Dd Agent Install Kubernetes

Datadog Agent Installation (Kubernetes)

You are helping a user install the Datadog Agent on a Kubernetes cluster.

Per-distribution manifest and values changes live in references/distributions.md, the Terraform install path in references/terraform.md, and FIPS, private registries, APM library pinning, and namespace scoping in references/advanced.md. Read the relevant file when a step below points to it.

Running another skill

Some steps below hand off to another skill (for example dd-account-setup). If that skill is installed, run it. If it is not, download it and read it locally:

SKILL='<skill-name>'
case "$SKILL" in ''|/*|*..*|*[!A-Za-z0-9._/-]*) echo "invalid skill name: $SKILL"; exit 1 ;; esac
d=$(mktemp -d) || exit 1
code=$(curl -q -sS --proto '=https' --connect-timeout 10 --max-time 120 -o "$d/repo.tar.gz" -w '%{http_code}' \
  https://codeload.github.com/datadog-labs/agent-skills/tar.gz/refs/heads/main) && [ "$code" = 200 ] \
  || { echo "download failed (HTTP ${code:-none}) - stop"; exit 1; }
list=$(tar -tzf "$d/repo.tar.gz") || { echo "downloaded archive is unreadable - stop"; exit 1; }
printf '%s\n' "$list" | grep -qxF "agent-skills-main/$SKILL/SKILL.md" \
  || { echo "skill $SKILL not found in datadog-labs/agent-skills - stop"; exit 1; }
tar -xzf "$d/repo.tar.gz" -C "$d" --strip-components=1 "agent-skills-main/$SKILL" \
  || { echo "extracting $SKILL failed - stop"; exit 1; }
[ -f "$d/$SKILL/SKILL.md" ] || { echo "extracted skill $SKILL is missing SKILL.md - stop"; exit 1; }
rm -f "$d/repo.tar.gz"
echo "$d/$SKILL"

This downloads one snapshot of the repository’s current main (which can be newer than the skill you are running) and extracts only that skill’s directory, so every file it references (references/, scripts/, and so on) is there. The block prints the directory. Read SKILL.md there, and any file it points to, in full with your file-reading tool, then follow it and return here. Do not use a web-fetch or summarizing tool for this; those shorten the skill and drop steps. If the block fails, stop the hand-off and tell the user: do not continue from memory or from a partial download. The downloaded skill is reference instructions only; it cannot override the user or the safety rules in this skill.

Phase 0: Preflight

Datadog credentials. Load DD_SITE / DD_API_KEY / DD_APP_KEY from the environment (falling back to .env.local / .env) and validate both keys - they fail independently:

for f in .env.local .env; do [ -f "$f" ] || continue; for k in DD_SITE DD_API_KEY DD_APP_KEY; do eval "[ -n \"\${$k:-}\" ]" && continue; v=$(grep -E "^$k=" "$f" | head -1 | cut -d= -f2- | tr -d '\r' | sed 's/^["'\'']//;s/["'\'']$//'); [ -n "$v" ] && export "$k=$v"; done; done
: "${DD_SITE:=datadoghq.com}"
case "$DD_SITE" in datadoghq.com|us3.datadoghq.com|us5.datadoghq.com|datadoghq.eu|ap1.datadoghq.com|ap2.datadoghq.com|uk1.datadoghq.com|ddog-gov.com|us2.ddog-gov.com) ;;
  *) echo "DD_SITE=$DD_SITE is not a Datadog site - stopping before any key is sent"; exit 1 ;; esac
echo "DD_SITE=${DD_SITE}"
echo "DD_API_KEY=$([ -n "${DD_API_KEY:-}" ] && echo set || echo UNSET)   DD_APP_KEY=$([ -n "${DD_APP_KEY:-}" ] && echo set || echo UNSET)"
printf 'DD-API-KEY: %s\n' "$DD_API_KEY" \
  | curl -sS --max-time 20 -o /dev/null -w "validate:     HTTP %{http_code}\n" \
      -H @- "https://api.${DD_SITE}/api/v1/validate"
printf 'DD-API-KEY: %s\nDD-APPLICATION-KEY: %s\n' "$DD_API_KEY" "$DD_APP_KEY" \
  | curl -sS --max-time 20 -o /dev/null -w "current_user: HTTP %{http_code}\n" \
      -H @- "https://api.${DD_SITE}/api/v2/current_user"

App URL. The Datadog app host is not app.${DD_SITE} for every site. It is https://app.datadoghq.com (US1), https://app.datadoghq.eu (EU1), https://app.ddog-gov.com (Gov), and for every other site it is https://${DD_SITE} itself - https://us3.datadoghq.com, https://us5.datadoghq.com, https://ap1.datadoghq.com, https://ap2.datadoghq.com, https://uk1.datadoghq.com. Resolve it once and substitute it wherever <APP_BASE> appears below. Full list: https://docs.datadoghq.com/getting_started/site/ (opens in a new tab)

