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Argo Workflows

Argo Workflows is a container-native workflow engine for Kubernetes that orchestrates parallel jobs as directed acyclic graphs. Each step runs in its own container, making it suitable for CI/CD pipelines, data processing, and machine learning workflows.

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Don't Panic — Argo Workflows

Argo Workflows is a way to describe a finite piece of container work as a graph that Kubernetes can run. Before this, you might have assembled shell scripts, CI jobs, cron entries, and hopeful notes about what must happen after what. Those tools can run work. They are less interested in remembering the route through a pile of dependent work. Argo is interested in the route, which is why it keeps a Workflow resource and its changing status together. The paperwork has volunteered to become useful.

The key idea is a workflow controller, a Kubernetes reconciler that reads the graph and starts the next eligible task. Your Workflow names an entrypoint and templates. The controller sees which node may run, Kubernetes schedules its Pod, and the status records the outcome. This is not a long-lived service wearing a graph-shaped hat. It is an execution record for work that finishes, fails, waits, or needs cleaning up.

The other idea is that the graph has two good costumes. Steps tell a stage story: one group, then the next, with some parallel tasks inside a group. A DAG, a directed acyclic graph of dependencies, tells the controller what each task actually needs before it can run. The second form often exposes more parallel work because it does not make unrelated tasks wait politely in a stage queue.

Data also arrives in three costumes, because apparently one was not enough. A parameter is a small string such as an identifier. An artifact is a file or directory moved through configured storage. A volume is a mounted filesystem. Large data normally stays in its own system while tasks pass a reference. Sending it through every node gives storage and failure modes more opportunities to introduce themselves.

The surprise is that retries are not apologies. A task can alter an external system and vanish before it reports success. Another attempt may therefore repeat a real change. Timeouts, retry limits, exit handlers, and mutexes help govern the graph, but idempotent task behavior is what keeps a rerun from becoming an accidental sequel.

Read the Intro when you need the full component map and security boundary. Use Slides to compare Steps, DAGs, data paths, and concurrency layers at a glance. Keep the Cheatsheet nearby when writing a manifest or choosing a control. Then use the Reference tab for the official first-run path and operational details. The graph declares intent; the controller advances it; Kubernetes runs the Pods; status tells you which part has started complaining.

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