Modern teams rely heavily on Kubernetes to run applications at scale, but running applications is only part of the story. Real production environments also require automation for tasks like data processing, infrastructure jobs, testing pipelines, machine learning workflows, and scheduled operations. This is where many Kubernetes teams start to struggle.
Most automation today still depends on external tools, custom scripts, or CI/CD systems that were not designed to work natively with Kubernetes. As workflows grow more complex, these approaches become harder to manage, less reliable, and difficult to scale. Debugging failures often means jumping between tools, logs, and dashboards, slowing teams down.
Argo Workflows was created to solve this exact problem. It brings automation directly into Kubernetes and treats workflows as first class Kubernetes resources. Instead of running automation outside the cluster, Argo allows teams to define, execute, and monitor workflows inside Kubernetes itself.
This approach aligns perfectly with how cloud native platforms are built today. Everything runs as containers, everything is declarative, and everything is version controlled. Argo Workflows fits naturally into this model and enables teams to automate complex processes in a consistent and repeatable way.
In this guide, you will learn what Argo Workflows is, why it exists, how it works, and how teams use it for real world Kubernetes automation in 2025.
What Is Argo Workflows
Argo Workflows is a Kubernetes native automation engine designed for teams that already run workloads on Kubernetes and want to automate tasks in a clean and scalable way. Instead of relying on external automation servers or complex scripting systems, Argo allows teams to define workflows that run directly inside the Kubernetes cluster.
For Atmosly users, the easiest way to think about Argo Workflows is this. It lets you describe a process as a sequence of container based steps, where each step runs as a Kubernetes pod. Kubernetes takes care of scheduling and execution, while Argo controls the order of steps, dependencies, retries, and failures.
Argo Workflows is not used to deploy applications or manage long running services. Its focus is automation. This includes tasks such as running batch jobs, processing data, executing tests, performing infrastructure operations, or orchestrating machine learning pipelines. Each workflow starts, runs its steps, and then completes.
Because Argo Workflows runs inside Kubernetes, it fits naturally into modern platform engineering setups. It uses the same namespaces, permissions, secrets, and observability tools as the rest of the cluster. This keeps automation consistent with how applications are already managed.
For teams using Atmosly to operate Kubernetes environments, Argo Workflows becomes a powerful automation layer. Workflows stay Kubernetes native, visible, and traceable, making it easier to manage complex automation alongside application deployments without introducing new operational silos.
Why Argo Workflows Exists and the Real Problem It Solves
As Kubernetes adoption grows, teams quickly realize that running applications is only one part of operating a platform. Every environment also needs automation for recurring tasks such as data processing, testing, maintenance jobs, infrastructure actions, and scheduled workflows. The problem is that most teams try to solve this automation outside Kubernetes.
Traditional approaches rely on shell scripts, cron jobs, or external CI CD tools that trigger tasks remotely. Over time, this creates several issues. Automation logic becomes scattered across different systems. Debugging failures requires jumping between tools. Scaling workflows means adding more infrastructure. Most importantly, automation stops behaving like the rest of the Kubernetes platform.
Argo Workflows exists to bring automation back into Kubernetes itself.
Instead of running tasks on external servers, Argo executes workflows as Kubernetes pods. This means automation follows the same rules as applications. It runs in namespaces, respects resource limits, uses Kubernetes security policies, and benefits from built in scheduling and resilience.
Another major problem Argo solves is complexity. As workflows grow, scripts become hard to read and pipelines turn fragile. Argo introduces a structured way to define steps, dependencies, retries, and conditions in a declarative format. This makes automation easier to reason about, review, and version control.
For platform teams, this approach reduces operational overhead. There is no separate automation platform to manage. Everything lives inside Kubernetes. For teams using Atmosly, this model aligns perfectly with a centralized platform view where automation, workloads, and environments are managed together.
Argo Workflows exists because Kubernetes needed a native way to automate complex processes reliably at scale.
Core Concepts of Argo Workflows
To use Argo Workflows effectively, it is important to understand a few core concepts. These concepts are simple once you see how they fit together, and they form the foundation of how Argo automates work inside Kubernetes.
A workflow is the main object in Argo. It represents the full automation process you want to run from start to finish. A workflow defines what tasks should run, in what order, and under which conditions. Each workflow is submitted to Kubernetes and managed like any other resource.
Templates define how individual tasks are executed. A template usually describes a container image to run along with commands, inputs, and outputs. Templates are reusable, which allows teams to standardize common tasks across multiple workflows.
Steps and DAGs control execution order. Steps are used when tasks must run in a specific sequence. DAGs are used when tasks can run in parallel with defined dependencies. This flexibility allows Argo to model both simple and complex automation patterns.
Parameters and artifacts are how data moves between tasks. Parameters pass small values such as strings or numbers. Artifacts are used for larger outputs like files, datasets, or model results. This makes Argo suitable for data heavy and compute intensive workflows.
At the core of everything is the Argo controller. It watches workflows, creates pods, tracks progress, handles retries, and updates status. Kubernetes handles the execution, while Argo handles orchestration.