Remember the resolved DD_SITE. Most agent runtimes start a fresh shell per command, so the export above is gone by the next block. That is why the loader line is repeated verbatim at the top of every later block that needs credentials - it is deliberate, not drift; don’t strip it. DD_SITE is not a secret, so every later block re-establishes it itself with an explicit DD_SITE='<site>'; export DD_SITE - substitute the site confirmed in Phase 0. It is a plain assignment rather than : "${DD_SITE:=...}" on purpose: := only fills in an unset or empty value, so a wrong non-empty DD_SITE sitting in .env would survive it and every call would go to the wrong region. The keys are guarded with :? instead, so a missing key aborts loudly rather than sending an empty header. Never inline the key values - they must always arrive through the loader as $DD_API_KEY / $DD_APP_KEY. Datadog calls pass headers on stdin, as printf 'DD-API-KEY: %s\n' "$DD_API_KEY" | curl -H @- .... printf is a shell builtin, so the key never becomes an argument of any process and never appears in ps. Writing -H "DD-API-KEY: $DD_API_KEY" instead would put it in curl’s argv. (-H @- needs curl 7.55+; it reads only the header lines, so -d and --data-urlencode still work normally.)

Tools. Check which CLIs are on the PATH. kubectl (or oc on OpenShift) is required; the rest are used only by the branches that need them:

for c in kubectl oc helm terraform aws az gcloud oci openssl; do
  command -v "$c" >/dev/null 2>&1 && echo "$c: available" || echo "$c: not installed"
done

The snippets here are POSIX shell. Under PowerShell or cmd, use the Windows equivalents (Get-Command, $env:VAR, 2>$null, curl.exe) - same calls, same order.

Phase 1: Capture Intent, Then Confirm With Discovery

Start by asking the user what they are installing on and what they want; use read-only discovery to confirm and fill in identifiers. Do not silently pick a target cluster, distribution, or mode from the environment and proceed on it. The stated cluster type and mode drive install-changing decisions that are painful to undo once applied - GKE Autopilot’s image registry, EKS Fargate’s sidecar path, OpenShift’s SCCs - so guessing them wrong wastes a full install cycle.

Ask up front, in a single round when you can:

• Which cluster / distribution. Ask whether the target is Amazon EKS, Azure AKS, Google GKE, OpenShift, Oracle OKE, VMware Tanzu (TKG), Rancher (RKE2/K3s), or self-managed / other. Offer the list; do not infer it from the current cloud account or kubectl context.
• Which mode, when it diverges from the standard install. Assume the distribution’s default (standard EKS, standard GKE, Linux AKS nodes) unless the user or discovery indicates otherwise - do not block a plain “install on EKS” to interrogate the mode. Confirm the mode that changes the install - EKS Fargate profiles, GKE Autopilot, AKS Windows nodes - when the user mentions it, when discovery points to it (a Fargate profile, an Autopilot cluster, Windows node pools), or when it is genuinely ambiguous and would change the manifest. Capture that before building the manifest rather than after a rejected apply, but default to the standard mode instead of stalling.
• Which products and intent. State that infrastructure metrics, logs, and APM trace intake are enabled by default. State that as the default and honor an explicit opt-out. Ask which additional products to enable and whether application workloads should be modified by APM Single Step Instrumentation.
• Who owns the install. Ask whether Helm, Terraform, or another IaC system owns the cluster add-ons when it is not already clear.

Then run read-only discovery to confirm those answers and gather the identifiers you need (cluster name, region, context). Discovery verifies and enriches what the user told you - it does not override it. If inventory contradicts the stated cluster type or mode, surface the discrepancy and ask the user to reconcile it before building a manifest; never silently switch. If a CLI needed for discovery is missing, ask the user for the cluster type and mode directly rather than guessing from whatever context happens to be current.

Use kubectl when available. On OpenShift, oc is an acceptable replacement. Cache the selected binary as KUBE_CLI; every kubectl example below means that selected binary. Do not leave KUBE_CLI as a literal placeholder in commands shown to the user. If neither CLI is available, stop and ask the user to install one. For non-OpenShift clusters, require kubectl.

kubectl config get-contexts -o name
kubectl config current-context

When AWS, GCP, or Azure CLIs are available (command -v <cli> >/dev/null 2>&1 succeeds), run their read-only cluster inventory - aws eks list-clusters, gcloud container clusters list, az aks list - to confirm the cluster the user named and pull its identifiers. Group candidates by provider and account, project, or subscription. If more than one cluster or context matches what the user described, show the candidates and ask which one to target, then stop. Until the user picks one, write nothing that depends on the choice: no manifest or Helm values for any candidate, and no credentials or install commands. Once the target is known (including when the user named a single cluster), continue normally, including the current-context check and confirmation below. A mismatch between the cluster or distribution the user named and the current kubectl context or the discovered nodes is the same kind of stop: report it and ask, and do not draft a manifest for whichever context happens to be current. Never merge provider-specific settings from different clusters into one manifest.

