Resolve AI investigated deployment alerts by checking pod status, analyzing container crash logs, reviewing recent deployment changes, comparing image versions, and identifying why a new deployment is failing while explaining the rollback path to restore service.
What makes this hard?
Your alert fires when pods are unavailable, but doesn't tell you why they're crashing. kubectl shows CrashLoopBackOff status, but not what's failing inside the container. Logs show graceful shutdown sequences, but don't explain why the application exited. Deployment history shows version changes, but not which version introduced the problem. Manual deployment debugging requires racing through multiple tools during an outage:
- Query alert system to understand what triggered and when replicas became unavailable
- Check kubectl for pod status, events, and restart counts
- Tail container logs across multiple crashing pods to find error patterns
- Review deployment history to identify recent changes and image versions
- Compare working versus failing configurations to isolate the regression
- Manually connect: alert timeline → pod crashes → deployment changes → image regression → rollback solution
How did Resolve AI help?
With one query, Resolve AI investigated the deployment alert across infrastructure, logs, and deployment history:
Identified complete service unavailability: All 3 ad service replicas in CrashLoopBackOff state with multiple container restart attempts—0 out of 3 desired replicas available Analyzed crash pattern across pods: All three pods showed identical failures—Java agent initialization succeeded, then immediate exit code 1 within seconds, followed by graceful gRPC server shutdown Found deployment regression through version comparison: Rolling deployment introduced an older image version that crashes on startup, regressing from a working newer version Built precise failure timeline: Rolling deployment initiated → pods scheduled → Java agent loads successfully → first pod crashes 4 seconds later → remaining pods crash immediately → alert fires when all replicas unavailable Identified investigation limitation: Application exited with code 1 but logs contained no error messages or stack traces explaining the startup failure—graceful shutdown suggested intentional termination rather than unexpected crash Recommended resolution path: Rollback deployment to previous working version to restore service immediately, then investigate why the older image version fails to start
What did Resolve AI deliver?
Resolve AI produced a structured incident report with four critical components:
- Root Cause Analysis: Deployment of older image version with startup failure. Rolling deployment introduced a regressed version that crashes immediately on startup. All new pods crashed within seconds following standard Kubernetes rolling update pattern, while containers successfully initialized the OpenTelemetry Java agent before failing.
- Causal Timeline: 4-event sequence showing deployment failure—rolling deployment begins → Java agent initialization in first pod → first pod crash with exit code 1 seconds later → alert fires when all replicas remain unavailable.
- Impact Assessment: Complete service unavailability with 0 out of 3 desired replicas available. All ad service functionality impacted during failed rollout with pods in CrashLoopBackOff showing multiple restart attempts. Resolve AI connected the deployment timing to crash pattern (exit code 1 within seconds) to version regression (newer working version → older failing version) to provide immediate rollback guidance. The investigation revealed that the older image version had a startup failure, but the graceful shutdown sequence suggested intentional termination rather than a crash—a critical distinction for understanding whether this was a configuration issue versus a code defect.Retry
