Introduction
A system administrator named her OpenClaw agent "Reef" and gave it SSH access to her company's Kubernetes cluster. Every 15 minutes, Reef runs health checks: service availability, disk usage, error logs, certificate expiration, temp files. In six months, Reef has resolved three incidents that would have required on-call engineer pages at inconvenient hours. In each case, the problem was detected and fixed before any human was aware. "It's like having a junior sysadmin who never sleeps and never complains about weekend shifts."
Self-healing infrastructure is one of the most compelling OpenClaw use cases. The agent runs on a Heartbeat — every 15 or 30 minutes — and executes runbooks when it detects anomalies. It doesn't replace human judgment for complex incidents. It handles the routine stuff: pod restarts, log rotation, cert renewal. The stuff that would otherwise page you at 3 AM. For DevOps and SRE teams, Reef-style agents are becoming table stakes. The ROI is clear: fewer pages, faster resolution, happier engineers.
This post explains the Reef pattern in depth: how it works, what incidents it can handle, and how to replicate it. The pattern is generalizable. Reef happens to use Kubernetes, but the same approach applies to any infrastructure: VMs, databases, networking. The key ingredients are health checks, runbooks, scoped access, and a Heartbeat. OpenClaw provides the orchestration. You provide the domain knowledge in runbooks.
Every SRE has stories of 3 AM pages. Disk full. Pod crash loop. Certificate expiring. The kind of incident that has a standard fix — if you're awake to apply it. Reef is the part of you that never sleeps. It runs the same checks you would. It applies the same fixes. It reports what it did. You wake up to a Slack message: "Resolved. No action needed." The value isn't just the time saved. It's the sleep preserved. It's the weekend not ruined. That's worth more than any dollar amount.
Reef
Reef = OpenClaw + SSH + Kubernetes + runbooks. The agent runs scripts for checks; escalates to the LLM when an anomaly is detected. The LLM consults runbooks, executes the fix (kubectl delete pod, certbot renew, clear /tmp). Reports to Slack. Human reviews in the morning. Reef is the name of the agent; the pattern is the infrastructure agent pattern. See that guide for the full architecture.
The workflow is deterministic for checks, intelligent for remediation. The health check scripts run first. They gather metrics: pod status, disk usage, cert expiry, log volume. If everything is green, Reef reports "all good" to Slack and exits. If something is wrong, Reef loads the relevant runbook. The runbook is Markdown: "When disk > 90%, run log rotation, clear /tmp, alert if still above 85%." Reef executes the steps. It reports what it did. The human sees the Slack message in the morning: "Reef resolved disk full at 3:42 AM. Freed 20GB. Service unaffected." No page. No wake-up. Just a resolved incident.
The LLM's role is runbook interpretation. The runbooks are written in natural language with specific commands. The LLM maps "disk full" to the runbook, extracts the commands, executes them in order. It doesn't improvise. It follows the runbook. That's the boundary: Reef can do what the runbooks say. It cannot do what they don't say. For novel failures, Reef escalates. "Detected anomaly not covered by runbooks: [description]. Human review required." The human gets paged for that. Reef handles the routine. Humans handle the edge cases.
Incidents Resolved
Three examples from the Reef deployment: (1) Pod crash loop — Reef detected the pod restarting every 30 seconds, restarted it cleanly, service recovered. (2) Disk 95% full — Reef cleared logs, rotated, freed 20GB. Would have filled the disk within hours. (3) Certificate expiring in 10 days — Reef ran certbot, updated ingress. All between 2–5 AM. No human woken. No customer impact. The cost of each incident: zero. The value of avoiding a 3 AM page: immeasurable.
Incident 1: The pod crash loop was a classic Kubernetes failure mode. A bug in the application caused repeated crashes. The runbook said: "If pod in CrashLoopBackOff for > 5 minutes, delete pod to force clean restart." Reef detected the condition, executed the runbook, the new pod came up clean (the bug was intermittent), and the service recovered. A human would have done the same. Reef did it at 2 AM without waking anyone.
Incident 2: Disk full is the most common infrastructure incident. Logs accumulate. Temp files grow. The runbook: "If disk > 90%, rotate logs, clear /tmp, remove old backups." Reef executed. Freed 20GB. The disk would have hit 100% within hours. That would have caused service outages. Reef prevented it. The value: avoided outage, avoided 3 AM page, avoided weekend firefight.
Incident 3: Certificate expiration. Reef's health check includes cert expiry. It detected a cert expiring in 10 days. The runbook: "If cert < 14 days to expiry, run certbot renew, update ingress, restart if needed." Reef executed. Cert renewed. No downtime. A human would have done this during business hours. Reef did it proactively. The cert never got close to expiry.
The Pattern
Self-healing requires: runbooks (what to do for each failure mode), scoped access (Reef can restart pods, not delete namespaces), and alerting for failures that need human judgment. Reef handles routine; escalates the rest. The key is defining boundaries. Reef can fix a crash loop. Reef cannot decide whether to roll back a deployment. That's a human call.
Scoped access is critical. Reef has SSH access to the cluster and kubectl. But the RBAC is restricted. Reef can delete pods. Reef cannot delete namespaces, persistent volumes, or secrets. Reef can run certbot. Reef cannot modify firewall rules. The principle: give the agent the minimum access needed for runbook execution. If a runbook requires an action outside that scope, Reef escalates. The human does it. This prevents agent error from causing catastrophic damage. Reef can't accidentally delete production. It can only do what the runbooks say, and the runbooks only include safe, reversible actions.
Runbook quality determines success. Good runbooks are specific: exact commands, exact conditions, exact escalation criteria. Bad runbooks are vague: "try to fix it." Reef needs the former. Invest in runbook documentation. Start with the top 3-5 failure modes. Expand as you validate. The Nightly Brainstorm pattern complements this: proactive log analysis to identify issues before they become incidents. Reef handles remediation. Nightly Brainstorm handles prevention.
What You Need
To replicate: OpenClaw, SSH access to your cluster (or kubectl with appropriate RBAC), runbooks in Markdown, a Heartbeat configured for your check interval, and a Slack webhook for reports. The runbooks are stored in memory; the agent references them when it detects an anomaly. Start with one or two failure modes — e.g., disk full, pod crash — and expand as you validate. See Nightly Brainstorm for the complementary pattern: proactive log analysis.
Implementation timeline: Day 1 — Install OpenClaw, configure Heartbeat, write first runbook (e.g., disk full). Day 2 — Add health check scripts, test runbook execution. Day 3 — Add Slack reporting, add second runbook (e.g., pod crash). Week 2 — Add more runbooks, tune check interval. Month 1 — Evaluate. How many incidents did Reef resolve? How many did it escalate? Refine runbooks. Expand scope. The pattern scales. Start small. Prove value. Grow.
Wrapping Up
Self-healing servers are a flagship OpenClaw use case. Reef has become the reference implementation. The pattern is proven. The ROI is clear. See Reef pattern and Kubernetes for implementation. Your on-call engineers will thank you. Your 3 AM will stay quiet.