Argo Workflows — Kubernetes-Native Workflow Engine

What it is

Argo Workflows is a CNCF-graduated workflow engine for orchestrating parallel jobs on Kubernetes. It models data pipelines, CI jobs, batch processing, and ML training as directed acyclic graphs (DAGs) where each step runs as a container.

Argo Workflows is built for platform engineers and data teams who need Kubernetes-native workflow orchestration with artifact passing, retry logic, and a visual UI.

How it saves time or tokens

Argo Workflows eliminates custom scripting for job orchestration. Instead of writing shell scripts that manage pod lifecycles, you declare workflows as YAML manifests. The engine handles parallelism, retries, timeouts, and artifact passing between steps. The web UI provides real-time visibility into running workflows.

How to use

1. Install the controller and UI:

kubectl create namespace argo
kubectl apply -n argo -f https://github.com/argoproj/argo-workflows/releases/latest/download/install.yaml

1. Install the CLI and submit a workflow:

brew install argo
argo submit -n argo --watch https://raw.githubusercontent.com/argoproj/argo-workflows/main/examples/hello-world.yaml

1. Access the UI:

kubectl -n argo port-forward svc/argo-server 2746:2746
# Open http://localhost:2746

Example

apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
  generateName: dag-pipeline-
spec:
  entrypoint: pipeline
  templates:
    - name: pipeline
      dag:
        tasks:
          - name: fetch-data
            template: run
            arguments:
              parameters: [{name: cmd, value: 'curl -o /tmp/data.csv https://example.com/data'}]
          - name: process
            template: run
            dependencies: [fetch-data]
            arguments:
              parameters: [{name: cmd, value: 'python process.py'}]
          - name: report
            template: run
            dependencies: [process]
            arguments:
              parameters: [{name: cmd, value: 'python report.py'}]
    - name: run
      inputs:
        parameters: [{name: cmd}]
      container:
        image: python:3.11
        command: [sh, -c]
        args: ['{{inputs.parameters.cmd}}']

Related on TokRepo

• DevOps tools — Kubernetes and infrastructure automation
• Automation tools — workflow orchestration resources

Common pitfalls

• Not configuring artifact storage (S3/GCS), which limits artifact passing between steps
• Running the argo-server without authentication in production environments
• Creating workflows with too many parallel steps without setting resource quotas