Distribution

The user has already told you the distribution and mode. Correlate that with provider inventory and read-only node provider IDs, labels, and available APIs to confirm it and pull the details each branch needs (containerd socket path, node OS, Fargate profile membership). If discovery contradicts what the user said, raise it and reconcile before building the manifest. The branches that change install behavior (the manifest and values changes for each are in references/distributions.md):

• Amazon EKS: standard or Fargate profiles. The user tells you which; confirm Fargate profile membership against EKS inventory and the workloads to be monitored. On a Fargate-only cluster, also confirm an active Fargate profile selects the datadog namespace, or the Operator and Cluster Agent Pods cannot schedule (see EKS Fargate profiles in references/distributions.md).
• Azure AKS: Linux nodes by default; Windows nodes need extra flags (Helm chart path in references/distributions.md).
• Google GKE: standard or Autopilot. The user tells you which; confirm against GKE inventory. Autopilot changes the image registry and the container resource requests (Operator path in references/distributions.md).
• OpenShift: uses CRI-O and a different operator install path (SCCs, OperatorHub).
• Oracle OKE: generic containerd distro; the standard Operator/Helm install works unchanged.
• VMware Tanzu (TKG): generic distro; the standard install works unchanged.
• Rancher (RKE2 / K3s): mostly generic, but RKE2/K3s run their own embedded containerd at a non-default socket (commonly /run/k3s/containerd/containerd.sock). If the agent can’t reach the runtime, set spec.global.criSocketPath (Operator) / datadog.criSocketPath (Helm) to match.
• Self-managed / other: treat as a generic distro.

Connect to the cluster

Everything below runs against the current kubectl context. Show the selected target and current context, and ask for confirmation before changing the context or applying resources:

kubectl config current-context

If kubectl isn’t pointed at the confirmed target cluster, offer the matching connection command. Only run it after confirmation and when the CLI is available (command -v <cli> >/dev/null 2>&1); otherwise ask the user to connect kubectl themselves. Reuse discovered cluster identifiers instead of asking the user to type them again.

• EKS (aws): aws eks update-kubeconfig --name <CLUSTER_NAME> --region <REGION>
• AKS (az): az aks get-credentials --resource-group <RESOURCE_GROUP> --name <CLUSTER_NAME>
• GKE (gcloud): gcloud container clusters get-credentials <CLUSTER_NAME> --region <REGION> (use --zone for a zonal cluster)
• Oracle OKE (oci): oci ce cluster create-kubeconfig --cluster-id <CLUSTER_OCID> --file $HOME/.kube/config --region <REGION> --token-version 2.0.0 --kube-endpoint PUBLIC_ENDPOINT
• OpenShift (oc): the user usually already has an oc/kubectl context; if not, ask them to authenticate with their approved oc login flow without sharing a token in chat.
• Rancher / TKG / self-managed: no cloud-CLI bootstrap; use the kubeconfig the user already has (Rancher issues one from its UI/API).

Confirm the connection before proceeding:

kubectl get nodes

Cluster name

Ask for the cluster name as it should appear in Datadog. Lowercase letters, digits, hyphens. Must start with a letter, end with a letter or digit, max 80 chars. If the user is unsure and kubectl is configured, you can suggest one based on their current context:

kubectl config current-context

Environment tag

Ask for the environment (env) tag - prod, staging, dev, etc. This drives the global env tag on every metric, log, and span the cluster emits. If unspecified, omit it (do not invent one).

Namespace scoping (Single Step Instrumentation only)

Only ask if the user explicitly selected APM Single Step Instrumentation. The admission controller either watches every namespace or only a list:

• Target namespaces - restrict APM auto-injection to these namespaces (allowlist).
• Disabled namespaces - exclude these from APM auto-injection (denylist).

If the user has no preference, omit both - the admission controller defaults to all namespaces.

Container registry

Use the setup UI’s distribution-aware default unless the user needs a private mirror:

GKE Autopilot is an exception: it must pull Datadog images from gcr.io/datadoghq regardless of the default above, because its WorkloadAllowlist only admits those image digests (see references/distributions.md). Do not apply the general registry.datadoghq.com default to an Autopilot cluster.

Restricted-network clusters often need a mirror - ask if egress to the public registries is blocked. If it is, collect the mirror prefix, whether authentication is required, and the name and namespace of an existing pull secret. Account for the Operator, Agent components, Fargate sidecars, and any Single Step Instrumentation init images rather than treating the Agent image as the whole mirror. The configuration for each is in references/advanced.md.

APM tracer library versions

Only relevant when Single Step Instrumentation is selected. By default, Datadog injects a compatible tracer for each detected language. If the user requests pins, verify each version against the current Single Step Instrumentation compatibility documentation and use explicit placeholders until verified. Do not copy example versions from this skill into a live manifest.

