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This runbook is Kubernetes-focused. Other orchestrators and container runtimes may expose different status fields, cgroup behavior, metrics, and probe semantics, so do not assume that every command or conclusion applies unchanged outside Kubernetes.
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1. Establish whether the delay is scheduling or initialization
Start with the pod’s state, node assignment, and events before changing resource values. A pod that is still pending has a different problem from a pod assigned to a node whose process takes too long to become ready.
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- Run
kubectl get pod <pod> -n <namespace> -o wide. Record the phase, node name, restart count, and IP. A blank node field usually means the scheduler has not assigned the pod. - Run
kubectl describe pod <pod> -n <namespace>and inspect the Events section. An event such asFailedSchedulingwith insufficient CPU or memory indicates that no eligible node currently satisfies the pod’s requests or placement rules. Kubernetes creates these events when it cannot find a node where a pod fits. See Kubernetes resource management documentation. - List recent events in time order with
kubectl get events -n <namespace> --sort-by=.lastTimestamp. Look for taints, affinity or anti-affinity failures, exhausted quotas, preemption, image-pull delays, volume attachment, and repeated probe failures.
Do not interpret a scheduling failure as slow application startup. Requests participate in scheduling; the container cannot begin initialization until a node is selected and the image and volumes are available.
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Build a startup timeline after scheduling
For a pod that has a node assignment, capture timestamps for pod creation, scheduling, image pull and container start (when available), process start, application initialization milestones, readiness success, and the first successful agent task. Kubernetes does not provide a universal timing decomposition for these phases, so use your own pod events, container logs, application metrics, and tracing. Compare a slow start with a healthy start on a similar node and image before assigning causality.
Useful evidence includes:
- Container logs with timestamps, including the interval before the first application log line.
- Image-pull duration and image size from node or registry telemetry.
- Time spent loading models, tokenizers, tool manifests, configuration, migrations, or caches.
- Readiness and startup-probe transition times.
- Whether the node was handling a rollout or other high-load workloads at the same time.
2. Separate requests, limits, usage, and pressure
Resource requests and limits answer different questions. Kubernetes uses requests when placing a pod. CPU and memory limits are enforced by the kubelet and container runtime through Linux control groups (cgroups). Actual usage is an observation, not a configuration value. A pod can have a low current average while still suffering short bursts, throttling, reclaim, or I/O waits.
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Inspect the Deployment, StatefulSet, Job, or rendered pod specification:
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kubectl get deploy <name> -n <namespace> -o yaml
kubectl get pod <pod> -n <namespace> -o jsonpath='{.spec.containers[*].resources}'Record each container’s CPU and memory request and limit, including sidecars and ephemeral containers. Check namespace quotas and node allocatable capacity as well as the sum of requests from competing pods.
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| Observation | What it can mean | Evidence to collect |
|---|---|---|
Pending with FailedScheduling |
Insufficient allocatable capacity, a placement constraint, taint, quota, or competing requests | Pod events, node allocatable values, requests of other pods, affinity and taint rules |
| Scheduled; CPU usage near a limit or visible throttling | The container may be constrained by its CPU limit or competing runnable work | Container CPU usage, throttling counters from your metrics system, node CPU pressure, initialization latency |
| Memory usage approaching a limit, restarts, or OOM events | Memory pressure or an enforced cgroup limit | Container status reason, kernel or kubelet events, working-set and RSS trends, limit and request values |
| Low average CPU and memory but long waits | I/O wait, lock contention, short bursts hidden by sampling, probe overhead, or device initialization | PSI where available, disk metrics, fine-grained application timings, probe logs |
A memory-backed emptyDir counts against a pod’s memory limit. If no memory limit is set, that volume can consume available node memory, so inspect volume configuration as well as process usage. The Kubernetes resource-management documentation explains this interaction at kubernetes.io/docs/concepts/configuration/manage-resources-containers/.
Use pressure signals instead of utilization alone
High utilization is not proof of contention, and low utilization does not disprove it. Linux Pressure Stall Information (PSI) measures the time tasks are stalled for CPU, memory, or I/O. Kubernetes documents node-, pod-, and container-level PSI through kubelet and cAdvisor metrics when the kernel’s PSI facility is enabled. Its Summary API examples show CPU, memory, and I/O pressure for a named container; availability depends on the cluster version, kernel, and endpoint configuration. Consult Kubernetes system metrics documentation and verify that your endpoint actually exposes PSI before building alerts around it.
