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cloud-native release engineering
Implementing CI/CD for Cloud-Native Applications the Right Way
Learn how to implement CI/CD for cloud-native applications the right way with immutable builds, container-native testing, declarative deployments and progressive delivery ...
Khushi Jitani
|
December 3, 2025
|
Argo CD GitOps
,
CI/CD for microservices
,
CI/CD metrics DORA
,
CI/CD pipeline reliability
,
CI/CD security scanning
,
cloud-native CI/CD
,
cloud-native DevOps
,
cloud-native release engineering
,
configuration drift prevention
,
container-native testing
,
continuous delivery Kubernetes
,
declarative deployments Kubernetes
,
DevOps pipeline optimization
,
GitOps pipelines
,
IaC validation CI/CD
,
immutable build artifacts
,
Kubernetes CI/CD best practices
,
Kubernetes deployment automation
,
Kubernetes rollout testing
,
microservices deployment strategies
,
progressive delivery canary blue-green
×
AI in CI/CD: Where Are You Really?
Step
1
of
7
14%
How would you describe your organization’s current level of AI adoption within your CI/CD pipeline?
(Required)
No active use of AI in CI/CD
Experimenting with AI in isolated areas (e.g., code suggestions, testing)
Limited production use in specific pipeline stages
Broad adoption across multiple stages of the pipeline
AI is fully integrated and operational across the CI/CD lifecycle
Not sure / don’t know
How would you describe your current CI/CD environment?
(Required)
Primarily legacy / Jenkins-based with custom integrations
Jenkins-centered with some modern tooling layered in
Hybrid environment with multiple CI/CD platforms and toolchains
Mostly modern, managed CI/CD platforms
Fully integrated, platform-driven architecture
Not sure / don’t know
In which areas of your software delivery pipeline are you currently using AI? (Select all that apply)
(Required)
Code generation / developer assistance
Automated testing / test generation
Build and pipeline optimization
Deployment automation
Monitoring and incident response
Security / compliance automation
We are not currently using AI in our pipeline today
In which areas of your software delivery pipeline are you considering using AI? (Select all that apply)
(Required)
Code generation / developer assistance
Automated testing / test generation
Build and pipeline optimization
Deployment automation
Monitoring and incident response
Security / compliance automation
Which AI use cases are delivering, or do you expect to deliver, measurable value in your CI/CD pipeline? (Select up to three)
(Required)
Faster development cycles
Improved code quality
Reduced testing effort
Improved reliability / fewer incidents
Reduced operational costs
None of these
Not sure / cannot assess
What, if anything, is limiting your organization’s progress with AI in CI/CD? (Select up to three)
(Required)
Fragmented toolchains
Lack of integration
Lack or unavailability of high-quality data
Data silos across pipeline stages
Security or governance concerns
Lack of internal expertise or skills
Unclear ROI or business case
Difficulty integrating AI into existing workflows
Performance or reliability concerns
Organizational resistance to change
Nothing is limiting my organization’s progress with AI in CI/CD
Not sure / don’t know
How do you expect your use of AI in CI/CD pipelines to change over the next 12–18 months?
(Required)
No plans to adopt AI
Continuing experimentation only
Expanding use in selected areas
Scaling across multiple pipeline stages
Making AI core to delivery strategy
Δ
×