A managed IT-operations provider (named reference under NDA) had reliable detection and an entirely manual response. We removed the toil between an alert and its resolution, with no human in the middle for defined incident categories.
The provider's operations team was running a growing IT estate with a growing alert volume. Detection was not the issue — the monitoring stack surfaced incidents reliably. The issue was everything that happened after detection.
Every alert triggered the same human sequence: triage, determine severity, consult the runbook, execute the remediation, verify resolution, update the ticket. For most incidents — disk pressure, service restarts, configuration drift, routine escalations to a more senior engineer — it was the identical sequence every time. Known problem, known fix, manual execution.
The toil compounded with the environment. As the estate grew, incidents grew with it, and the team spent more time executing known fixes than on the engineering work that actually needed their judgment.
The core problem: a predictable, rule-bound remediation workflow being executed by hand, at scale.
We deployed an autonomous-operations workflow built on TurfAI, running on the Elastic and Red Hat stack the team already operated. It was an augmentation, not a rip-and-replace.
This sat on our deeper infrastructure and observability practice — the discipline behind the "zero manual runbooks" posture: infrastructure that runs itself within guardrails the customer defines. We owned the outcome, operated on the customer's existing stack, and stayed accountable through go-live. The engagement was owned by Platform Delivery & Operations, with TurfAI as the platform underneath it — one named lead answerable for how it was built, and no daylight between that and what had been committed.
Alert noise came down at ingestion through deduplication and correlation, so engineers see signals rather than volume. Engineer time was reallocated from runbook execution to judgment work.
The design decision worth copying is the escalation path. The incidents that fall outside the playbooks are the ones that need a person, so the system's job on those is not to try, but to hand over a completed investigation: what happened, what was attempted, what the evidence shows, what it recommends. The engineer starts from a position rather than from a blank page.
Stack — TurfAI · Elastic · Red Hat · automated playbook execution within customer-defined guardrails · full decision audit trail
Tell us the alert volume and which categories always end the same way. We will tell you which of them can safely run themselves, and which should not.