Correlate pressure windows with process-start timestamps, probe timeouts, restarts, and task latency. A CPU-pressure spike during model loading supports a contention hypothesis; a quiet node with a slow model deserialization path points elsewhere.
3. Investigate CPU, memory, and I/O contention separately
CPU: distinguish throttling from insufficient scheduling capacity
CPU requests influence placement, while a CPU limit can constrain execution after placement. Check per-container usage and throttling over the startup interval, not only a five-minute average. Compare the pod’s limit with the node’s concurrent runnable work and observe whether probe processes run at the same time as initialization. Raising a limit can help only when the limit is the bottleneck and the node has spare capacity; it can also increase competition for CPU or make scheduling harder.
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Memory: look for reclaim, eviction, and cgroup termination
Review restart reasons and events for out-of-memory termination or eviction. Compare working-set and resident-memory growth with the configured limit, and include model files, decompression buffers, caches, shared memory, and memory-backed temporary volumes. A larger request may improve placement on a less pressured node, whereas a larger limit changes the container’s enforcement boundary; neither is a universal remedy.
I/O: measure storage work during initialization
Model loading, image extraction, cache population, log bursts, and volume mounts can make a pod slow even when CPU and memory appear adequate. Google Kubernetes Engine guidance identifies high disk I/O as a cause of delayed container startup, application initialization, and probe timeouts. Inspect node disk latency and throughput, container filesystem activity, volume performance, and application log timestamps. Attribute this guidance to GKE rather than treating it as identical behavior on every Kubernetes distribution: GKE CrashLoopBackOff troubleshooting.
4. Make probes match the real initialization path
Probes are control logic, not merely monitoring. A startup probe is designed for containers that need time to load large data, configuration, or migrations. Kubernetes states: “For containers that need to work on loading large data, configuration files, or migrations during startup, consider using a startup probe.” When a startup probe is configured, liveness and readiness probes do not run until startup succeeds.
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| Probe | Primary role | Failure consequence |
|---|---|---|
| Startup | Gate the initial boot period | Repeated failure can restart the container after the configured failure threshold |
| Readiness | Control whether the pod receives traffic | Pod is removed from service endpoints while unready; the container is not necessarily restarted |
| Liveness | Detect a running container that should be restarted | Failure can trigger a restart, repeating expensive initialization |
A timeout while the process is under CPU or I/O pressure shows that the probe could not obtain an answer in time; it does not by itself prove an irrecoverable application defect. Conversely, an incorrect probe can create a restart loop and add more model loads, disk reads, and CPU work.
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Tune the complete probe policy
- Measure normal and worst-case initialization duration under realistic node and image conditions.
- Set the startup probe’s delay, period, timeout, and failure threshold to cover that observed interval with margin, without hiding a genuinely stuck process.
- Keep readiness tied to the condition required to serve agent requests, such as model availability and tool registration.
- Use liveness only for a failure mode that a restart can recover. Do not use it as a proxy for readiness.
- Check probe implementation cost. Kubernetes warns that exec probes create or fork a process for every check; frequent exec checks across many pods can add measurable CPU overhead. An incorrect readiness implementation can also allow an ever-growing process count and resource starvation. Prefer a lightweight endpoint when the application can expose one, and review interval and timeout together with density.
Probe semantics and configuration details are documented at Kubernetes Liveness, Readiness, and Startup Probes.
5. Interpret autoscaling signals during startup
Horizontal Pod Autoscaler (HPA) behavior can make startup appear inconsistent. Kubernetes documents a default CPU initialization period of five minutes. Early CPU samples are ignored unless the pod is Ready and the sample was taken entirely while Ready. This prevents a model-loading spike from immediately driving scaling decisions, but five minutes is a default, not a universal ideal.
Set startup and readiness behavior so the pod becomes Ready only after the startup CPU spike has subsided, then compare the configured initialization period with measured startup duration. If initialization regularly exceeds the default, tune the period and readiness policy based on evidence. If it finishes sooner, an unnecessarily long period can delay useful scaling. See Kubernetes Horizontal Pod Autoscaling.
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6. Check rollout surges and cluster-wide timing
A healthy pod can start slowly when many replicas start simultaneously. GKE troubleshooting guidance notes that a large deployment burst can overwhelm cluster resources and delay startup. During the incident window, correlate pod creation times with rollout strategy, pending pods, node CPU and disk pressure, image-pull traffic, and registry latency.
Possible responses depend on the evidence:
- Stagger the rollout or reduce the deployment’s temporary surge when simultaneous image pulls and initialization saturate nodes.
- Adjust node count or node shape when scheduling capacity is genuinely insufficient.