For teams using Atmosly, understanding these concepts makes it easier to design workflows that are reliable, observable, and easy to operate alongside application workloads.
How Argo Workflows Works Behind the Scenes
When a workflow is submitted to Argo Workflows, the process starts entirely inside the Kubernetes cluster. The workflow definition is created as a Kubernetes resource, just like a Deployment or a Service. From that moment on, Kubernetes and Argo work together to execute it.
The Argo controller constantly watches the cluster for new workflows. When it detects a workflow, it reads the defined templates, steps, or DAG structure to understand what needs to run and in what order. Based on this definition, Argo creates Kubernetes pods for each step in the workflow. Each pod runs a container that performs a specific task.
Kubernetes handles where and how these pods run. It schedules them on available nodes, enforces resource limits, and restarts them if needed. Argo focuses only on orchestration. It tracks which steps have completed, which are running, and which are waiting for dependencies.
As each step finishes, Argo updates the workflow status and decides what should run next. If a step fails, Argo can retry it based on the workflow configuration or stop execution entirely. All execution details are recorded, which makes workflows easy to inspect and debug.
Artifacts and parameters are passed between steps using Kubernetes storage and metadata. This allows complex workflows to share results without relying on external systems.
This execution model is powerful because it stays fully Kubernetes native. There are no external runners or hidden processes. For teams using Atmosly, this means Argo workflows are visible, traceable, and manageable alongside application workloads, making automation easier to operate at scale while staying well within platform boundaries.
Installing Argo Workflows on Kubernetes
Before using Argo Workflows, it must be installed inside a Kubernetes cluster. The installation process is straightforward and does not require additional infrastructure outside the cluster, which is one of the reasons teams adopt Argo for Kubernetes native automation.
Argo Workflows runs using a controller that manages workflow execution. This controller is installed into a dedicated namespace, commonly called argo. The recommended approach is to install Argo using the official Kubernetes manifests provided by the Argo project.
Once the manifests are applied, Kubernetes creates the required resources such as the Argo controller, service accounts, roles, and services. From this point, the cluster is ready to accept workflow submissions.
After installation, it is important to verify that the Argo controller pods are running correctly. This confirms that Argo is active and able to manage workflows. Teams can also install the Argo CLI locally to submit and manage workflows more easily.
Basic security should be considered early. Argo uses Kubernetes RBAC, so access to create and run workflows can be restricted by namespace or role. This ensures that only authorized users or systems can trigger automation.
For teams operating Kubernetes at scale, installing Argo Workflows keeps automation close to workloads and avoids maintaining separate execution systems. When combined with a platform like Atmosly, Argo installations become easier to monitor and manage as part of the broader Kubernetes environment, ensuring automation remains reliable and observable as usage grows.
Your First Argo Workflow
The best way to understand Argo Workflows is to run a very simple workflow and see how it behaves inside Kubernetes. This example focuses on clarity and shows how Argo executes steps as containers without adding unnecessary complexity.
A basic Argo workflow is defined using a Kubernetes manifest. Below is a minimal example that runs a single step and prints a message.
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: hello-argo-
spec:
entrypoint: hello
templates:
- name: hello
container:
image: alpine
command: ["sh", "-c"]
args: ["echo Hello from Argo Workflows"]
This workflow defines one template named hello. The template runs a lightweight container and executes a simple command. When the workflow is submitted, Argo creates a pod for this step and runs it inside the cluster.
To run the workflow, teams submit the file to Kubernetes using the Argo CLI or kubectl. Once submitted, the Argo controller detects the workflow and starts execution immediately.
After execution begins, Argo tracks the status of the workflow and updates it as the pod runs and completes. Teams can view logs, execution status, and outputs directly from the cluster. This visibility makes it easy to debug and understand what happened during each step.
Once the workflow finishes, the resources are cleaned up automatically unless configured otherwise. This makes Argo well suited for short lived automation tasks.
For teams using Atmosly, workflows like this become easier to observe and manage at scale. Execution status, failures, and resource usage remain visible alongside application workloads, helping teams operate Kubernetes automation with confidence.
Common Argo Workflow Patterns Used in Production
In production Kubernetes environments, Argo Workflows is rarely used for single step tasks. Teams rely on a few well established workflow patterns that make automation scalable, predictable, and easier to operate over time.
One common pattern is sequential execution. In this pattern, tasks run one after another, where each step depends on the successful completion of the previous one. This approach is often used for build pipelines, validation jobs, or multi stage processing where order is critical.
Parallel execution is another widely used pattern. Here, multiple tasks run at the same time without waiting for each other. This significantly reduces execution time and is useful for running test suites, processing multiple datasets, or performing independent checks simultaneously.
A more advanced pattern is fan out and fan in. In this setup, a workflow splits into multiple parallel tasks and then joins back together once all tasks complete. This is commonly used in data processing and machine learning workflows where the same operation runs on different inputs and results are aggregated later.
