To optimize our frontend performance, I wanted to identify which API endpoints degraded, trace to slow dependencies, and identify the code changes responsible.
What makes this hard?
Your APM shows slow traces, but doesn't show you the code. GitHub shows you code, but doesn't show you performance. Git history shows you changes, but doesn't connect to live system behavior. So debugging requires manual work across 5 disconnected tools to:
- Query your APM to compare performance and find the slowdown
- Dig through trace spans to find the slow database query
- Search your codebase to locate where that query runs
- Check git history to find who changed it and when
- Manually connect: traces → SQL → code location → git commit → performance timeline
How did Resolve AI help?
With one query, Resolve AI automatically queried Tempo traces to compare week-over-week performance across endpoints to
- Identifies the slow dependency: ad service database queries (85% increase, 117ms → 216ms)
- Find the specific SQL query: select count(?) from product
- Locate exact code: AdService.java:219 in getAds() method
- Retrieve git history: commit 80dd1d5 from June 17, 2025
- Analyze the anti-pattern: no connection pooling, synchronous blocking, runs on every request
- Recommend the fix: add connection pooling, cache the count, or remove the query
The demo shows this happening with citations to every piece of evidence. Resolve AI has deep integrations into your telemetry, code repository, and git history. It understands that a slow database span in your traces corresponds to line 219 in AdService.java from a specific commit, and can analyze why that code is slow (connection pooling, blocking I/O, request frequency).
