Kubernetes is excellent at running applications, but most teams quickly realize that running apps is only half the work. Real environments also need automation for tasks like batch jobs, data processing, testing, scheduled jobs, and maintenance workflows. This is where beginners often get stuck.
Many teams start with shell scripts or cron jobs. These solutions work at first, but they become hard to manage as soon as automation grows. Scripts break silently, logs are scattered, retries are manual, and scaling automation becomes painful. Traditional CI CD tools can help, but they are often external to Kubernetes and add another layer of complexity.
Argo Workflows solves this problem in a simple and Kubernetes native way. It lets you define automation as workflows that run directly inside your Kubernetes cluster. Each step runs as a container, follows Kubernetes rules, and is fully visible in the cluster.
The best part is that Argo Workflows is very beginner friendly. You describe what you want to run using YAML, submit it to Kubernetes, and Argo handles execution, retries, and tracking.
In this guide, you will build your very first Argo workflow using copy paste templates. By the end, you will have a working automation running inside Kubernetes and a clear understanding of how Argo Workflows fits into modern platforms like Atmosly.
Prerequisites Before You Start
Before building your first Argo Workflow, you need a small set of basics in place. The goal is to keep setup minimal so you can focus on learning automation rather than troubleshooting infrastructure.
First, you need access to a Kubernetes cluster. This can be a local cluster like Minikube or Kind, or a managed cluster on a cloud provider. As long as you can run Kubernetes workloads, Argo Workflows will work.
Next, make sure kubectl is installed and configured. You should be able to run a simple command like kubectl get nodes and see your cluster respond. This confirms that your local machine can communicate with Kubernetes.
Argo Workflows runs entirely inside Kubernetes, so no external servers or runners are required. You do not need a CI CD tool or additional databases to get started. This is one of the reasons Argo is beginner friendly.
Basic familiarity with YAML is helpful but not mandatory. You do not need to be an expert. The examples in this guide are designed to be copy paste friendly and easy to follow.
Finally, make sure you have permission to create resources in a namespace. Argo creates workflow resources and pods, so your Kubernetes role must allow this.
Once these prerequisites are ready, you are set to install Argo Workflows and build your first automation inside Kubernetes using simple templates that also fit naturally into platforms like Atmosly.
Installing Argo Workflows on Your Cluster
To build your first Argo Workflow, you first need Argo Workflows running inside your Kubernetes cluster. The installation is simple and does not require any external services, which makes it ideal for beginners.
Argo Workflows runs using a controller that watches workflow resources and executes them as Kubernetes pods. The recommended way to install it is by applying the official Kubernetes manifests provided by the Argo project.
Start by creating a dedicated namespace for Argo. This keeps automation components isolated and easier to manage.
Once the namespace is ready, apply the Argo Workflows installation manifests. Kubernetes will create the required resources such as the controller, service accounts, roles, and services automatically.
After installation, verify that the Argo controller pod is running. This confirms that Argo is active and ready to accept workflows. At this point, your cluster is prepared for automation.
You can optionally install the Argo CLI on your local machine. While not mandatory, it makes submitting workflows and checking their status easier than using kubectl alone.
Security is handled using Kubernetes RBAC. Access to create and run workflows can be limited by namespace and role, which is useful even in beginner environments.
Once Argo Workflows is installed, you are ready to move from setup to action. The next step is creating your first workflow using a simple copy paste template that runs directly inside Kubernetes and integrates cleanly with platforms like Atmosly.
Installing Argo Workflows on Your Cluster
To build your first Argo Workflow, you first need Argo Workflows running inside your Kubernetes cluster. The installation is simple and does not require any external services, which makes it ideal for beginners.
Argo Workflows runs using a controller that watches workflow resources and executes them as Kubernetes pods. The recommended way to install it is by applying the official Kubernetes manifests provided by the Argo project.
Start by creating a dedicated namespace for Argo. This keeps automation components isolated and easier to manage.
Once the namespace is ready, apply the Argo Workflows installation manifests. Kubernetes will create the required resources such as the controller, service accounts, roles, and services automatically.