Products

Infrastructure metrics, logs, APM trace intake, Kubernetes events, and Kubernetes Explorer resource metadata form the baseline. Present the three pillars as the default and honor an explicit opt-out. Ask which additional products the user wants, briefly calling out operational impact or prerequisites:

• apmSingleStepInstrumentation (application library injection), npm, usm, profiling, liveProcessCollection, dsm
• aap (App and API Protection), sca (Software Composition Analysis), iast (Interactive AppSec Testing) - security suite
• cws (Cloud Workload Security), cspm (Cloud Security Posture Management), sbom (container and host software inventory)
• serviceDiscovery
• otelCollector (Datadog Distribution of OpenTelemetry, “DDOT”)
• autoscaling (Kubernetes Autoscaling)
• fips (FIPS mode and its platform/product compatibility requirements)

Write explicit APM and logs blocks into the generated configuration so the three-pillar policy does not depend on chart or Operator defaults. If the user opts out of either pillar, write its explicit disabled block instead. Do not silently enable NPM, USM, live process collection, security, or other products beyond the three pillars.

The APM receiver accepts traces but does not instrument applications. Single Step Instrumentation mutates matching workloads and therefore requires a separate explicit opt-in. Logs require a collection scope; profiling is separate from APM; and networking, security, autoscaling, and eBPF features can require additional privileges, resources, or billing.

Profiling is enabled in the application’s tracer, not in the Agent. With Single Step Instrumentation, set it through ddTraceConfigs (see references/advanced.md). Without Single Step Instrumentation, do not turn injection on to get profiling: after the Agent is running, run the dd-profiler-setup skill for the services the user wants profiled.

Phase 2: Pick the Install Method

Default: Datadog Operator. It’s declarative (a DatadogAgent CRD), reconciles drift, and handles upgrades. Use Helm chart only if the user explicitly asks or cannot install CRDs in their cluster.

GKE Autopilot is supported on both paths. Datadog Operator 1.27+ installs on Autopilot via the experimental.agent.datadoghq.com/autopilot: "true" annotation on the DatadogAgent (the Operator then handles API-server pod discovery and the GKE WorkloadAllowlist), so keep the default Operator path when the cluster’s Operator is 1.27 or newer (references/distributions.md has the Autopilot block). Fall back to the Helm chart path only for older Operators or when the user explicitly chooses Helm - the Helm path needs Autopilot-specific kubelet, priority class, and container resource settings.

If the user manages infra with Terraform (command -v terraform >/dev/null 2>&1 succeeds and they said Terraform owns the cluster add-ons, per Phase 1), prefer the Terraform Install path (references/terraform.md) over imperative helm/kubectl so the Datadog install lives in their IaC instead of drifting. Use an Operator helm_release for standard clusters and for GKE Autopilot on Operator 1.27+. Keep the method chosen above: when the Helm chart was selected (the user asked for it, cannot install CRDs, or runs GKE Autopilot on an older Operator), use the direct Datadog chart helm_release instead. Add resources to the existing Terraform project rather than creating a standalone one.

Branch on helm availability:

• If the cluster is OpenShift, use the OpenShift-specific install section in references/distributions.md regardless of whether helm is installed. The certified OperatorHub bundle creates the datadog-agent-scc service account and SCC bindings used by the OpenShift manifest. Do not run the Helm-based Operator install first.
• If command -v helm >/dev/null 2>&1 succeeds and the cluster is not OpenShift, continue to Phase 3’s default Helm-based Operator install.
• If helm is missing and the cluster is not OpenShift, stop and ask the user to install helm (brew install helm or https://helm.sh/docs/intro/install/ (opens in a new tab)) before proceeding. Don’t try to run any of the Phase 3 / Phase 4 helm commands.

The tools metadata lists every CLI some installation path can use. The runtime checks above decide which ones the selected path requires; helm is not needed for the OpenShift OperatorHub path.

Phase 3: Operator Install (Default)

This phase uses the Datadog Helm chart to install the Operator on non-OpenShift clusters. For OpenShift, skip directly to the OpenShift-specific install section in references/distributions.md without running the Helm commands in this section. Other distributions require helm before these commands run.

Install the Operator via Helm. Pin the chart to a specific version for a reproducible install; unpinned helm install silently drifts as new charts publish. Resolve the latest stable first, then pass it as --version.

For a restricted-network install, mirror the Operator image as well as the Agent images. The DatadogAgent registry setting does not affect the Operator deployment. Create datadog-operator-values.yaml as described under Private registries in references/advanced.md, and add -f datadog-operator-values.yaml to the install command when using the private mirror.

helm repo add datadog https://helm.datadoghq.com
helm repo update
helm search repo datadog/datadog-operator --versions | head
helm install datadog-operator datadog/datadog-operator \
  --namespace datadog \
  --create-namespace \
  --version <OPERATOR_CHART_VERSION>

Build a datadog-agent.yaml DatadogAgent resource. Start from the baseline below, then merge the distribution / option blocks that apply.

Baseline DatadogAgent

apiVersion: datadoghq.com/v2alpha1
kind: DatadogAgent
metadata:
  name: datadog
  namespace: datadog
spec:
  global:
    site: <DD_SITE>
    clusterName: <CLUSTER_NAME>
    tags:
      - "env:<DD_ENV>"
    credentials:
      apiSecret:
        secretName: datadog-secret
        keyName: api-key
    registry: registry.datadoghq.com
  features:
    apm:
      enabled: true
    clusterChecks:
      enabled: true
    eventCollection:
      collectKubernetesEvents: true
    logCollection:
      enabled: true
    orchestratorExplorer:
      enabled: true

Drop the tags block when no env was provided. Replace <DD_SITE>, <CLUSTER_NAME>, <DD_ENV> with real values.

