Elastic Workflows takes a plain-text prompt and generates YAML you can inspect, version and run against your Elasticsearch data. Now GA, with human-in-the-loop workflows in Slack, parallel execution, and 10 new connectors. Elastic Workflows now writes its own YAML. You type what you want automated in plain language, the Elastic AI Agent generates a complete workflow against a typed schema, and nothing runs until you've read it. YAML is why this works: it gives the model a constrained, well-typed target, so what comes back is actual building blocks you can edit and run.
Natural language authoring is GA and on by default: describe an automation, and the Elastic AI Agent writes the workflow, you review and run it. Three new experimental previews (behind an advanced setting): a visual mode that renders a workflow as a graph, human-in-the-loop steps that reach people in Slack for input or approval, and parallel execution. More to build on: new connectors, event triggers that react to Cases activity, token metering for AI steps, and a queue strategy for concurrency. Workflows is the automation engine built into the Elastic platform. It reached general availability in 9.
4, enabled by default and running against your Elasticsearch data with the connectors and access controls you already have. This post walks through what 9. YAML is the authoring language for Elastic Workflows because it's declarative, version-controllable, diffable, and portable across environments. It reads the same in a pull request as it does in the editor. Large language models (LLMs) are very good at generating structured, well-typed content, and a workflow language is close to an ideal target for that. Ask for prose and a model can wander.
Ask for a workflow against a typed schema, with named step types and validated inputs, and there is a right shape for the answer. 5 that bet pays off, and it is GA. Inside the workflow editor, you write what you want in plain language: When a detection alert fires for a host, pull the last 24 hours of related logs, ask the AI step to summarize what happened, and post the summary to the on-call Slack channel. The Elastic AI Agent generates the workflow: the trigger, the Elasticsearch query, the AI summarize step, the Slack step, wired together with the right inputs and outputs.