CloudThinker’s Frontier Investigation Agents find the why behind every Kubernetes issue and propose the fix. Your engineers approve, and stay on the loop instead of the night shift.
Works with EKS, GKE, AKS, and self-managed clusters. No standing credentials required.
Clusters scale faster than the humans who operate them. The signal grows; the on-call bandwidth does not.
Every namespace generates its own firehose. Your on-call rotation drowns in CrashLoopBackOff pages that a runbook could have closed hours ago.
Deep kubectl and cluster-internals knowledge lives with two or three engineers. When they are asleep or on PTO, MTTR balloons.
Rightsizing, rollout babysitting, and drift chasing eat the week. The platform team ships less because it is busy keeping the cluster alive.
The agents run the DARV loop (Detect, Analyze, Remediate, Verify) so a Kubernetes incident goes from page to proven fix. Your engineers approve the fix instead of running every step.
The agent clusters the noisy signal from Prometheus, Datadog, and Alertmanager into a single cluster incident (CrashLoopBackOff, OOMKilled, pending pods, a stuck rollout) instead of paging on every alert.
It walks the live cluster state and dependency graph (events, logs, resource limits, recent deploys) to find real root cause, not just statistical co-occurrence.
It proposes the matching runbook and runs it inside a sandbox with scoped, task-time credentials (roll back a bad deploy, right-size a limit, cordon a node) at the autonomy level you set for that environment.
It confirms the fix actually held (pods healthy, rollout complete, error rate back to baseline) and writes a tamper-evident record. If it did not hold, the agent escalates instead of declaring victory.
New in the DARV loop? Read the DARV loop explained.
The agent does not get the keys on day one. Every runbook starts at L1: investigate and propose. As it earns trust in your environment, you promote it to act-with-approval, then to autonomous within a defined guardrail. Trust is granted per runbook, per cluster, not all at once.
How graduated autonomy worksGiving an AI agent access to production Kubernetes is only safe when the platform is built for it. Every action is brokered, scoped, sandboxed, tokenized and logged, end to end.
No standing cluster access. The agent gets scoped, task-time credentials issued at the moment of the job and revoked after.
Learn moreThe credential lives in an isolated environment, never in the prompt. Every action is reversible and contained.
Learn moreSensitive data (secrets, PII in logs) is deterministically tokenized at egress before it ever reaches a model.
Learn moreEvery detection, decision, and action lands in an audit trail you can replay for compliance and post-mortems.
Learn moreRecurring cluster failures (CrashLoopBackOff, OOMKills, stuck rollouts) resolve faster when the agent has already found the why and proposed the fix.
Rightsizing, rollout babysitting, and drift chasing move off the humans and onto the agent, freeing the platform team to ship.
The agents do not sleep. Overnight cluster incidents get investigated and, where autonomy allows, resolved before the morning stand-up.
A Kubernetes AI agent is an autonomous system that investigates and acts on cluster problems (CrashLoopBackOff, OOMKills, failing rollouts, pending pods, noisy alerts) instead of only surfacing them on a dashboard. CloudThinker’s Frontier Investigation Agents find the why and propose the fix. They run the full DARV loop (Detect, Analyze, Remediate, Verify) under team policy, with brokered credentials, sandboxed execution, deterministic data tokenization, and a tamper-evident audit trail, so engineers stay on the loop rather than in the weeds.
A copilot suggests a command and waits for you to run it. CloudThinker closes the loop. It reasons over live cluster state, proposes or executes a remediation inside a sandbox with scoped, task-time credentials, and verifies that the fix actually held, then writes an audit record. You choose how much autonomy it has per environment, from notify-only up to fully autonomous within a guardrail.
Only within the autonomy level you set. On graduated autonomy, new actions start at L1 (the agent investigates and proposes). As a runbook earns trust you promote it to L2 (act with approval, via a scoped change request), then L3 and L4 (autonomous within a defined guardrail). Every action is reversible, scoped to brokered credentials, and logged in a tamper-evident audit.
Common day-2 cluster toil: CrashLoopBackOff and OOMKilled pods, failed or stuck deployments and rollbacks, pending pods and scheduling/node-pressure issues, misconfigured resource requests and limits, HPA and autoscaling anomalies, ingress and networking failures, RBAC and drift questions, and cost/rightsizing across namespaces. The agent correlates the signal, finds root cause, proposes the fix and verifies it.
Yes. CloudThinker connects to your observability and alerting stack (Prometheus, Grafana, Datadog, Alertmanager, PagerDuty) and your clusters (EKS, GKE, AKS, self-managed) through CloudThinker Connections. It ingests the signal you already produce rather than asking you to rip anything out.
It is when the platform is built for it. CloudThinker never holds standing cluster credentials. Access is brokered per task, scoped to the job, and lives inside a sandbox rather than a prompt. Sensitive data is deterministically tokenized at egress, and every action lands in a tamper-evident audit. That is the difference between an autonomous Kubernetes agent and an unsupervised script.
Connect a cluster. The agents find the why and propose the fix. Your engineers approve, under your policy, with a full audit trail.