How AI-assisted development helped us build the new Dropbox experience

For three years, we’ve been working toward a new Dropbox experience that helps people do more with their files, from finding a project decision to getting a folder ready to share. Today, we’re beginning to roll that experience out to customers. But getting here meant changing how we build software. We had complex integrations to tackle and a deadline to meet, and AI-assisted development helped us take on work that would've previously been difficult to complete in time for launch.

The new Dropbox experience puts that investment into practice by helping customers understand what’s in their files and get work done with them. Ask Dropbox what changed in a project folder, and it answers with sources you can open and check. Ask it to organize a folder, rename files based on their contents, or combine documents into a PDF, and it shows a plan you can review and edit before approving. Dropbox only reads and acts on content you already have permission to access, and supported file changes can be undone. And the results stay in Dropbox, where teams can review, share, and keep working with them.

Delivering that experience meant building new capabilities on web and bringing them into desktop, with shared code that let us develop features for both applications. Engineers took broader ownership of product decisions and brought quality checks into development from the start. As the work progressed, we moved from using AI to help with individual coding steps to delegating defined tasks—including implementation and testing—for engineers to review. Together, those changes helped us deliver a product years in the making within an ambitious launch timeline.

We began with a broad product direction and a lot of details to work out. We needed to turn that direction into specific experiences people could use, including a fuller desktop app. Desktop had primarily focused on keeping files in sync, while web already supported richer ways to browse and work with content. We wanted to build on that foundation and bring both existing features and new capabilities developed on web into desktop without rebuilding everything separately for each platform.

The difficulty was that web and desktop had grown around different needs. They used different tools to build and release software and handled tasks such as signing in, navigating between pages, and retrieving data differently. A web feature could depend on services that desktop didn’t provide in the same way. Before we could reuse it, engineers needed to identify those dependencies and work out how to support them—a substantial integration effort to complete before our November launch deadline.

During our Hackathon in May, engineers built a working prototype that explored two possible approaches to tackling this problem, and AI helped them trace dependencies, identify platform-specific assumptions, and test the integration. One approach displayed the existing All Files page inside a desktop-specific window using Electron, the framework behind desktop’s interface. An embedded web view—a browser surface inside the app—let us bring the existing web browsing experience into desktop quickly. The second approach imported selected web components directly into desktop’s user interface. These individual building blocks required more integration work but gave us greater control over how they fit into the desktop app.

The working prototype gave Product and Design something concrete to try before we committed to a larger architectural change. Engineers turned directional designs into usable flows, surfaced missing states, and proposed options as platform constraints became clear. Product and Design worked through those choices as engineers built the features, allowing us to refine the experience as we learned. We found that both approaches had a place: complete web pages where we wanted close parity, and individual components where we needed more control. AI reduced the effort of testing those options, helping turn a long-standing product direction into an early version of the desktop experience we developed for launch.

Alongside the desktop work, engineers used coding agents to implement defined interface changes across Home, Library, and file previews. These included the Jump Back In list view and grid-to-list toggle, giving people another way to browse and return to their work. Agents also handled smaller updates to menus, upload banners, and shared styling. Handing off those tasks gave engineers more time to focus on how the product should behave and how the experiences should connect. That combination helped us move the broader desktop experience and its individual interface improvements toward launch.

The prototype showed that we could bring more of the web experience into desktop. Next, we needed a way to keep improving both applications without rebuilding the same features twice. We wanted Dropbox to feel familiar whether someone used it on web or desktop. That meant reusing the code that makes a feature look and work the same in both places, while letting each app handle things like signing in and moving between screens in its own way.

We share React components—the pieces of code that build what people see and interact with—along with the code they need to work with data. Web and desktop each build and test those components as part of their own app. When a shared feature needs information, it asks for it in the same way on either platform. Web handles that request through the browser, while desktop uses its own networking system. That lets us reuse the feature without making both apps work exactly the same way behind the scenes.

Engineers had to work out which code could be reused and which parts needed to work differently in each app. AI helped them follow the connections between those pieces, pull out reusable components, and write adapters—the code that connects a shared feature to each app’s services—along with tests. Engineers reviewed the resulting code to check that it worked as intended. AI helped us complete that integration before launch, putting an approach we had discussed for years into practice. Now, improvements to shared features have a path to both web and desktop.

The new capabilities also needed to work for people using a keyboard, a screen reader, or an enlarged display. As we prepared for launch, engineers used AI-assisted development to deliver 22 accessibility features across Home, All Files, file previews, and Photos. These included announcements that help screen-reader users follow changes in the Up Next carousel on web and desktop, and improvements to navigating the All Files table. The team also fixed how keyboard focus moves between controls and adjusted layouts to keep pages usable at 200%–400% zoom.

To carry out this work, the web team gave coding agents clearly defined tasks and a consistent process to follow. Each task started with a written plan agreed on by the engineer and agent. The agent made the changes, ran the relevant builds and tests locally, and recorded the results for an engineer to review before accepting the code. Instructions stored with the code explained the rules agents needed to follow, giving engineers a repeatable way to delegate work while remaining responsible for the result. A working group and champions across teams helped establish those shared instructions, task formats, and review expectations, so the approach could be used consistently across the project.

Retiring an older file list had been deferred for two quarters because updating the automated tests required substantial engineering time. AI-assisted development gave the team a practical way to tackle that cleanup alongside its launch work. Engineers divided the cleanup into 53 tasks. Coding agents then updated or removed the affected tests using the same testing and review process, and the team finished all of those tasks in one week.

The team also used coding agents to add automated tests for five core parts of the web app, including the file browser people use to navigate their content. By checking for problems when the code changes, these tests help engineers catch issues before they reach customers. 

Beyond writing tests, AI agents tried out the experience during development, which helped engineers catch problems while they were still working on the relevant code. In one case, an agent found that a deleted item in Chat history could remain visible until the page was refreshed; engineers fixed the underlying synchronization issue and added tests to prevent it from returning. This let human reviewers spend more time assessing whether the experience made sense to customers, while automated checks helped catch functional problems.

And importantly, customer feedback helped us decide what to improve and when to expand access. All eight of our initial design partners chose to keep the new experience enabled. As we introduced the experience to larger groups, we watched how people used familiar features such as upload, preview, search, and sharing, and tracked whether AI actions succeeded and whether customers switched the experience off. Those signals helped us prioritize issues, while the coding-agent workflow gave engineers a consistent way to implement and check fixes. AI helped us work through the changes; customer feedback and engineering review guided what was ready to reach more people.

The new Dropbox experience reflects years of work toward helping customers understand their content and complete more of the tasks around it. AI-assisted development helped us bring that direction to launch by making it easier to test a fuller desktop experience, connect features across web and desktop, and address accessibility and testing work while building new capabilities. Engineers guided that work, deciding how the applications should fit together and checking the changes against the behaviors customers depend on.

The value of that work extends beyond this release. Shared components let improvements reach both applications. And the process engineers used to delegate and review accessibility fixes and testing work gives them a way to tackle further improvements, with customer feedback helping determine where to focus.

Together, these practices give us more room to improve Dropbox as we learn how people use it. They also broaden the work engineers can take on: understanding customer needs, trying out interactions, weighing product decisions, and following changes through release. Technical expertise remains essential, and AI gives engineers more opportunities to apply it across those responsibilities. Their judgment and customers’ needs continue to guide what we build and why.

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