The reality is that most organizations do build their own feature flag solutions. After all, on the surface, it’s pretty basic functionality to build. And that’s what gets them: their pace of innovation outpaces their basic feature flag solution. That’s where Harness Feature Flags, with its intelligent and automated features, comes in.
Harness Feature Flags is a new approach to feature flagging that empowers developers to release features with high velocity and low risk by utilizing automated feature flag pipelines, machine learning-based verification, and a developer-first GitOps experience.
This article contains an excerpt from our eBook, The Basics: Feature Flags 101. If you like the content you see, stick around to the end where we’ll link the full eBook for you.
We designed Harness Feature Flags with developers in mind. We want to meet developers where they are by storing and managing flag configurations in Git as simple YAML files for easy versioning and audit across any preferred environments.
Because developers really are the arbiters of feature flags at an organization, it makes sense to make it easy for them to onboard and tie right into their existing workflows. Rather than have another tool to use, everything should be doable with code. Of course, for those who like adding a UI to the mix, Harness has just the thing.
Feature Flag Pipelines
Developers can build flexible pipelines piecing together flag changes with notifications, timing, approval, and more. They can standardize release processes using reusable feature flag pipeline templates. Feature flag pipelines can be created using the visual pipeline builder and step library, and also by using config-as-code.
By using feature flag pipelines, developers can automate feature rollouts and reduce the time to go from commit to production. If you use other Harness modules like CI or CD, you can also extend those same pipelines into Feature Flags to further simplify software deployment.
Harness Feature Flags also makes use of the same machine learning built into the Harness platform to automate progressive delivery.
Automated Feature Verification
Automating progressive delivery with machine learning-based feature verification is a big part of the ease of use of Feature Flags. Harness can pull in all of the monitoring metrics that you use to automate feature or service verification and kick off remediation processes in case of any abnormalities. With feature flags, usually that’s as simple as turning it off.
On the other hand, if you want to automate feature rollout across subsets of your user base, Harness can also use those same verifications to progressively deliver the feature. In this way, developers get the same level of control with feature flags as they’re used to with their deployments. In the end, simplifying the developer workflow is what it’s all about.
This was an extremely short and sweet look into three unique intelligent Harness Feature Flags features. Over the coming weeks, we’ll build out full blog posts for each feature and link them back here.
We hope you got an inkling of how powerful feature flags can be – and how they’re not for “just” the typical use case. As we’ve stressed in the past – and continue stressing – feature flags pack a punch! We’re especially excited for the Feature Flags Pipelines feature. Making feature flags an integral part of your CI/CD pipeline is the next logical step – and it’ll allow for even better metric collection. Even before that, Feature Flag Pipelines combine all of the typical pieces of a feature release into a step-based workflow – scheduling releases, ensuring approvals, verifying performance… you name the step in the process, we support your ability to set it up and automate.
Are you ready to learn more about feature flags? If so, download the full eBook here: The Basics: Feature Flags 101.