After installation, verify that the Argo controller pod is running. This confirms that Argo is active and ready to accept workflows. At this point, your cluster is prepared for automation.
You can optionally install the Argo CLI on your local machine. While not mandatory, it makes submitting workflows and checking their status easier than using kubectl alone.
Security is handled using Kubernetes RBAC. Access to create and run workflows can be limited by namespace and role, which is useful even in beginner environments.
Once Argo Workflows is installed, you are ready to move from setup to action. The next step is creating your first workflow using a simple copy paste template that runs directly inside Kubernetes and integrates cleanly with platforms like Atmosly.
Running Multi Step Workflows Using Steps Template
Single step workflows are useful, but most real automation involves more than one task. Argo Workflows allows you to run multiple steps in a defined order using a steps template. This is one of the easiest ways for beginners to build real automation.
In a steps based workflow, tasks run sequentially. One step starts only after the previous step has finished successfully. This is ideal for simple pipelines where order matters, such as setup followed by processing and then cleanup.
Below is a simple two step workflow example.
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: multi-step-
spec:
entrypoint: steps-example
templates:
- name: steps-example
steps:
- - name: step-one
template: echo-step
arguments:
parameters:
- name: message
value: "First step running"
- - name: step-two
template: echo-step
arguments:
parameters:
- name: message
value: "Second step running"
- name: echo-step
inputs:
parameters:
- name: message
container:
image: alpine
command: ["sh", "-c"]
args: ["echo {{inputs.parameters.message}}"]
In this workflow, step one runs first and prints a message. Once it completes, step two runs. Both steps reuse the same template, which shows how templates help avoid duplication.
Each step runs as its own Kubernetes pod. Logs, execution status, and failures are easy to inspect. This structure makes workflows predictable and easy to extend.
Steps based workflows are perfect for beginners and form the foundation for more advanced automation that teams later operate at scale using platforms like Atmosly.
Running Multi Step Workflows Using Steps Template
Single step workflows are useful, but most real automation involves more than one task. Argo Workflows allows you to run multiple steps in a defined order using a steps template. This is one of the easiest ways for beginners to build real automation.
In a steps based workflow, tasks run sequentially. One step starts only after the previous step has finished successfully. This is ideal for simple pipelines where order matters, such as setup followed by processing and then cleanup.
Below is a simple two step workflow example.
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: multi-step-
spec:
entrypoint: steps-example
templates:
- name: steps-example
steps:
- - name: step-one
template: echo-step
arguments:
parameters:
- name: message
value: "First step running"
- - name: step-two
template: echo-step
arguments:
parameters:
- name: message
value: "Second step running"
- name: echo-step
inputs:
parameters:
- name: message
container:
image: alpine
command: ["sh", "-c"]
args: ["echo {{inputs.parameters.message}}"]
In this workflow, step one runs first and prints a message. Once it completes, step two runs. Both steps reuse the same template, which shows how templates help avoid duplication.
Each step runs as its own Kubernetes pod. Logs, execution status, and failures are easy to inspect. This structure makes workflows predictable and easy to extend.
Steps based workflows are perfect for beginners and form the foundation for more advanced automation that teams later operate at scale using platforms like Atmosly.
Running Tasks in Parallel Using DAG Workflows
After learning sequential steps, the next useful pattern is running tasks in parallel. Argo Workflows supports this using a DAG workflow. DAG stands for directed acyclic graph, but you do not need to worry about the term. All it means is that tasks can run at the same time when they do not depend on each other.
Parallel execution is useful when multiple tasks can run independently. Examples include running test suites, processing multiple inputs, or validating data in parallel. This helps workflows finish faster and use cluster resources more efficiently.
Below is a simple DAG workflow example that runs two tasks in parallel and then finishes.
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: parallel-example-
spec:
entrypoint: dag-example
templates:
- name: dag-example
dag:
tasks:
- name: task-one
template: echo-task
arguments:
parameters:
- name: message
value: "Task one running"
- name: task-two
template: echo-task
arguments:
parameters:
- name: message
value: "Task two running"
- name: echo-task
inputs:
parameters:
- name: message
container:
image: alpine
command: ["sh", "-c"]
args: ["echo {{inputs.parameters.message}}"]
In this workflow, task one and task two start at the same time because there are no dependencies defined between them. Argo creates separate pods for each task and runs them in parallel.
