Post-AIOps Recovery: An Emerging Need

AI-powered automation promises efficiency, but errors can compromise the entire infrastructure. Cohesity, ServiceNow, and Datadog have announced a collaboration to address this issue: a suite of tools focused on restoring systems after incorrect interventions by AIOps systems.

The suite aims to identify files and data corrupted as a result of automation errors, and then restore the systems to an operational state considered reliable. This approach recognizes the growing need for effective rollback mechanisms as companies increasingly rely on AI to manage their infrastructures.

For those evaluating on-premise deployments, there are trade-offs to consider carefully. AI-RADAR offers analytical frameworks on /llm-onpremise to evaluate these implications.