- Spread replicas across failure domains or pools when one node group is saturated.
- Pre-pull or cache large images only when image transfer is demonstrated as the delay and the operational cost is acceptable.
These are conditional operational choices, not a universal instruction to add nodes or increase replicas. The GKE guidance is available at cloud.google.com/kubernetes-engine/docs/troubleshooting/crashloopbackoff-events.
7. Branch into GPU diagnostics only for GPU workloads
If the agent requests GPUs, verify device visibility and library initialization separately from CPU and memory investigation. Check the pod’s resource request, runtime configuration, driver compatibility, device-plugin state, and the devices visible inside the container. A CPU-only agent does not acquire an NVIDIA-specific failure merely because it runs in a container.
NVIDIA documents a class of NVIDIA Container Runtime Hook and cgroup-update problems that can remove GPU access. One reported symptom is Failed to initialize NVML: Unknown Error. For the affected configuration, NVIDIA says deleting and restarting the affected container restores access; CDI or explicit device-node configuration are listed as mitigations. Apply those steps only after confirming that the runtime, driver, cgroup, and device configuration match the documented failure. See NVIDIA Container Toolkit troubleshooting.
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8. Handle agent-specific latency claims carefully
The arXiv preprint AgentCgroup: Understanding and Controlling OS Resources of AI Agents reports that “OS-level execution (tool calls, container and agent initialization) accounts for 56-74% of end-to-end task latency” in its abstract. The same paper’s analysis section reports OS-level execution at 55-60% and LLM reasoning at 40-45%. The ranges are internally inconsistent, and the work is a 2026 preprint rather than an operations standard.
Use the result only as a motivation to measure initialization and tool-call overhead in your own system. If you cite the abstract statistic, identify it exactly as: “56-74% of end-to-end task latency — AgentCgroup authors, 2026 (arXiv preprint; abstract; inconsistent with the analysis section’s 55-60% OS-level figure).” Do not generalize either range to every agent, model, runtime, or deployment. The paper is available at arxiv.org/abs/2602.09345.
9. Choose remediation by symptom and layer
Make one evidence-backed change at a time where possible, then compare the same timeline and pressure signals on an equivalent start.
Quick Recap
| Observed symptom | Layer addressed | Evidence that justifies the change | Trade-off or scope |
|---|---|---|---|
| Pending pod with insufficient CPU or memory | Scheduling capacity or placement | FailedScheduling, requests, allocatable capacity, taints, affinity, quota |
Adding or resizing nodes costs capacity; larger requests can make placement harder |
| Scheduled pod reaches a CPU limit while initializing | CPU throttling or pressure | Limit, throttling counters, CPU PSI, correlated startup delay | A higher limit can increase node contention and affect scheduling |
| Memory limit or OOM termination | Memory enforcement | OOM/restart reason, memory trend, cgroup limit, volume and cache usage | A higher limit consumes more node memory and does not fix a leak |
| High disk latency during model or image loading | I/O | Node and volume latency, throughput, startup log timestamps | Faster storage or caching may increase cost and cache-management work |
| Probe timeouts during otherwise normal boot | Probe policy | Probe events aligned with measured initialization and pressure | More tolerance can delay detection of a genuinely stuck container |
| Restart loop after a failed health check | Probe implementation or application failure | Container logs, probe command cost, failure threshold, restart timing | Changing liveness without diagnosis can hide faults or amplify work |
| Startup degradation only during deployments | Rollout surge and shared capacity | Replica creation timestamps, pending pods, node and registry load | Slower rollouts trade deployment speed for lower peak pressure |
| GPU library or NVML initialization error | GPU device access | Device request, in-container device listing, runtime and driver logs | Fix is runtime- and driver-specific; irrelevant to CPU-only agents |
10. A repeatable incident checklist
- Capture pod phase, node assignment, restart count, events, and rollout timing.
- Classify the delay as unscheduled, image or volume startup, process initialization, probe gating, or post-start resource contention.
- Record requests, limits, node allocatable resources, quotas, sidecars, and memory-backed temporary volumes.
- Compare usage with limits and inspect restarts, throttling, memory pressure, disk latency, and PSI when the kernel and kubelet expose it.
- Align probe failures with initialization milestones; check startup, readiness, and liveness settings together.
- Account for HPA’s startup CPU handling rather than treating an early spike as steady-state demand.
- Check rollout surges and concurrent image, volume, and model activity.
- Run the GPU branch only when the pod requests a GPU or logs a device/library failure.
- Change the smallest configuration layer supported by the evidence, then repeat the same measurements.
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