dropboxMay 21, 2026

Introducing Nova, our internal platform for coding agents (opens in new tab)

Nova is Dropbox’s internal cloud platform for running coding agents across the software development lifecycle. Rather than building separate tools for coding, CI debugging, migrations, and operational tasks, Dropbox created a shared platform that supports interactive sessions and autonomous workflows within its monorepo and infrastructure. The platform grounds agent changes in real builds and tests, making AI assistance more reliable and easier to integrate into engineering workflows. ## The Case for a Shared Platform - Engineering work includes repetitive but important tasks such as: - Debugging CI failures - Updating dependencies - Improving test coverage - Fixing flaky tests - Managing migrations and operational work - Different tasks require different interaction models: - Interactive chat for developer-driven work - Asynchronous workflows for long-running remediation and automation - Dropbox’s environment has specialized requirements: - A large monorepo - Bazel for builds and tests - Caching and remote execution - On-premises infrastructure - Dropbox-specific validation workflows - Off-the-shelf coding agents were designed primarily for local development and did not naturally fit this environment. ## How Nova Runs Coding Sessions - Each session runs in an isolated environment using a specific snapshot of the codebase. - Callers provide: - The repository commit - A task description - Optional validation commands - Iteration limits and branch settings - Nova can run builds and tests after an agent proposes a change. - If validation fails, the results are sent back to the agent so it can continue troubleshooting. - This creates a feedback loop of: - Propose a change - Validate it in the real environment - Correct failures - Repeat as needed - Nova supports multiple coding agents behind a common interface. - Engineers can access it through: - A web interface - A command-line client - An API - Internal scripts and services - The platform also provides prompt evaluation, observability, feedback collection, skills, plugins, and MCP integrations for accessing systems such as logs and monitoring tools. ## Deterministic Code Publication - Nova keeps code publication outside the agent. - Each session is limited to a single branch. - This makes active work and publication status predictable. - It avoids the complexity of agents creating and managing multiple branches. - The deterministic model simplifies automation such as: - Running tests - Rebasing onto the main branch - Tracking which changes belong to each session ## Engineering Workflows Using Nova ### Developer-Driven Sessions - Engineers use Nova’s web interface for quick fixes and prototypes without disrupting local work. - Validation commands can use Bazel selectivity tools to target the relevant compile and test dependencies. - Slack discussions can be carried into Nova sessions, preserving context and reducing manual setup. ### Flaky Test Remediation - Dropbox built Deflaker, a durable workflow connected to Athena, its flaky-test detection system. - Deflaker gathers examples of a test passing and failing. - It sends the associated logs to Nova. - The agent analyzes the evidence, identifies a likely cause, and proposes a fix. - This demonstrates how Nova can combine investigation, context gathering, and code changes in a longer-running automated process. ## Practical Takeaway Dropbox’s experience suggests that coding agents are most useful when embedded in existing engineering systems rather than treated as isolated code-generation tools. A shared platform like Nova can support many workflows while preserving consistent execution, validation, context, and observability.

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dropboxMar 25, 2026

Reducing our monorepo size to improve developer velocity (opens in new tab)