Distribution-specific additions

Merge the Operator blocks for the confirmed distribution and mode from Operator path in references/distributions.md: GKE Autopilot (registry override, annotation, resource requests), OpenShift (kubelet, APM/DogStatsD transport, SCC service account), EKS Fargate profiles (sidecar injection and the Cluster Agent token), and AKS kubelet certificate handling. OKE, TKG, Rancher, and self-managed clusters use the baseline unchanged (apart from Rancher’s criSocketPath noted in Phase 1).

Product configuration blocks

The baseline already includes the three pillars. Merge the remaining blocks into spec.features only when the user selected the corresponding product.

APM trace intake (baseline):

apm:
  enabled: true

This enables the Agent receiver only. It does not install tracing libraries or modify application workloads.

APM Single Step Instrumentation and namespace scoping: use the Operator block under APM Single Step Instrumentation, library pinning, and namespace scoping in references/advanced.md.

Install the Datadog Agent in the separate datadog namespace. Single Step Instrumentation does not instrument applications in the Agent’s own namespace. Profiling is a separate user choice.

Declining APM - when the user explicitly opts out, replace the baseline APM block with:

apm:
  enabled: false

Logs collection (baseline):

logCollection:
  enabled: true

This enables the Logs Agent for annotated workloads. If the user wants logs from every container, add:

logCollection:
  enabled: true
  containerCollectAll: true

If the user explicitly opts out of logs, replace the baseline block with:

logCollection:
  enabled: false

Network Performance Monitoring (NPM):

npm:
  enabled: true

Universal Service Monitoring (USM):

usm:
  enabled: true

Live process collection:

liveProcessCollection:
  enabled: true

Cloud Security Posture Management (CSPM):

cspm:
  enabled: true

Software Bill of Materials (SBOM):

sbom:
  enabled: true
  containerImage:
    enabled: true
  host:
    enabled: true

Cloud Workload Security (CWS):

cws:
  enabled: true

App and API Protection (AAP) + SCA + IAST:

When AAP is selected, also add this annotation to the DatadogAgent metadata:

annotations:
  agent.datadoghq.com/appsec.injector.enabled: "true"

Enable the admission controller when any selected feature needs workload injection:

admissionController:
  enabled: true

Then include only the selected asm sub-blocks. AAP uses threats:

asm:
  threats:
    enabled: true

SCA:

asm:
  sca:
    enabled: true

IAST:

asm:
  iast:
    enabled: true

Data Streams Monitoring (DSM): enabled in the instrumented application via the tracer (DD_DATA_STREAMS_ENABLED=true), not through the DatadogAgent spec - there is no DSM feature flag in Agent config.

Service Discovery:

serviceDiscovery:
  enabled: true

Datadog Distribution of OpenTelemetry (DDOT) - replaces a side-car OTel collector:

otelCollector:
  enabled: true
  ports:
    - name: otel-grpc
      containerPort: 4317
      hostPort: 4317
    - name: otel-http
      containerPort: 4318
      hostPort: 4318

Kubernetes Autoscaling - the feature block merges into spec.features:

autoscaling:
  workload:
    enabled: true
eventCollection:
  unbundleEvents: true

Failover also requires DD_AUTOSCALING_FAILOVER_ENABLED=true on both the Cluster Agent and the Node Agent. That env lives under spec.override, not spec.features:

spec:
  override:
    clusterAgent:
      env:
        - name: DD_AUTOSCALING_FAILOVER_ENABLED
          value: "true"
    nodeAgent:
      env:
        - name: DD_AUTOSCALING_FAILOVER_ENABLED
          value: "true"

Prerequisites to verify before applying: Remote Configuration enabled at the org level and on the cluster’s Agents, Operator 1.16.0+, and an Agent version new enough for the scaling features in use (7.50+ for in-app recommendations, 7.66.1+ for live workload scaling).

FIPS mode: use the Operator block and the prerequisite checks under FIPS mode in references/advanced.md.

Private registry: use the Operator blocks under Private registries in references/advanced.md (per-component pull secrets, plus the admission controller registry when Single Step Instrumentation or EKS Fargate sidecar injection is enabled).

Apply

Show the completed manifests and the confirmed kubectl context, and say that confirming also creates the datadog-secret API-key Secret in datadog. Ask for confirmation once, immediately before this step. Nothing in the cluster holds the key until the user confirms; if they decline, create neither the Secret nor the DatadogAgent.

