Automation Platform > Deployment & hosting

Managed: Kubernetes backend

Deploy the Automation Platform managed worker into a Kubernetes cluster with the included Helm chart. Each agent task runs as a Kubernetes Job in your cluster.

Deploy the oz-agent-worker daemon into a Kubernetes cluster using the included Helm chart. Each agent task runs as a Kubernetes Job in your cluster. The Automation Platform orchestrates runs end to end (Slack, Linear, schedules, API, oz agent run-cloud); your cluster provides the compute, scheduling, and policy enforcement.

When to use the Kubernetes backend

You already operate a Kubernetes cluster and want agents to run there.
You need Kubernetes-native scheduling, resource management, or policy enforcement.
You want to use Kubernetes Secrets, ServiceAccounts, and admission policies to control task behavior.

How it works

The worker connects to the Kubernetes API server (using in-cluster auth by default, or an explicit kubeconfig).
On startup, the worker runs a short-lived preflight Job to verify that cluster permissions, admission policies, and Pod Security Standards are compatible. If the preflight fails, the worker exits with a diagnostic error before accepting any tasks.
For each assigned task, the worker creates a Kubernetes Job in the configured namespace.
The worker monitors the Job and Pod status via Kubernetes Watch (with a 30-second safety-net poll for watch disconnects).
The worker deletes successful Jobs. Failed Jobs remain until Kubernetes TTL cleanup. Set --no-cleanup to disable worker-managed cleanup.

Prerequisites

Enterprise plan with self-hosting enabled — Contact sales if self-hosting is not yet enabled for your team.
A Kubernetes cluster with the worker process able to reach the API server. The cluster must:
- Allow the worker’s namespace to create Jobs with a root init container (sidecar materialization depends on this pattern).
- Grant the worker these namespace-scoped permissions: create, get, list, watch, delete on jobs; get, list, watch on pods; get on pods/log; list on events.
Helm installed locally, plus kubectl authenticated against the target cluster.
An agent API key — Create one in the Oz web app so the worker can authenticate to the Automation Platform. You can bind the key to any cloud agent — that choice doesn’t restrict which agents can run on the worker. See API Keys for the full creation flow.

Install with the Helm chart

The oz-agent-worker repository includes a namespace-scoped Helm chart at charts/oz-agent-worker. This is the recommended way to deploy the worker into a cluster.

What the chart deploys

A long-lived Deployment running oz-agent-worker with the Kubernetes backend.
A namespaced ServiceAccount for the worker.
A namespaced Role / RoleBinding with the minimum permissions needed to manage task Jobs and Pods.
A ConfigMap containing the worker config YAML.
An optional Secret for WARP_API_KEY (or a reference to an existing Secret).

The chart does not create CRDs or cluster-scoped RBAC resources.

1. Set your API key and namespace

export WARP_API_KEY="your_agent_api_key"

Create the namespace if it doesn’t exist:

kubectl create namespace warp-oz

2. Create the API key Secret

If you’re not using an existing Secret, create one with the API key:

kubectl create secret generic oz-agent-worker \
  --from-literal=WARP_API_KEY="$WARP_API_KEY" \
  --namespace warp-oz

Expected outcome: kubectl get secret -n warp-oz oz-agent-worker shows the Secret.

3. Install the chart

Clone the worker repo and install the chart:

git clone https://github.com/warpdotdev/oz-agent-worker.git

helm install oz-agent-worker ./oz-agent-worker/charts/oz-agent-worker \
  --namespace warp-oz \
  --set worker.workerId=oz-k8s-worker \
  --set image.tag=<version>

Expected outcome: kubectl get pods -n warp-oz shows the worker Deployment pod as Running, and the worker logs show Connected to Oz / Listening for tasks.

To scale horizontally, deploy multiple Helm releases with distinct worker IDs rather than increasing replicas on a single release.

Key chart values

Set worker.workerId and image.tag for each installation. These values affect capacity and task placement:

worker.resources — CPU and memory for the worker Deployment, not task Jobs. The chart requests 100m CPU and 128Mi memory by default and sets no limits.
worker.maxConcurrentTasks — Maximum concurrent tasks. The default 0 allows unlimited tasks.
kubernetesBackend.podTemplate — Raw PodSpec YAML for task Jobs.

See the self-hosted worker reference for every worker, backend, API key, and metrics value.

