What 'self-healing' actually means
A self-healing endpoint is one where the platform can detect a problem, work out the cause, and apply the remediation, closing the loop without a technician hand-running steps. The goal is to kill alert fatigue: instead of a pager that says "disk 92% full" at 3am, you get a proposed fix waiting for one click.
The catch with most "automation" is that it is blind: it fires a script on a trigger and hopes. Vertex grounds every action in evidence first, so the fix matches the actual fault.
Detect → diagnose → remediate → verify
The same agentic loop runs whether you asked a question or an alert fired:
- Detect. Proactive alerts and metric anomalies (≥2σ off baseline) surface problems before users file a ticket.
- Diagnose. Detectors pinpoint the cause: failing disk, slow boot, problem driver, stopped service, memory pressure, persistence-risk scheduled task, with the evidence attached.
- Remediate. The Co-Pilot proposes a scoped fix: restart the service, kill the runaway process, refresh inventory, run an allow-listed probe.
- Verify. After it runs, the signal is re-read to confirm the issue is gone, not just attempted.
Approval gates keep humans in charge
Self-healing should not mean uncontrolled. Every change is gated, role-based, and audited. You decide what auto-resolves and what waits for approval. Protected processes (like core system services) can never be suggested for termination. The result is automation you can actually trust in production.
Build self-healing automations visually
Turn a recurring fix into a standing rule on the endpoint automation canvas: a trigger ("service Spooler stopped") wired to an action ("restart Spooler") behind an approval gate. Or just describe it in plain English and let the AI build the workflow for you.
Frequently asked questions
Will it reboot or change machines without asking?
No. Remediation is gated behind approval by default, and every action is audited. You choose what is allowed to self-resolve.
What problems can it detect and heal?
Stopped services, failing disks (SMART), slow boots, problem drivers, runaway/CPU-hog processes, memory and disk pressure, failing or suspicious scheduled tasks, and stale Group Policy, among others.
Does it work across the whole fleet?
Yes. Fixes can be scoped to one endpoint or fanned out fleet-wide, still behind the same gates and audit trail.
Does the AI train on our data?
No. Telemetry is used to diagnose and fix your endpoints, not to train models.