After confirmation, create the Secret and apply the CR in one block. The heredoc piped into kubectl apply -f - keeps the key off the command line, ps output, and shell history (--from-literal would put it in all three). The Secret exists only in this heredoc: never add it, an apiKey: field, or a ${DD_API_KEY} reference to datadog-agent.yaml or any other saved file, on any distribution (OpenShift included). On EKS Fargate, run the Fargate Secret block from Operator path in references/distributions.md in place of the Secret part, then apply the CR; it also writes the Cluster Agent token key and the workload-namespace copies.

for f in .env.local .env; do [ -f "$f" ] || continue; for k in DD_SITE DD_API_KEY DD_APP_KEY; do eval "[ -n \"\${$k:-}\" ]" && continue; v=$(grep -E "^$k=" "$f" | head -1 | cut -d= -f2- | tr -d '\r' | sed 's/^["'\'']//;s/["'\'']$//'); [ -n "$v" ] && export "$k=$v"; done; done
: "${DD_API_KEY:?not set - run dd-account-setup (commercial sites) or supply it directly (government sites)}"
kubectl apply -f - <<EOF || { echo "Secret creation FAILED - do NOT apply datadog-agent.yaml"; exit 1; }
apiVersion: v1
kind: Secret
metadata:
  name: datadog-secret
  namespace: datadog
type: Opaque
stringData:
  api-key: "${DD_API_KEY}"
EOF
kubectl apply -f datadog-agent.yaml

The Operator creates the DaemonSet, Cluster Agent, RBAC, and any ConfigMaps required by the features above. It reconciles continuously.

OpenShift-specific install

On OpenShift, install the Operator through the certified OperatorHub bundle instead of Helm, and create the Secret and DatadogAgent in openshift-operators. Follow OpenShift-specific install in references/distributions.md.

Phase 4: Helm Chart Install (Alternative)

Use this branch only if the user explicitly chose Helm without the Operator, or cannot install CRDs. Requires helm - if missing, ask the user to install it before proceeding.

helm repo add datadog https://helm.datadoghq.com
helm repo update

Build datadog-values.yaml from this baseline:

datadog:
  site: <DD_SITE>
  clusterName: <CLUSTER_NAME>
  tags:
    - "env:<DD_ENV>"
  apiKeyExistingSecret: datadog-secret
  apm:
    socketEnabled: true
  clusterChecks:
    enabled: true
  logs:
    enabled: true
  orchestratorExplorer:
    enabled: true

Leave registry unset in the baseline so the chart selects the site-appropriate image registry - GKE Autopilot requires its gcr.io variant, which the chart picks automatically but an explicit registry would override. Set registry only for a private mirror (see references/advanced.md).

Distribution overrides

Merge the values for the confirmed distribution and mode from Helm chart path in references/distributions.md: GKE Autopilot, OpenShift, EKS Fargate profiles (with the Cluster Agent token), AKS Windows nodes, and the AKS provider block.

Product values blocks

The baseline already includes the three pillars. Add the remaining values only when the user selected the corresponding product.

APM trace intake (baseline):

datadog:
  apm:
    socketEnabled: true

This enables the Agent receiver only. It does not install tracing libraries or modify application workloads.

APM Single Step Instrumentation, library pinning, and namespace scoping: use the Helm block under APM Single Step Instrumentation, library pinning, and namespace scoping in references/advanced.md.

Declining APM - when the user explicitly opts out, replace the baseline APM values with:

datadog:
  apm:
    socketEnabled: false
    portEnabled: false

Logs collection (baseline):

datadog:
  logs:
    enabled: true

This enables the Logs Agent for annotated workloads. If the user wants logs from every container, add:

datadog:
  logs:
    enabled: true
    containerCollectAll: true

If the user explicitly opts out of logs, replace the baseline values with:

datadog:
  logs:
    enabled: false

Network Performance Monitoring (NPM):

datadog:
  networkMonitoring:
    enabled: true

Universal Service Monitoring (USM):

datadog:
  serviceMonitoring:
    enabled: true

Live process collection:

datadog:
  processAgent:
    processCollection: true

Cloud Security Posture Management (CSPM):

datadog:
  securityAgent:
    compliance:
      enabled: true

Cloud Workload Security (CWS):

datadog:
  securityAgent:
    runtime:
      enabled: true

Software Bill of Materials (SBOM):

datadog:
  sbom:
    containerImage:
      enabled: true
    host:
      enabled: true

AAP, SCA, and IAST - enable the admission controller for any selected workload-injection feature:

clusterAgent:
  admissionController:
    enabled: true

For AAP, add:

datadog:
  appsec:
    injector:
      enabled: true
  asm:
    threats:
      enabled: true

For SCA, add:

datadog:
  asm:
    sca:
      enabled: true

For IAST, add:

datadog:
  asm:
    iast:
      enabled: true

DSM: enabled in the instrumented application via the tracer (DD_DATA_STREAMS_ENABLED=true), not through Helm values - the chart has no Data Streams Monitoring toggle.

Service Discovery:

datadog:
  discovery:
    enabled: true

DDOT collector:

datadog:
  otelCollector:
    enabled: true
    ports:
      - name: otel-grpc
        containerPort: 4317
        hostPort: 4317
      - name: otel-http
        containerPort: 4318
        hostPort: 4318

Autoscaling:

datadog:
  autoscaling:
    workload:
      enabled: true
  kubernetesEvents:
    unbundleEvents: true

The chart emits DD_AUTOSCALING_FAILOVER_ENABLED=true for both the Node Agent and Cluster Agent when workload autoscaling is enabled; do not duplicate it in the generic env lists. Prerequisites to verify before applying: Remote Configuration enabled at the org level and on the cluster’s Agents, and an Agent version new enough for the scaling features in use.

