CursorCursorProblem & solution
Amplitude ships 3x more production code with Cursor
Summary
Amplitude’s engineering team built toward an autonomous development pipeline that moves software from idea to production with minimal developer intervention, using Cursor to connect customer feedback, observability, code reviews, and other lifecycle context to agents. Cloud agents run in isolated, scalable VMs with complete development environments, enabling parallel, long-running, self-testing work that local machines could not support at the same scale. Automations now turn Slack bug reports and feature requests into Linear tickets and pull requests, perform hourly legacy migrations, and classify pull-request risk, while Bugbot reviews and autofixes low-risk changes. Since adopting cloud agents, Amplitude reports a threefold increase in weekly production commits, more than 1,000 unprompted agent runs per week, and Cursor as a top-three contributor by commit volume. About 60–70% of low-risk pull requests merge without additional developer work, while high-risk changes are routed to appropriate reviewers; the next goal is automating CI/CD, build validation, and deployment.
Context
Amplitude found that local coding agents were constrained by developer-machine resources, competed for limited capacity, lacked complete development environments for testing, and required manual configuration and verification. The team also faced customer-report triage, legacy frontend patterns, and manual code review as bottlenecks to engineering velocity.
Approach / What changed
Amplitude uses Cursor cloud agents in isolated scalable VMs, alongside local agents, to run parallel and long-lived tasks with full development environments. Cursor Automations connect Slack reports to Linear tickets and pull requests, execute scheduled CSS and React migrations, and classify pull-request risk. Bugbot reviews changes, autofixes low-risk issues, and routes higher-risk work to engineers.
Takeaways
Cursor cloud agents let Amplitude run many isolated, parallel, long-running tasks with access to a complete development environment for testing and verification.
A Slack-triggered Automation checks Linear for existing issues, adds customer context, or investigates the codebase and creates a ticket and pull request.
An Automation classifies pull-request risk: roughly 60–70% of low-risk PRs merge without additional developer work, while high-risk PRs are routed to the appropriate engineers.
Source and freshness
- Source
- cursor.com
- Published
- Added to Yomu
- Last updated
Summaries are generated from the original by a language model. Check the original before relying on it.
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