Cluster selection

Cluster selection follows Kubernetes client config conventions:

Set backend.kubernetes.kubeconfig to use an explicit kubeconfig file.
If kubeconfig is omitted and the worker runs inside a Kubernetes pod, the worker uses in-cluster config automatically.
Otherwise, the worker falls back to the default kubeconfig loading rules and uses the current context.

namespace selects the namespace inside the chosen cluster. It defaults to default when omitted.

Pod template

The pod_template field accepts standard Kubernetes PodSpec YAML and is the declarative way to configure task pod scheduling, service accounts, image pull secrets, resources, and environment variables.

When using pod_template, define a container named task to customize the main task container directly. Otherwise, the worker appends its own task container to the PodSpec.

Size task workloads

The worker does not set default CPU or memory resources on task containers. Configure task resources in either of these places:

Pod template — Set resources.requests and resources.limits on the container named task. Use the rest of the PodSpec for node selectors, affinity, and tolerations.
Runner instance shape — Assign a runner with the vCPUs and memory needed for a workload. An explicit shape sets the task container’s CPU and memory requests equal to its limits and overrides matching pod_template values.

A runner instance shape sizes only task. A pod_template resource setting applies only to a container you define in the template; it does not alter worker-generated setup or materialization init containers.

Requests determine Pod placement, while limits constrain a running container. Account for concurrent Jobs and node headroom. Use workload-specific runners for occasional heavy builds or tests instead of increasing every task’s baseline.

Use valueFrom.secretKeyRef to inject Kubernetes Secret values into task container environment variables:

pod_template:
  serviceAccountName: agent-task-sa
  imagePullSecrets:
    - name: my-registry-creds
  containers:
    - name: task
      resources:
        requests:
          cpu: "2"
          memory: 4Gi
        limits:
          memory: 8Gi
      env:
        - name: GITHUB_TOKEN
          valueFrom:
            secretKeyRef:
              name: my-k8s-secret
              key: github-token
  tolerations:
    - key: "dedicated"
      operator: "Equal"
      value: "agents"
      effect: "NoSchedule"

The task Job’s serviceAccountName controls runtime access. It is separate from the worker Deployment’s ServiceAccount, which manages Jobs and Pods.

Preflight check

On startup, the worker creates a short-lived preflight Job to verify that:

The worker has sufficient RBAC permissions in the target namespace.
Cluster admission policies (Pod Security Standards, OPA Gatekeeper, Kyverno, etc.) allow the worker’s task pod shape.
The preflight image can be pulled.

If the preflight fails, the worker logs a diagnostic error and exits before accepting any tasks. This surfaces policy and configuration issues at deploy time rather than at task execution time.

The preflight image defaults to busybox:1.36. If your cluster restricts allowed registries or images, set preflight_image to an allowlisted image. When imagePullSecrets is configured in pod_template, those secrets apply to the preflight Job as well, so you can point preflight_image at an image in your private registry.

Environment variables for Kubernetes tasks

There are two ways to pass environment variables to Kubernetes task containers:

pod_template (recommended for Kubernetes-native config) — Use standard Kubernetes env syntax in the task container, including valueFrom.secretKeyRef for Kubernetes Secrets.
-e / --env flags — Backend-agnostic runtime overrides that work across all managed backends.

When configuring the Kubernetes backend via YAML or Helm, declarative task-container env belongs in pod_template rather than a separate top-level list.

Setup and teardown commands

Use kubernetesBackend.setupCommand (Helm value) or backend.kubernetes.setup_command (config file) to run a shell command before each task. Use teardownCommand / teardown_command for cleanup after the task finishes. These run inside the task Pod and are useful for workspace bootstrapping or post-run reporting.

Metrics

Enable worker OpenTelemetry metrics with metrics.enabled=true. See Monitoring for Helm values, the metric catalog, and sample PromQL queries.

Self-hosted worker reference — Full CLI flag and config file schema, including every Kubernetes backend field.
Self-hosting overview — Managed vs unmanaged and the backend decision guide.
Routing runs to this worker — How to send tasks to your connected worker from the CLI, schedules, integrations, the API, and the web UI.
Environments — Define the task image, repos, and setup commands.
Monitoring — OpenTelemetry metrics, including Helm chart metrics values.
Security and networking — RBAC, admission policies, and data boundaries.
Troubleshooting — Common Kubernetes-backend issues.