[Dimittri]

Interesting. it makes sense that it depends on the number of alerts you receive. but I’d think that if 70% of the PRs you receive are noise, an AI triager could be useful—if you give it the context it needs based on your best practices. I’m very curious about the kinds of manual intervention you do on PRs when one is required. What does the follow-up prompt look like? Is it because the fix was bad, because the RCA itself was wrong, or because of something else?

[nojs]

The most common case would be defensive sentry logging that tracks unexpected LLM API call responses/parsing bugs, which are handled gracefully in the app (so not critical), but that i still want to know about to improve the prompts, response structures, cleaning code, etc.

Claude will typically resolve these in a surface level way when in practice they often require deeper changes (to prompts, routing to different model, more general cleaning code, etc) and it’s hard/impossible to have Claude do these without some input.

Other noise arises from Claude thinking related issues are unrelated and so solving them separately, and also just intermittent infrastructure type issues that are clearly transient being “solved” with some weird code change.