Kibana Workflows matter for observability because many incidents follow a known path: collect context, compare the incident window with a healthy baseline, open a case, notify the right team, and decide whether remediation is safe. Workflows make those steps explicit and repeatable.
Why this matters
Dashboards show symptoms, but they do not decide the next investigation step. A workflow can preserve the team’s runbook logic: what to query, what to summarize, where approval is required, and what evidence should be attached to the incident record. This is especially useful when combined with Agent Builder for bounded analysis.
When to use this idea
Use this article when designing AI workflows for Elastic Observability. It is a good fit for root cause analysis, human-in-the-loop incident response, OpenShift remediation, and operational workflows that should be auditable instead of improvised.
What the source content explains
The Observability Labs posts show three patterns: automated root cause analysis, human approval gates, and platform remediation. Together they explain how to keep automation useful without removing operator judgment. The workflow can gather evidence and prepare a case, while the human still decides whether to scale, restart, roll back, or investigate further.
Original source
Read more in the original content
The summary above highlights the practical value for Elastic Content Share readers. The original Elastic or GitHub source contains the full technical context, screenshots, implementation details, and updates from the maintainers.
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Sources
Read the related Elastic Observability Labs articles on automated root cause analysis, human-in-the-loop incident response, and OpenShift incident response.