The AIOps platform that resolves incidents — not just routes them.

An AIOps platform (Artificial Intelligence for IT Operations) is software that ingests telemetry from infrastructure, applications, and services, applies machine learning to detect anomalies and correlate signals, and acts on those insights to keep systems healthy. Modern AIOps platforms have shifted from passive "alert correlation" toward agentic execution — selecting and running remediation playbooks autonomously instead of waking on-call engineers.

Observability tools (Datadog, New Relic, Splunk) collect and visualize telemetry so humans can investigate. An AIOps platform acts on that telemetry — it correlates, prioritizes, and increasingly resolves. Observability ends at a dashboard. AIOps begins at the alert and, in agentic systems, ends at a verified fix.

Agentic AIOps is the current generation of AIOps platforms. Instead of merely clustering or correlating alerts, an agentic AIOps platform pairs anomaly detection with a library of remediation playbooks selected via RAG (retrieval-augmented generation), executes the selected playbook in production, verifies the outcome, and logs the result immutably. SentienGuard is an agentic AIOps platform: detect → select → execute → verify → log, end-to-end in under 90 seconds.

1) Multi-source telemetry ingestion (metrics, logs, traces, events). 2) Anomaly detection (statistical baselines + ML). 3) Event correlation and noise reduction. 4) Playbook or runbook selection (rules, ML, or RAG). 5) Autonomous or semi-autonomous remediation. 6) Verification and rollback. 7) Immutable audit logging for compliance (SOC 2, HIPAA, PCI-DSS, GDPR). 8) Integrations with the existing ITSM, on-call, and monitoring stack.

Most AIOps platforms stop at correlation — they cluster Datadog alerts into one ticket but still wake an engineer. SentienGuard executes the fix. A 50 MB agent watches infrastructure, anomaly scoring triggers RAG-based playbook selection (~165 ms, ~95% accuracy), the selected playbook runs in production, the result is verified, and an immutable audit log is written. 87% of incidents resolved without paging anyone.

Yes, when execution is gated by a confidence model. SentienGuard runs every new playbook in approval mode first — actions are previewed in Slack and a human approves or rejects. After successful approved runs, the playbook is promoted to autonomous. All actions, approved or autonomous, are logged immutably for audit.

A modern AIOps platform reduces alert fatigue by resolving incidents instead of routing them. SentienGuard removes 87% of routine pages — disk cleanup, pod restarts, connection pool resets, cert rotations — before a human ever sees them. Only novel or low-confidence incidents are escalated, restoring focus and sleep to the on-call rotation.

In the autonomous-resolution generation, end-to-end MTTR drops from hours to seconds. SentienGuard typical timing: anomaly detected (1–3 s), playbook selected via RAG (~165 ms), playbook executed (15–90 s depending on action), verification (5–30 s). 87% of routine production incidents resolve in under 90 seconds total.

It depends on the AIOps platform. SentienGuard replaces the on-call resolution layer that sits on top of Datadog/PagerDuty/New Relic. You can keep your existing monitoring (or move off it gradually) — SentienGuard ingests their telemetry, but the resolution work shifts from your humans to autonomous playbooks. Many teams cut monitoring spend from ~$18K/month to ~$2K/month by replacing premium tiers once SentienGuard is doing the resolution work.

Legacy AIOps platforms (BigPanda, Moogsoft, Splunk ITSI) price per data volume or per-event, which scales unpredictably. SentienGuard prices per node (flat, predictable). Free tier covers 3 nodes with full features, including audit logs. See /pricing for tiers.