Check logs for errors -> if errors then collect stack traces and env vars -> paste to ticket -> if deployment then do a rolling restart. If a CI/CD job has an error then find the tests that it referenced -> find the deployments referenced by the test -> collect env vars, manifest and stack traces from deployment -> if existing ticket then paste to ticket -> if no ticket then create a new ticket. Untangling the test environment again. Collect logs -> -> grep for stack traces -> file ticket -> restart VM (AWS). Check for high error rate nginx paths -> find deployment -> check deployment resource health -> copy logs to a ticket -> restart deployment. Health check /login for http 200s -> check auth microservice logs for errors. Check postgres write-ahead log storage utilization -> add emergency capacity and escalate immediately. Helping developers with repetitive troubleshooting. Check Kubernetes Error events for Deployment -> check logs for application errors -> check CPU/mem/IO metrics -> check node for noisy neighbors -> paste all info into a ticket. Check databricks for failing job references -> if databricks job failed then check node health under Deployment -> if node health is OK then check databricks dependent deployment for Error Events. If developer says service is down -> help the developer run liveness probe check and collect recently logs and notify of find the service owner. Collect env status and pod logs -> paste into new ticket -> rolling restart deployment. Triage noisy alerts. Run test env pre-flight check -> check all Deployments are in ready state -> check transaction table has at least 1 row. Check transaction queue is <100 items deep -> if not, collect env info, deployment logs and file a ticket. Collect StatefulSet manifest -> paste to ticket. Manual health checks. Check certificate is valid -> if not, rotate certificate. Check Ingress for Warning Events. Check Ingress log for error messages. Increase cpu/memory capacity for Azure Web App. Check Ingress for paths with high rates of 500 errors. Read/write test key to Redis. Read test row from postgres -> restart VM if query returns no rows. Check kafka client latency. Restart kafka client to rebalance. Search logs and paste results to ticket. Add env vars to ServiceNow ticket. Confirm no root account logins in last 30 days. Check volume for utilization. Add emergency 10Gi storage capacity to volume. Add emergency 500 millicores CPU capacity. Compare deployment manifest to Vertical Pod Autoscaler CPU/Mem recommendations -> if misaligned then prepare manifest to align them in a PR -> email service owner. Check manifest for readiness probe configurations -> if missing then notify service owner -> if incorrect then prepare a PR with a fix and file a ticket. Check manifest for non-standard open ports -> if non-standard ports then check exception list -> if not on exception list then file a ServiceNow ticket. Check oauth login latency -> if latency is slow then restart VM -> email service owner. Check queue is less than 60% of capacity -> if queue is beyond basic capacity then check CPU / memory -> if CPU/memory is high then copy recent logs to a ticket and emergency restart process. If test body mentions vault error then do test read/write in vault test path with pod credentials -> if vault test read/write fails then try with default read only credentials -> if that fails then notify service owner. Check test DB is running and volume is not full and login string matches and no key tables locked and test user is entered in user table -> if any fail, stop running tests and notify test owner. Check CPU is not >80% for the last 5 minutes. Check memory is not >80% for the last 5 minutes. If resource utilization is over limits, open a PR for capacity increase. Check Azure metrics for http 500 rate overnight. Check logs for errors after deployment scale-up. Check for high CPU/mem after deployment scale-to-one.Run test env pre-flight check that all Deployments are in ready state and transact