DAG workflows give you more control than steps workflows. You can define dependencies when needed and keep tasks independent when possible. This makes automation faster and more flexible.
For beginners, DAG workflows are the next natural step after sequential workflows. For teams using Atmosly, this parallel execution model makes large scale automation easier to observe and manage within Kubernetes.
Adding Parameters and Reusable Templates
As workflows grow, hardcoding values quickly becomes a problem. Parameters solve this by allowing you to pass inputs into workflows and templates, making them reusable and easier to manage.
Parameters are values such as names, versions, or environment specific settings that can change without modifying the workflow logic. You define parameters once and reference them wherever needed.
Below is a simple example that accepts a message as a parameter.
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: param-example-
spec:
entrypoint: param-flow
arguments:
parameters:
- name: message
value: "Hello from parameters"
templates:
- name: param-flow
steps:
- - name: print-message
template: echo
arguments:
parameters:
- name: text
value: "{{workflow.parameters.message}}"
- name: echo
inputs:
parameters:
- name: text
container:
image: alpine
command: ["sh", "-c"]
args: ["echo {{inputs.parameters.text}}"]
This workflow defines a parameter called message at the top level. The value is passed into a reusable template that simply prints it. You can change the message without touching the template logic.
Reusable templates reduce duplication. Instead of writing the same container definition multiple times, you define it once and reference it across steps and workflows.
For beginners, parameters are the key to clean workflows. For teams using Atmosly, parameterized and reusable workflows are easier to operate across environments while keeping automation consistent and visible at scale.
Adding Parameters and Reusable Templates
As workflows grow, hardcoding values quickly becomes a problem. Parameters solve this by allowing you to pass inputs into workflows and templates, making them reusable and easier to manage.
Parameters are values such as names, versions, or environment specific settings that can change without modifying the workflow logic. You define parameters once and reference them wherever needed.
Below is a simple example that accepts a message as a parameter.
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: param-example-
spec:
entrypoint: param-flow
arguments:
parameters:
- name: message
value: "Hello from parameters"
templates:
- name: param-flow
steps:
- - name: print-message
template: echo
arguments:
parameters:
- name: text
value: "{{workflow.parameters.message}}"
- name: echo
inputs:
parameters:
- name: text
container:
image: alpine
command: ["sh", "-c"]
args: ["echo {{inputs.parameters.text}}"]
This workflow defines a parameter called message at the top level. The value is passed into a reusable template that simply prints it. You can change the message without touching the template logic.
Reusable templates reduce duplication. Instead of writing the same container definition multiple times, you define it once and reference it across steps and workflows.
For beginners, parameters are the key to clean workflows. For teams using Atmosly, parameterized and reusable workflows are easier to operate across environments while keeping automation consistent and visible at scale.
Viewing Logs Debugging and Monitoring Workflows
- Once workflows are running, the next important skill is knowing how to see what is happening inside them. Argo Workflows makes debugging straightforward because every step runs as a Kubernetes pod.
- Each workflow has a clear status that shows whether it is running, succeeded, or failed. You can check this status using Kubernetes commands or the Argo CLI. This immediately tells you where the workflow is in its execution.
- Logs are handled the same way as any Kubernetes workload. Since each step runs in its own pod, you can view logs for a specific step using standard pod log commands. This makes troubleshooting familiar if you already work with Kubernetes.
- Failures are easy to identify. Argo marks the exact step that failed and keeps execution details available for inspection. You do not need to search through long scripts or external systems to understand what went wrong.
- Basic monitoring comes from Kubernetes itself. Pod status, resource usage, and events provide enough visibility for most beginner and intermediate workflows.
- For teams using Atmosly, this visibility becomes even clearer. Workflow executions, failures, and resource usage can be viewed alongside application workloads, making it easier to operate automation without switching tools or losing context.
Best Practices for Beginners Using Argo Workflows
- When starting with Argo Workflows, following a few simple best practices will save time and prevent common issues as automation grows.
