Thanks for sharing the log. Here’s what it means and how to track down the actual cause. What that warning means - The message from kopf.objects is from the Prefect Kubernetes Observer. It saw that your Kubernetes Job failed and no other retries are running for that flow run, so the flow run was marked as crashed in Prefect. - The Kubernetes event reason BackoffLimitExceeded means the Job’s pods failed more times than the Job’s backoffLimit (default is 6). The real cause is almost always visible in the failing pod(s) events and logs. Quick path to the root cause Replace <ns> with your namespace and the job name with misty-galago-jmj4x. 1) Inspect the Job and its events

kubectl -n <ns> describe job misty-galago-jmj4x
kubectl -n <ns> get events --field-selector involvedObject.kind=Job,involvedObject.name=misty-galago-jmj4x --sort-by=.lastTimestamp

Look for: - FailedScheduling, ImagePullBackOff, ErrImagePull - DeadlineExceeded, OOMKilled (memory), permission errors, command not found 2) List all pods that were created for this Job

kubectl -n <ns> get pods -l job-name=misty-galago-jmj4x -o wide

There may be several (each retry creates a new pod). Pick the newest failed pod for deep inspection. 3) Check pod details and container termination status

kubectl -n <ns> describe pod <pod-name>

# Quick view of exit reasons and codes
kubectl -n <ns> get pod <pod-name> -o jsonpath='{range .status.containerStatuses[*]}{.name} {.state.terminated.reason} exit={.state.terminated.exitCode} last={.lastState.terminated.reason}{"\n"}{end}'

Common signals: - OOMKilled or exit 137 → memory limit too low - CrashLoopBackOff / ContainerCannotRun → bad entrypoint/command, permissions, or image arch mismatch - ImagePullBackOff / ErrImagePull → bad image tag or missing registry secret - FailedScheduling → not enough resources or node constraints 4) Get container logs (this is where Python exceptions or Prefect startup issues show)

# All containers in the pod
kubectl -n <ns> logs <pod-name> --all-containers --timestamps

# If a container restarted and you need the prior attempt
kubectl -n <ns> logs <pod-name> --all-containers --previous

If the pod never started (e.g., image pull error), logs will be empty—rely on describe/events in that case. 5) Check the command/args used by the pod (bad commands fail immediately)

kubectl -n <ns> get pod <pod-name> -o jsonpath='{.spec.containers[0].command}{" "}{.spec.containers[0].args}'

6) Optional: Observer logs (context around the “marking as crashed”)

kubectl -n <ns> get pods | grep observer
kubectl -n <ns> logs <observer-pod-name>

This confirms the observer’s decision but won’t show application errors inside your pod. Common fixes by symptom - OOMKilled: increase container memory request/limit in your Prefect Kubernetes work pool/job template; reduce data loaded in memory; chunk work. - ImagePullBackOff: verify image name/tag and imagePullSecrets; ensure the secret is in the same namespace. - CrashLoopBackOff / ContainerCannotRun: validate base image, entrypoint, file permissions, and that Python/env deps exist. If using a custom image, confirm Prefect is installed and the flow code is reachable. - FailedScheduling: adjust requests/limits, node selectors, tolerations, or quotas so the pod can land. - Network/API issues: if logs show connection issues to the Prefect API, ensure outbound egress and that PREFECT_API_URL and credentials are set. Where to look in Prefect - If the container started the Prefect engine, you’ll also see error logs on the Flow Run page in the UI. If the job failed before Prefect started (e.g., image pull or command error), the UI will have little/no logs—Kubernetes logs are authoritative. Docs - Prefect 3.x Kubernetes deployment guide: Prefect Kubernetes guide

- Kubernetes Jobs and backoff limit: Kubernetes Job backoff limit If you can share: -

kubectl -n <ns> describe job misty-galago-jmj4x

-

kubectl -n <ns> describe pod <one-failed-pod>

- The last 100 lines of

kubectl -n <ns> logs <that-pod> --all-containers

…I can help pinpoint the exact cause and the right fix. Do you know which namespace this job ran in?