argo workflows

Each step in an Argo workflow runs in its own container, providing isolation and reproducibility, with workflow definitions expressed in Kubernetes YAML that feel native to teams already operating Kubernetes infrastructure.

This container-per-step model makes Argo workflows highly scalable and reproducibleany step can be reproduced by running the same container image with the same inputs.

The workflow engine supports sophisticated orchestration patterns: parallel step execution, conditional branching, recursive loops, nested workflows as templates, artifact passing between steps via Kubernetes persistent volumes or S3-compatible storage, and retry policies with configurable backoff.

Argo Workflows is the foundation of Kubeflow Pipelines' execution layer and is widely used as the orchestration engine for ML training pipelines, data processing workflows, and CI/CD systems that require Kubernetes-native execution.

Platform engineering teams running ML infrastructure on Kubernetes, data engineering teams orchestrating ETL and data transformation pipelines, and ML researchers executing reproducible experiment workflows across GPU nodes use Argo Workflows for its Kubernetes-native model that eliminates the need to run a separate workflow orchestration service outside the Kubernetes cluster.

Its GitOps-compatible YAML-based workflow definitions integrate naturally with infrastructure-as-code practices, and the Argo Workflows UI provides visibility into workflow execution, step logs, and artifact outputs that makes debugging complex pipeline failures tractable.