Act as a DevOps architect and recommend a CI/CD setup for a team that deploys to Kubernetes, needs manual approvals for prod, and wants automated rollback on failure.

Here’s a solid CI/CD setup for a team deploying to Kubernetes with manual production approvals and automated rollback on failure.

Recommended approach

Use a GitOps-based deployment model with a CI pipeline for build/test and a CD pipeline for progressive delivery to Kubernetes.

Suggested stack

• CI: GitHub Actions / GitLab CI / Jenkins
• Artifact registry: ECR / GCR / ACR / Harbor
• CD/GitOps: Argo CD
• Progressive delivery / rollback: Argo Rollouts
• Packaging: Helm or Kustomize
• Secrets: External Secrets Operator or sealed secrets
• Policy/quality gates: OPA Gatekeeper / Kyverno, image scanning (Trivy), SAST/DAST as needed

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Target flow

1) Developer commits code

• Push to feature branch / PR.
• CI runs:
  • lint
  • unit tests
  • build container image
  • security scan
  • integration tests if applicable
• On merge to main:
  • build immutable image tagged with commit SHA
  • push to registry
  • update deployment manifest repo or Helm values repo

2) Deploy to lower environments automatically

• Argo CD syncs changes to dev/staging
• Validate:
  • smoke tests
  • health checks
  • API checks
  • basic synthetic tests

3) Production requires manual approval

Two common options:

Option A: Manual approval in CI

• After staging passes, pipeline pauses at a manual approval gate
• Approved release updates prod GitOps repo
• Argo CD syncs prod

Option B: Manual promotion via GitOps PR

• Pipeline opens a PR against the prod environment repo
• Human approves and merges
• Argo CD deploys automatically after merge

Recommendation: Option B is usually better for auditability and traceability.

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Automated rollback strategy

Use Argo Rollouts with Kubernetes readiness/health checks and progressive traffic shifting.

Rollout pattern

• Deploy new version as canary or blue/green
• Send a small percentage of traffic first
• Measure:
  • pod readiness
  • error rate
  • latency
  • custom app metrics from Prometheus
• If metrics fail or rollout analysis fails:
  • automatically abort rollout
  • rollback to previous stable ReplicaSet

Why this is better than plain Deployment rollbacks

Standard Kubernetes Deployments can rollback, but they do not do intelligent traffic-based progressive delivery. Argo Rollouts gives:

• automated canary analysis
• controlled exposure
• automatic rollback on metric failure
• integration with Prometheus, Datadog, Kayenta, etc.

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Recommended pipeline design

CI pipeline stages

1. Validate
   • format/lint
   • unit tests
2. Build
   • build container image
   • tag with commit SHA and semantic version
3. Scan
   • dependency scan
   • image vulnerability scan
4. Publish
   • push image to registry
5. Deploy to dev/staging
   • update GitOps repo or Helm chart values
   • Argo CD syncs automatically
6. Verify
   • smoke tests
   • integration tests
7. Manual approval
   • approval gate or PR merge to prod
8. Production rollout
   • Argo Rollouts canary/blue-green
9. Post-deploy monitoring
   • alerting and automated rollback on failed analysis

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Environment model

Use separate environments:

• dev
• staging
• prod

Recommended best practice:

• separate namespaces at minimum
• ideally separate clusters for prod vs non-prod if compliance/risk warrants it
• separate GitOps overlays per environment

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Rollback implementation details

You should define rollback triggers from:

• failed readiness/liveness probes
• failed canary analysis
• increased 5xx rate
• latency thresholds exceeded
• custom business metrics dropping

Example:

• route 10% traffic to new version
• wait 5–10 minutes
• compare error rate and latency
• if metrics exceed thresholds, automatically abort

Also keep:

• last known good image/version
• immutable image tags
• deployment history in Git and cluster controller

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Security and governance

Add these controls:

• signed images with cosign
• least-privilege service accounts in Kubernetes
• RBAC for manual approvals
• audit logs for promotion events
• policy checks before prod deploy
• secret scanning in CI
• container vulnerability scanning before publish

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High-level architecture

Developer PR
   -> CI (test/build/scan)
   -> push image to registry
   -> update GitOps repo
   -> Argo CD deploys to dev/staging
   -> tests pass
   -> manual approval or PR merge for prod
   -> Argo CD syncs prod
   -> Argo Rollouts canary/blue-green
   -> automated analysis
   -> success = promote
   -> failure = rollback automatically

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My recommendation in one sentence

If you want a modern, reliable setup: GitHub Actions/GitLab CI for CI, Argo CD for GitOps deployments, Argo Rollouts for canary/blue-green with automated rollback, and a manual approval gate via PR merge or pipeline approval before production.

If you want, I can also provide:

1. a reference architecture diagram,
2. a sample GitHub Actions + Argo CD workflow, or
3. a Terraform/Kubernetes implementation blueprint.