later. You can give feedback and ask Devin AI to revise its plan before it begins.

3. Monitor progress in the interface

Once Devin AI starts working, its interface gives you a real-time window into its process. You can see the shell commands it’s running, the code it’s writing in the IDE, and the websites it’s visiting in the browser.

Keep an eye on its progress and be ready to intervene if it gets stuck in a loop or starts heading in the wrong direction. You can pause, offer guidance, and then let it resume with your new instructions.

4. Review and merge the pull request

When Devin AI completes the task, it will create a new branch and open a pull request on GitHub with all of its changes.

And by now, run a standard code review. Check for edge cases, verify whether all tests are passing, and manually inspect any security-sensitive code. In the meantime, Devin AI can respond to feedback on the pull request, iterating on its work until you’re ready to merge.

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Tips for Getting Better Results From Devin AI

Here are some lessons learned from teams using Devin AI effectively: 

• Define outcomes in observable checks: Replace fuzzy goals with pass/fail criteria like ‘tests pass,’ ‘endpoint returns 400 on invalid payload,’ ‘no new lint errors,’ or ‘p95 latency improves by 20%’
• Always include a verification loop: Give Devin the exact commands to run and what ‘success’ output looks like, so it can validate work
• Ask for a plan before implementation: Have it outline the approach, files it will touch, edge cases, and risks first to make sure you can catch wrong assumptions early
• Anchor Devin to existing patterns in your repo: Point it to one similar endpoint/component and say ‘mirror this structure’ to prevent new architectures or inconsistent styles
• Control refactors: Specify ‘minimum viable change’ and allow cleanup only if required for correctness
• Constrain the blast radius: State what must not change, like public API shape, database schema, auth flows, or build tooling, to keep the solution focused and safe

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Limitations of Using Devin AI to Build Applications

Devin AI, too, has limitations you need to keep in mind. And they include:

❌ Complex architectural decisions: Devin AI is an excellent executor, but it won’t make high-level strategic tradeoffs about your system’s design. That’s still part of your job

❌ Highly ambiguous requirements: Without clear acceptance criteria, Devin AI might produce a solution that is technically correct but contextually wrong for your business needs

❌ Novel or cutting-edge frameworks: Devin AI’s knowledge is vast but not infinite. If you’re working with a brand-new or obscure library, it may struggle to find relevant documentation

❌ Security-critical code: You should always have a human expert manually review any code related to authentication, authorization, and data handling

❌ Long-running sessions: For very large, complex tasks, Devin AI may hit its context limits or require careful session management to complete the work

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How to Manage Devin AI Projects in ClickUp

Using an AI agent like Devin introduces a layer of chaos you didn’t consider earlier. That’s AI sprawl, where agent work spreads across tools and tabs until it becomes expensive, duplicated, and risky. 

One day, the task lives in the Devin UI, the decisions are buried in chat, and the real truth is hidden in a GitHub pull request. Your team ends up context-switching all day just to answer basic questions!

This is when many shift to ClickUp. As the world’s first Converged AI Workspace, ClickUp gives you one place to manage the complete lifecycle of agent-driven engineering work, including specs, execution plans, tasks, approvals, and the audit trail. Let’s see how:

Standardize every Devin request with a single intake Doc

First and foremost, you need to standardize all requests with ClickUp Docs. In other words, use one repeatable intake Doc template as the front door for every operation you run on Devin AI, so the actual spec never gets trapped in a chat thread or buried inside a GitHub pull request.

ClickUp Docs are structured, navigable documents you can build with pages and subpages. This means a single Devin project can hold everything from the initial brief to edge cases and prompts without turning into a scroll nightmare. You can keep long Docs easy to scan with a table of contents and collapsible sections, and restructure content as the scope evolves.

That means your intake can be consistent and reviewable every single time, for example:

• What is the exact change, and what does done look like?
• Which repo paths should Devin work in, and which file should it mirror for patterns?
• What must not change, like API shape, auth flow, or schema?
• What are the acceptance criteria, and the exact commands to verify success?
• What are the known edge cases, performance constraints, and security notes?

And when the Doc turns into action, just highlight text and create ClickUp Tasks from it, which is perfect for turning ‘open questions’ and ‘review feedback’ into owned work items.

Scale and govern project delivery with ClickUp Super Agents

When you start using Devin AI across multiple teams, the problem is no longer ‘Did we follow through?’ In contrast, it’s the operational control. That means keeping agent work aligned with standards, enforcing approvals, and proving outcomes without manual policing.

And to do that (and so much more), use ClickUp Super Agents. They are AI-powered teammates that can run multi-step workflows across your Workspace, beyond single, rule-based actions. These are basically your own AI coworkers active 24/7.

Use Super Agents to handle higher-order operational work around Devin AI, like:

• Enforce quality gates before a Devin project runs, by validating the intake Doc has acceptance criteria, constraints, and verification commands
• Route high-risk changes through the right approvals, so security and platform reviews happen by design
• Generate an auditable ‘review packet’ for every GitHub pull request, including what changed, how it was tested, and what still needs sign-off
• Create traceability automatically, since Super Agent activity can be reviewed in the Super Agents Audit Log for troubleshooting and audit purposes

Build coding agents with Codegen by ClickUp

If you like the idea of Devin AI, but do not want engineering work spanning yet another product, use Codegen by ClickUp. ClickUp’s Codegen is an AI developer teammate that completes tasks, builds features, answers code questions, and creates production-ready pull requests.

What makes it even better is the workflow shape. Unlike Devin AI, which follows a cycle of ticket-to-plan-to-test-to-PR, Codegen operates differently in ClickUp.

It acts as an autonomous AI agent that can:

• Read task descriptions and full context (including docs, comments, attachments)
• Generate high-quality, production-ready code from natural language prompts or task details
• Build features, fix bugs, or implement changes
• Create and open pull requests (PRs) in your Git repo
• Answer code-related questions

Integrate with GitHub using ClickUp Integrations

With ClickUp Integrations that pair with GitHub, you can link commits, branches, and pull requests directly to a ClickUp Task and see that activity from inside the task. It also supports rich link previews when someone pastes a GitHub link into a task, Chat, or Doc.

And to keep linking friction low, your team can reference the ClickUp Task ID in commit messages, branch names, or pull requests using formats like #{task_id} or CU-{task_id}, so the activity shows up where it belongs, without manual copy-paste.

Even more, ClickUp supports GitHub-triggered automations. Long story short, once the code is linked, you can automatically move statuses, notify reviewers, or kick off next steps based on GitHub events. 

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Ship the Workflow, Not Just the Code, With ClickUp

Autonomous coding agents are quickly becoming part of how software gets built. Teams that learn how to work with them well will ship faster, iterate more confidently, and give developers more room for the high-value, creative problems humans are best at.

But there’s an even bigger win than ‘faster code.’ It’s running the entire development lifecycle with more clarity.

Do that and more with ClickUp. Use Codegen by ClickUp when you want an AI coding agent that helps you implement and ship work. Pair it with ClickUp AI Agents and ClickUp’s powerhouse of integrations to connect PRs, issues, docs, approvals, and release checklists in one place.

✅ Get started for free with ClickUp today.

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