Cloudythings Blog

Continuous Verification as a First-Class Pipeline Stage

Operationalizing continuous verification with automated experiments, policy engines, and real user signals in CI/CD pipelines.

May 06, 2025 at 09:11 AM EST 12 min read
Continuous VerificationCI/CDObservabilitySREExperimentation
Engineers reviewing continuous verification results on a dashboard
Image: Sincerely Media / Unsplash

“Release, observe, hope” is not a strategy. Continuous verification (CV) brings production-like checks into the deployment pipeline. Companies like Harness, Adobe, and Intuit have documented huge incident reductions by treating verification as code. This article shares our playbook for integrating CV into GitOps pipelines.

Compose verification hypotheses

Every change declares hypotheses:

  • Performance targets (latency, error rate).
  • Business metrics (conversion, retention).
  • Guardrails (no regression in accessibility or carbon footprint).

Hypotheses live in YAML next to application manifests. CI pipelines parse them and configure verification jobs automatically.

Team designing verification hypotheses on whiteboards
Photo by Teemu Paananen on Unsplash. Verification starts with clarity.

Automate experiment orchestration

Pipelines trigger:

  • Synthetic traffic (k6, Locust) or recorded sessions (SpeedCurve).
  • Chaos experiments (Gremlin, Litmus) targeting dependencies.
  • Feature flag rollouts via LaunchDarkly/OpenFeature for controlled exposure.

Results feed into an analysis service (Keptn, Kayenta) that compares metrics against baselines. Verdicts (pass, warn, fail) return to the pipeline for gating decisions.

Integrate with observability

Verification queries pull data from:

  • APM/trace systems (Honeycomb, Datadog).
  • Real user monitoring (RUM) tools.
  • Business analytics (Amplitude, Looker) via APIs.

We use OpenTelemetry to tag verification traffic so dashboards slice results easily.

Close the loop

Failures create Jira/PagerDuty tickets with evidence. Developers iterate using the same hypotheses until verification passes. Over time, the library of hypotheses becomes institutional knowledge.

Continuous verification catches issues before customers ever notice. Treat it as a first-class pipeline stage, and reliability becomes proactive instead of reactive.