Workflow Orchestration platforms coordinate code-defined, durable execution of multi-step workloads — data pipelines, ML training pipelines, microservice sagas, scheduled jobs, event-driven processes. Core capabilities include declarative or programmatic workflow definition, dependency management across tasks, retry and error handling, scheduling (cron, event-driven, dependency-triggered), distributed execution, state durability across process restarts, observability (task-level logs, lineage graphs), and backfill/replay. The category spans Python-first data orchestrators (Apache Airflow, Prefect, Dagster), microservice durable-execution engines (Temporal, AWS Step Functions), Kubernetes-native workflow engines (Argo Workflows), and ML-specific orchestrators (Metaflow, Kubeflow Pipelines).