AI workflows for Elastic
An AI workflow for Elastic is a documented sequence of steps that combines Elastic data, AI reasoning, human review, and an output. It can be run repeatedly by different team members because the trigger, data, prompt chain, and review step are written down.
Why use AI workflows with Elastic?
Documented workflows preserve institutional knowledge. They help every analyst or engineer handle a recurring Elastic task in the same way, which improves speed and consistency under pressure.
Who benefits from AI workflows?
Security operations teams use workflows for alert triage. SRE and DevOps teams use them for root-cause analysis and incident review. Platform teams use them for Elastic Cloud reporting and operational handoffs.
How do you get started?
Define the trigger, list the Elasticsearch data needed, write the ES|QL or search steps, define the prompt chain, add a human review checkpoint, and document the final output format.
How this differs from related AI resources
Workflows are process documentation. Agents may execute parts of a workflow, but the workflow is the reusable guide that explains what should happen and why.
Browse Elastic AI workflows
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Related Elastic resources
Continue with Elastic Security downloads, Elastic Observability examples, ES|QL examples, and runtime field examples.