Jina proactively monitors your product and fixes issues before users face them. Unlike AI coding agents that operate blind in production, Jina maintains a live service topology and unified log intelligence — correlating signals across Datadog, Sentry, CloudWatch, and more into one timeline. It auto-generates monitors with every commit, groups noisy alerts into actionable issues, identifies root causes, and opens draft PRs with fixes.
We built Jina after running into the same problem again and again. Teams have monitoring everywhere but still miss real issues or get flooded with noise.
Monitors are manually set up and go stale as the code changes. When something breaks in production, you have to piece together logs, traces, recent PRs, and third party systems just to figure out what happened. That part is slow and messy.
We started with a simple question. What if monitoring stayed in sync with the code automatically, and every issue came with context, root cause, and a clear next step?
So we built Jina to:
* create monitors directly from code changes
* group signals across tools into one issue
* use logs, code, and system context to find root cause
* suggest fixes and open a PR when possible
The main shift for us was realizing this is not about better alerts. It is about going from alert to fix without the manual work.
Curious how others are handling this today. Are you still tuning monitors manually or trying to automate this layer end to end?