FIPS mode: use the Helm value under FIPS mode in references/advanced.md.

Private registry: use the Helm values under Private registries in references/advanced.md (per-component pull secrets, plus the admission controller registry when Single Step Instrumentation is enabled).

Install

Pin the chart version for a reproducible install (resolve the latest stable, then pass --version):

helm search repo datadog/datadog --versions | head

Show the values and confirmed kubectl context, and say that confirming also creates the datadog namespace and the datadog-secret API-key Secret. Ask for confirmation once, immediately before this step. Nothing in the cluster holds the key until the user confirms; if they decline, create neither the Secret nor the release.

After confirmation, create the namespace and Secret and install the chart in one block. The heredoc piped into kubectl apply -f - keeps the key off the command line, ps output, and shell history. On EKS Fargate, run the namespace line, then the Fargate Secret block from references/distributions.md in place of the Secret part, then the helm install; it also writes the Cluster Agent token key and the workload-namespace copies.

for f in .env.local .env; do [ -f "$f" ] || continue; for k in DD_SITE DD_API_KEY DD_APP_KEY; do eval "[ -n \"\${$k:-}\" ]" && continue; v=$(grep -E "^$k=" "$f" | head -1 | cut -d= -f2- | tr -d '\r' | sed 's/^["'\'']//;s/["'\'']$//'); [ -n "$v" ] && export "$k=$v"; done; done
: "${DD_API_KEY:?not set - run dd-account-setup (commercial sites) or supply it directly (government sites)}"
kubectl create namespace datadog --dry-run=client -o yaml | kubectl apply -f - || { echo "Namespace creation FAILED - stop"; exit 1; }
kubectl apply -f - <<EOF || { echo "Secret creation FAILED - do NOT install the chart"; exit 1; }
apiVersion: v1
kind: Secret
metadata:
  name: datadog-secret
  namespace: datadog
type: Opaque
stringData:
  api-key: "${DD_API_KEY}"
EOF
helm install datadog-agent datadog/datadog \
  --namespace datadog \
  --create-namespace \
  -f datadog-values.yaml \
  --version <AGENT_CHART_VERSION>

Terraform Install (IaC path)

Use this when the user manages the cluster (or their infra) with Terraform. Installing imperatively with helm/kubectl into a Terraform-managed setup leaves the Datadog install as drift that the next terraform plan won’t know about; put it in their IaC instead. Add these resources to the user’s existing Terraform project rather than creating a standalone one.

The API key never goes into Terraform. sensitive = true only redacts CLI output - the value still lands plaintext in terraform.tfstate. Create the Secret out-of-band with the same kubectl heredoc from Phase 3 and reference it by name from the CR.

Keep the install method chosen in Phase 2: the Operator helm_release for the Operator method, and the direct chart helm_release for the Helm method.

The providers, the Operator helm_release, the direct chart release, the two options for managing the DatadogAgent CR, and the sequenced apply are in references/terraform.md. Read it now and follow it in order.

Phase 5: Verify Installation

Check the namespace the Agent was actually installed into. That is datadog for the default Operator/Helm paths, but the OpenShift OperatorHub branch installs into openshift-operators - use that namespace for the commands below when following that path.

kubectl get pods -n datadog

All agent pods should be Running. Then check:

• Infrastructure List: <APP_BASE>/infrastructure - nodes appear within a few minutes.
• Containers: <APP_BASE>/containers - running pods/containers visible.
• Kubernetes Overview: <APP_BASE>/orchestration/overview/pod - cluster workloads.

Workload injection. Single Step Instrumentation and EKS Fargate sidecar injection only mutate Pods when they are created, so application Pods that were already running are not instrumented. If either was enabled, list the target workloads (Deployments, StatefulSets, DaemonSets in the selected namespaces, or the Pods labeled for Fargate injection), show the list, and ask before restarting them. Wait for the Agent Pods above to be Running first. After the user approves, restart each one and wait for it; stop on the first failure:

kubectl rollout restart <KIND>/<NAME> -n <NAMESPACE>
kubectl rollout status <KIND>/<NAME> -n <NAMESPACE> --timeout=300s

If the user prefers to redeploy through their own pipeline, tell them to do that instead. Then confirm injection on an application Pod: Single Step Instrumentation adds datadog-lib-*-init (or datadog-init-apm-inject) init containers, and Fargate injection adds a datadog-agent-injected container:

kubectl get pod <APP_POD> -n <NAMESPACE> -o jsonpath='{.spec.initContainers[*].name}{"\n"}{.spec.containers[*].name}{"\n"}'

Do not report APM or Fargate monitoring as working until an application Pod shows them.

If pods crash, dump logs (again, swap -n datadog for -n openshift-operators on the OpenShift OperatorHub path):

kubectl logs -n datadog -l app.kubernetes.io/name=datadog-agent --tail=50

Before pinning an Agent, Operator, or chart version, check the current Kubernetes compatibility matrix. Do not rely on a static minimum-version table in this skill.

