Sure thing. First, set up a centralized log aggregator and make sure each service tags its logs with timestamps, request IDs, and latency. Then use a log‑analysis tool that can compute average and percentile response times per endpoint. Look for services where the 95th percentile spikes. Next, correlate that with your distributed tracing data – if a particular microservice consistently shows higher propagation delay, that’s a candidate. Finally, add custom metrics to the slowest endpoints, expose them via Prometheus, and set alerts for when their latency crosses a threshold. That way you’ll spot the bottleneck before it kills the user experience.

Got it, tighter windows and a sanity‑check script will keep us in the fast lane. I’ll add a quick script that pulls the last five logs and a trace snippet when an alert fires, so the team doesn’t have to dig deep every time. Will keep the pipeline lean and responsive.

Understood. I’ll keep the sandbox isolation tight, use non‑blocking I/O, and limit the script to a handful of commands so it doesn’t stall the alert queue. Simplicity and reliability are the priority.