SpaceX's Lauren Tan: 11,000 PRs a month with 0.5% revert rate via agent trust system
TheTuringPost · x · 2026-11-12
SpaceX engineer Lauren Tan shared 7 concrete practices for making coding agents trustworthy at AI Engineer Summit — her team shipped 11,000 PRs in a month with 0.5% revert rate:
- Clean the codebase agents learn from: they copy workarounds, deprecated code and yesterday's questionable decisions
- Turn corrections into enforceable checks: lint rules, type constraints, tests, better APIs — a growing AGENTS.md still relies on the agent obeying
- Remove predictable mistake opportunities: restrict useEffect to framework internals, enforce import boundaries
- Give agents reliable ways to run and inspect the product: debugging tools, reusable CLIs, a user-perspective feature map
- Review evidence of behavior: screenshots, videos, state diagrams beat a convincing explanation of a fix
- Spend serious effort on verification: runtime verification, property-based UI testing, TLA+; most tokens go to verification
- Automate once the loop is reliable: agents now exercise releases, find bugs, open and merge fixes — took 6 months of environment investment
Her takeaway: automating a slop-producing loop yields faster slop with a bigger token bill. Make good code the easiest thing to produce, then scale.
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