Conditional execution is also important in production. Some steps should only run if certain conditions are met, such as a previous step succeeding or a parameter having a specific value. This allows workflows to adapt dynamically without manual intervention.
Retries and timeouts form another critical pattern. Production workflows must handle failures gracefully. Argo allows tasks to retry automatically and stop execution if time limits are exceeded, improving reliability.
These patterns are why Argo Workflows scales well in real world automation. For teams using Atmosly, such workflows remain visible and manageable alongside application workloads, making complex Kubernetes automation easier to operate with confidence.
Argo Workflows vs CI CD Tools Like Jenkins and GitHub Actions
Argo Workflows is often compared with traditional CI CD tools such as Jenkins and GitHub Actions. While they can overlap in some use cases, they are designed for different problems and operate in very different ways.
CI CD tools are primarily focused on code centric pipelines. They work best for building, testing, and deploying applications triggered by source code changes. These tools usually run jobs on external runners or managed infrastructure and then interact with Kubernetes from the outside.
Argo Workflows takes a different approach. It is Kubernetes native and runs entirely inside the cluster. Every step in a workflow runs as a Kubernetes pod. This makes Argo ideal for long running jobs, data processing, batch workloads, machine learning pipelines, and infrastructure automation that needs tight integration with Kubernetes resources.
Another key difference is execution control. Argo provides fine grained control over task dependencies, retries, parallelism, and resource usage at the Kubernetes level. This is harder to achieve with traditional CI CD tools without significant customization.
The table below highlights the differences clearly.
Capability | Argo Workflows | Jenkins or GitHub Actions |
Execution environment | Kubernetes pods | External runners |
Kubernetes native | Yes | No |
Long running workflows | Well suited | Limited |
Data and batch jobs | Strong support | Limited |
Code build pipelines | Not primary focus | Primary focus |
Resource control | Kubernetes based | Tool specific |
In practice, many teams use both. CI CD tools handle code delivery, while Argo Workflows manages Kubernetes native automation. For teams using Atmosly, this combination provides a clear separation between application delivery and operational automation, while keeping everything observable within the platform.
Security Observability and Resource Management in Argo Workflows
Running automation inside Kubernetes means security and resource control are just as important as execution logic. Argo Workflows is designed to align closely with Kubernetes security and observability models, which makes it suitable for production environments.
Argo relies on Kubernetes RBAC for access control. Permissions to create or run workflows can be limited by namespace and role. This allows platform teams to control who can trigger automation and what resources workflows are allowed to access. Secrets are handled using native Kubernetes Secrets, which keeps sensitive data out of workflow definitions.
Observability is another strong area. Every step in a workflow runs as a pod, which means logs, events, and metrics are available using standard Kubernetes tools. Teams can inspect execution status, view logs for each step, and trace failures without leaving the cluster.
Resource management is handled through pod level configuration. Teams can define CPU and memory limits for workflow steps, preventing automation from consuming excessive resources. This is especially important for data processing and batch workloads.
The table below summarizes how Argo handles these concerns.
Area | How Argo Handles It |
Access control | Kubernetes RBAC |
Secrets | Kubernetes Secrets |
Logs | Standard pod logs |
Metrics | Kubernetes monitoring |
Resource limits | Pod level CPU and memory |
For teams using Atmosly, this tight integration makes Argo workflows easier to monitor, secure, and control alongside application workloads.
Best Practices for Argo Workflows in 2025
As teams adopt Argo Workflows more deeply, following a few best practices helps keep automation reliable, scalable, and easy to operate in production Kubernetes environments.
The first best practice is to design workflows with clarity. Each workflow should have a clear purpose and well defined steps. Large workflows should be broken into reusable templates rather than placing all logic in a single file. This improves readability and makes reviews easier.
Template reuse is especially important. Common tasks such as validation, data preparation, or notifications should be defined once and reused across workflows. This reduces duplication and ensures consistent behavior.
Version control is another key practice. Workflow definitions should live in source control alongside application or platform code. This allows teams to track changes, review updates, and roll back safely when needed.
Environment separation should be handled through parameters rather than multiple workflow definitions. Using inputs to control behavior keeps workflows flexible and avoids configuration drift between environments.
Finally, resource limits should always be defined. Setting CPU and memory limits prevents workflows from overwhelming the cluster and makes capacity planning easier.
Following these practices ensures Argo Workflows remains a stable automation layer as usage grows. For teams operating Kubernetes with platforms like Atmosly, disciplined workflow design makes automation easier to observe, troubleshoot, and scale across environments.
Simplify Kubernetes Automation with Confidence
Argo Workflows brings powerful automation into Kubernetes but operating workflows at scale can still become complex. Monitoring failures, understanding execution paths, and managing resources across environments shouldn’t slow your team down.
Atmosly helps you go further.
With AI-powered Kubernetes troubleshooting, visual workflow and CI/CD insights, environment cloning, and cost visibility, Atmosly gives you a clear, unified way to operate Argo Workflows alongside your applications.
Sign up for Atmosly today and take full control of your Kubernetes automation without the operational overload.