Dropbox’s server monorepo grew to 87GB, making full clones take over an hour and threatening GitHub’s 100GB limit. The root cause was inefficient Git delta compression of internationalization files, not unusually large source files. By changing how the repository was repacked, Dropbox reduced it to about 20GB and cut clone times to under 15 minutes. ## Repository Size and Developer Velocity - The monorepo contains backend services and libraries used across Dropbox. - AI feature development often requires coordinated changes across ranking, retrieval, evaluation, and UI systems. - A full clone exceeded one hour at 87GB, slowing onboarding and affecting CI jobs that start from fresh clones. - Internal synchronization systems also processed more data, increasing timeout and reliability risks. - The repository grew by roughly 20–60MB per day, with occasional increases above 150MB. - At that rate, Dropbox expected to hit GitHub Enterprise Cloud’s 100GB hard limit within months. ## How Git Compression Caused the Growth - Git normally reduces storage by representing similar file versions as deltas rather than complete copies. - Its default file-matching heuristic considers only the final 16 characters of a path. - Dropbox’s i18n files used paths such as: - `i18n/metaserver/[language]/LC_MESSAGES/[filename].po` - Because the language component appears early in the path, Git often compared files from different languages instead of related versions of the same language. - Translation updates consequently produced oversized deltas and disproportionately large pack files. ## Testing `--path-walk` - Dropbox tested Git’s experimental `--path-walk` option during a local repack. - The option considers the full directory structure when selecting delta candidates. - A local repack reduced the repository from the low-80GB range to the low-20GB range, confirming that packing—not data volume—was the main issue. - GitHub could not use this approach because it conflicted with server-side optimizations such as bitmaps and delta islands. ## Why Server-Side Repacking Was Necessary - Local optimization cannot permanently change the packs GitHub generates for clones and fetches. - GitHub dynamically constructs transfer packs based on what each client needs. - Dropbox’s mirror experiment showed that an aggressive repack could reduce the repository from 84GB to 20GB: - `git repack -adf --depth=250 --window=250` - The repack took approximately nine hours. - Dropbox worked with GitHub Support to apply a compatible server-side solution. - Larger `window` and `depth` values make Git search more thoroughly for compression opportunities, trading increased repack time for smaller storage and transfer sizes. ## Results - Repository size fell from 87GB to approximately 20GB—a 77% reduction. - Clone time dropped from more than an hour to under 15 minutes. - The work reduced pressure on GitHub’s repository size limit and improved the performance of developer and CI workflows. Dropbox’s experience shows that monorepo growth can result from repository layout interacting poorly with Git’s compression heuristics. When large repositories exhibit abnormal growth, teams should inspect pack-file behavior and consider server-side repacking rather than focusing only on removing large files.

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dropboxFeb 11, 2026

Insights from our executive roundtable on AI and engineering productivity (opens in new tab)

Dropbox argues that AI improves engineering productivity only when tied to measurable business outcomes rather than adopted for its own sake. The company has expanded AI use across the software development lifecycle, while recognizing trade-offs involving quality, maintenance, and organizational change. Its executive roundtable concluded that leadership, formal AI competency, and stronger outcome measurement will be central to realizing AI’s potential. ## Dropbox’s AI Adoption Strategy - Dropbox made AI adoption a company-wide priority with leadership sponsorship, enabling teams to experiment more easily and reducing delays in approving new tools. - Engineers use AI across code review, documentation, debugging, testing, and other stages of development. - Because Dropbox operates a large, multilingual monorepo, it combines commercial tools such as Claude Code and Cursor with internally built systems. - One internal tool detects failed pull-request builds and uses Dropbox’s AI platform to suggest fixes. - Most developers now use at least one AI tool. - Dropbox tracks monthly pull-request throughput per engineer and has observed higher output among developers who use AI coding tools more actively. - The company also monitors engineer sentiment, reporting increased positive sentiment and reduced negative sentiment as adoption improves. ## Focus of the Executive Roundtable Leaders from multiple companies discussed engineering productivity and AI in rotating peer groups organized around three themes: - **Measuring impact** - Identifying ways to measure AI-driven productivity gains. - Connecting engineering improvements to broader business results. - **Leadership alignment** - Establishing how executives should communicate AI deployment progress. - Determining the appropriate pace and scope of adoption. - **The human element** - Recruiting, evaluating, and developing AI-capable employees. - Applying lessons from developer productivity to help non-engineering teams work more effectively. ## Lessons About AI and Productivity - **Balance is essential:** Faster development must not come at the expense of software quality or increased long-term maintenance costs. - **Leadership sets standards:** Technical managers play a key role in defining responsible and effective AI usage norms. - **AI skills should be formalized:** Including AI competency in career frameworks demonstrates that it is a lasting strategic capability rather than a temporary trend. - **Extra capacity needs direction:** Dropbox is currently using productivity gains to address technical debt, complete migrations, and improve reliability. ## Priorities for 2026 Dropbox’s main unresolved challenge is linking engineering productivity metrics to tangible business outcomes. Its next phase will focus on mapping AI-driven gains to specific results, extending operational discipline beyond engineering, and improving end-to-end product velocity.

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