Cursor engineer Lauren Tan: 600+ PRs to let AI agents auto-merge code
xiaohu · x · 2026-09-08
Lauren Tan, Cursor engineer (ex-Meta React compiler, ex-Netflix), shared at Maven how she went from supervising every agent step to letting them auto-merge code — treating agents like new hires who can code but don't know the product.
Three conditions she builds:
- Verification: agents launch the app, drive the UI, and collect CPU traces/memory snapshots themselves. Her early bottleneck was humans reviewing screenshots while multiple agents queued for one reviewer.
- Product knowledge: feature maps (entry points, interactions, selectors, preconditions) plus skills docs, with missing steps written into task specs and validated on real tasks, re-checked across tasks and models.
- Engineering constraints: recurring mistakes become automated checks that fail loudly, while correct patterns are made easy — enabling more parallel changes.
Cost caveat: the refactor took 600+ PRs and heavy token spend, and she notes her AI-lab token budget isn't available to most teams. She describes waking up to 20 auto-merged PRs once trust was established.
Related event: Cursor Engineer Shares How to Trust Coding Agents with Auto-Merge(2 posts)→
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