Cluster events, handled.
Not just alerted.
An AI agent that monitors Kubernetes events in real time, correlates failures with recent changes, executes runbooks, and assembles incident briefs — before your on-call engineer even opens a terminal.
Why on-call engineers waste 20 minutes before they can start fixing
Pod failures surface as noise, not signal
CrashLoopBackOff in 3 services, 14 Slack notifications, 1 PagerDuty alert that woke someone up. Before your on-call engineer can do anything, they need context that isn't in any of those messages.
Resource waste is invisible until capacity runs out
Over-requested CPU and memory that's never used. Pods running at 8% utilisation. You find out when something fails to schedule — not when the waste starts accumulating.
Deployment failures require a post-mortem to understand
A rollout goes wrong. The rollback gets triggered manually. Then begins the archaeology: which image, which config change, which namespace, which time. All the information exists — it just isn't assembled anywhere.
Runbooks live in Confluence and get run inconsistently
Your runbooks are good. But at 3am, engineers skip steps, improvise, or miss them entirely because they're buried three pages deep. The runbook might as well not exist.
From event to remediation — or a complete brief
Continuous cluster event ingestion
The agent subscribes to Kubernetes events across your clusters — pod lifecycle events, node pressure conditions, resource quota breaches, deployment status changes. Not polling. Event-driven.
Event classified and correlated
Events are classified by type and correlated with recent changes — recent deployments, config map updates, scaling events. A CrashLoopBackOff gets a different response if it started 10 minutes after a deployment than if it started at 3am with no recent changes.
Runbook executed or escalation assembled
Known failure patterns trigger runbooks automatically — restart a stuck job, cordon a node under pressure, roll back a failed deployment. Unknown patterns escalate with a structured brief: what happened, what changed, what the agent tried, what it recommends.
Change record written
Every agent action — restart, cordon, scale, rollback — written to an immutable change log with the triggering event, the runbook invoked, and the outcome. Your post-mortem starts with a complete record.
What the agent monitors and manages
Pod health monitoring
CrashLoopBackOff, OOMKilled, Pending — detected immediately and classified by failure type before alerting.
Node pressure detection
Memory, disk, and CPU pressure conditions monitored per node. Cordoning and drain initiated per runbook before workloads are evicted.
Resource quota management
Namespace resource quotas monitored against actuals. Over-request patterns surfaced with utilisation data to back right-sizing recommendations.
Deployment rollout watch
Deployment and StatefulSet rollouts monitored in real time. Failed rollouts trigger automatic rollback or escalation based on your policy.
Auto-scaling triggers
HPA and KEDA metrics tracked. Scaling events logged with the metric that triggered them and the outcome.
Log aggregation for incidents
On alert, the agent pulls recent logs from affected pods and surfaces the most relevant lines in the incident brief — not a 10k line dump.
Namespace isolation checks
Network policy compliance and RBAC policy drift monitored per namespace. Overly permissive configurations flagged.
Multi-cluster support
Single agent instance across multiple clusters. Findings and change records unified per environment tag.
Connects to your clusters and incident stack
Alerts route to PagerDuty or Slack. Runbook executions log back to Jira or ServiceNow. Your existing on-call workflow stays intact.
View all integrations →
Kubernetes
AWS
Azure
GCP
PagerDuty
Slack
Jira
ServiceNowSee it respond to a real cluster event
We'll simulate a failure against a test cluster, walk through the classification, runbook execution, and change record it produces — end to end.