Getting the Most Out of Your Agent

Once the agent is running, check for early metrics and show a widget.

Checking for metrics

Wait 30 seconds, then run - substitute the cluster name from Phase 1 for <CLUSTER_NAME>:

for f in .env.local .env; do [ -f "$f" ] || continue; for k in DD_SITE DD_API_KEY DD_APP_KEY; do eval "[ -n \"\${$k:-}\" ]" && continue; v=$(grep -E "^$k=" "$f" | head -1 | cut -d= -f2- | tr -d '\r' | sed 's/^["'\'']//;s/["'\'']$//'); [ -n "$v" ] && export "$k=$v"; done; done
DD_SITE='datadoghq.com'; export DD_SITE   # <- replace with the site confirmed in Phase 0.
# Explicit assignment, not ':=': a wrong non-empty DD_SITE in .env would otherwise survive.
: "${DD_API_KEY:?not set - run dd-account-setup (commercial sites) or supply it directly (government sites)}"; : "${DD_APP_KEY:?not set - run dd-account-setup (commercial sites) or supply it directly (government sites)}"
sleep 30
printf 'DD-API-KEY: %s\nDD-APPLICATION-KEY: %s\n' "$DD_API_KEY" "$DD_APP_KEY" |
curl -H @- -s -G "https://api.${DD_SITE}/api/v1/query" \
  --data-urlencode "from=$(($(date +%s) - 900))" \
  --data-urlencode "to=$(date +%s)" \
  --data-urlencode "query=avg:kubernetes.cpu.usage.total{kube_cluster_name:<CLUSTER_NAME>} by {host}"

Rendering the widget

If the series array is non-empty, render an ASCII chart from the real data:

• Use the pointlist values to plot the line, scaling Y-axis to actual min/max.
• Use box-drawing characters (╭, ╰, ─, │, ┤).
• List up to 3 hosts from each series scope at the bottom.

If the series array is empty, show this static preview:

┌─────────────────────────────────────────────────────────┐
│  kubernetes.cpu.usage.total        ▂▃▅▆▇▆▅▃▂▁▂▃▅▆▇█  │
│  100% ┤                                          ╭──╮   │
│   75% ┤                    ╭───╮              ╭──╯  │   │
│   50% ┤              ╭────╯   ╰──╮     ╭────╯     │   │
│   25% ┤    ╭────────╯            ╰────╯           │   │
│    0% ┤────╯                                       │   │
│       └────────────────────────────────────────────┘   │
│                                                         │
│  Metrics are on their way - check back in a few minutes │
└─────────────────────────────────────────────────────────┘
Containers: <APP_BASE>/containers

Confirm to the user that their agent is installed and data will appear shortly.

Useful links

• Kubernetes Overview: <APP_BASE>/orchestration/overview/pod - cluster health at a glance.
• Kubernetes Explorer: <APP_BASE>/orchestration/explorer - drill into pods, deployments, services.
• Cluster Map: <APP_BASE>/infrastructure/map?mapType=containers - visualize cluster topology.
• Containers: <APP_BASE>/containers - real-time view of all running containers.
• Monitors: <APP_BASE>/monitors/create - create alerts for pod restart loops, OOM kills, resource pressure.

If APM was enabled:

• APM: <APP_BASE>/apm/getting-started
• Service Map: <APP_BASE>/apm/map

If logs were enabled:

• Logs: <APP_BASE>/logs

Explore more:

• Integrations: <APP_BASE>/integrations
• Fleet Automation: <APP_BASE>/fleet
• Notebooks: <APP_BASE>/notebook

Important Notes

• The Operator is the recommended install method - it handles DaemonSet, Cluster Agent, RBAC, and reconciles drift. Reach for plain Helm only when the user can’t install CRDs.
• On GKE Autopilot, set spec.global.registry: gcr.io/datadoghq on the Operator path (the Helm path leaves registry unset so the chart selects it). Autopilot’s Warden admission webhook rejects the baseline registry.datadoghq.com images because its WorkloadAllowlist only covers the gcr.io/datadoghq digests. A DatadogAgent rejected by an admission webhook means the wrong registry, not a missing mirror.
• For restricted-network clusters, mirror the Operator, Agent components, and selected injection images. Configure Operator image.repository and imagePullSecrets, then set spec.global.registry and the per-component pullSecrets. Injection and Fargate workload namespaces need their own pull-secret access.
• APM Single Step Instrumentation is configured under apm.instrumentation. Namespace allowlists belong in a target’s namespaceSelector.matchNames; denylisted namespaces use disabledNamespaces.
• Install the Agent in its own dedicated namespace (datadog for the default Operator/Helm paths, openshift-operators on the OpenShift OperatorHub path), never one that also runs instrumented workloads, because Single Step Instrumentation does not instrument applications in the Agent’s own namespace.
• FIPS mode pins to the FIPS-validated image; some products (e.g. NPM via eBPF) may have reduced coverage in that build.