- Keep workflows small and focused. Each workflow should solve one clear problem. If a workflow starts becoming too large, split it into reusable templates. This keeps automation easier to read and maintain.
- Use namespaces properly. Run workflows in dedicated namespaces so automation does not interfere with application workloads. This also makes permissions and cleanup easier to manage.
- Always define resource limits for workflow steps. Setting CPU and memory limits prevents workflows from consuming excessive resources and protects cluster stability.
- Store workflow definitions in version control. Treat workflows like application code. This allows teams to review changes, track history, and roll back when needed.
- Avoid hardcoding values. Use parameters instead so workflows can be reused across environments without modification.
These practices help beginners build clean and reliable automation from day one. For teams operating Kubernetes with platforms like Atmosly, disciplined workflow design makes automation easier to scale, observe, and troubleshoot as usage increases.
Common Beginner Mistakes and How to Avoid Them
- When building your first Argo Workflows, a few common mistakes can make automation harder than it needs to be. Knowing these early helps you move faster and avoid frustration.
- One common mistake is overcomplicating workflows too soon. Beginners often try to build large workflows with many steps and conditions from the start. It is better to begin with small workflows and add complexity only when required. Simple workflows are easier to debug and maintain.
- Another mistake is hardcoding values directly inside templates. This makes workflows difficult to reuse and change. Using parameters keeps workflows flexible and allows the same logic to work across different environments.
- Ignoring retries and timeouts is also risky. Without these controls, workflows can fail due to temporary issues or run longer than expected. Defining retries and time limits makes automation more reliable and protects cluster resources.
- Some teams forget to set resource limits for workflow steps. This can cause automation to consume more CPU or memory than intended. Always define limits to keep the cluster stable.
- Finally, not storing workflows in version control leads to confusion and lost changes. Treat workflows like code so they can be reviewed, tracked, and improved over time.
- Avoiding these mistakes helps beginners build automation that is clean, predictable, and easy to operate, especially when workflows grow and are managed alongside platforms like Atmosly.
Final Summary What You Have Built
By following this guide, you have built your first Argo Workflow step by step using simple copy paste templates. You learned how Argo Workflows fits into Kubernetes automation, how workflows are structured, and how each step runs as a container inside the cluster.
You started with a basic single step workflow and then expanded it into multi step and parallel workflows. You added parameters to make workflows reusable, handled failures with retries and timeouts, and learned how to view logs and debug executions using familiar Kubernetes tools. Along the way, you also learned practical best practices and common mistakes to avoid as a beginner.
What you have built is not a demo toy. It is real Kubernetes native automation that follows the same execution, security, and observability model as application workloads. This is what makes Argo Workflows so powerful and approachable.
From here, you can confidently automate batch jobs, data processing tasks, maintenance workflows, and other operational processes inside Kubernetes. As workflows grow, they remain structured, visible, and manageable.
For teams operating Kubernetes platforms and using tools like Atmosly, this foundation makes it much easier to scale automation without adding unnecessary complexity or losing control.
Operating Argo Workflows at Scale With Atmosly
Building your first Argo Workflow is an important milestone, but the real challenge begins when automation grows. As more workflows are added, teams need clear visibility into executions, failures, resource usage, and environment level impact. Without the right platform support, troubleshooting and operational overhead can quickly increase.
This is where Atmosly fits naturally into Argo based Kubernetes automation.
Atmosly helps teams operate Argo Workflows with better clarity and control. Workflow executions remain visible alongside application workloads, making it easier to understand what is running, what failed, and why. Instead of jumping between Kubernetes dashboards, logs, and scripts, teams get a unified view of their platform.
With capabilities like visual CI CD pipelines, AI assisted Kubernetes troubleshooting, environment management, and cost awareness, Atmosly reduces the operational complexity that often follows large scale automation. Teams can confidently run workflows, manage changes, and resolve issues faster.
If Argo Workflows helps you automate Kubernetes tasks, Atmosly helps you operate that automation reliably at scale.
Sign up for Atmosly and simplify how you manage, monitor, and scale Argo Workflows in Kubernetes.
