StackGen Extends AI Platform for Generating IaC Code to Argo CD Platform
StackGen, formerly appCD, has added an ability to automatically generate Helm charts that are used to deploy cloud-native applications using the open-source Argo continuous delivery (CD) platform running in Kubernetes environments.
Cesar Rodriguez, vice president of engineering for StackGen, said this extension to the generative artificial intelligence (AI) platform the company developed to automatically write code, is needed to programmatically configure IT infrastructure, otherwise known as infrastructure as code (IaC).
Instead of mastering a specific IaC tool, the StackGen platform applies static analysis to Python or Java code to understand intent, identifies dependencies and then infers the application programming interface (API), service configuration, ingress/egress and other variables required. Terraform code or Helm charts are then automatically generated based on the static analysis of the code that has been reinforced using AI learning techniques.
Working with the open-source Argo CD community, that capability has now been extended to include Helm charts that DevOps teams can now use to programmatically configure infrastructure, said Rodriguez. StackGen also ensures that the generated Helm charts remain synchronized with application source code, allowing organizations to more easily deploy applications at scale in a way that also increases productivity.
That approach makes it possible to consistently create secure code for provisioning IT infrastructure versus requiring developers, with little to no cybersecurity, to manually write it. That’s critical because IaC code is often one of the primary root causes of a cloud services breach when it is later discovered that the code manually written inadvertently created a misconfiguration that cybercriminals later exploited. By adhering to established security standards, such as least-privilege access control, StackGen automatically enforces critical security policies within the Helm charts.
Argo has been gaining traction among DevOps teams that are embracing best GitOps practices to automate the CD process in a way that is not dependent on a specific continuous integration (CI) workflow. DevOps teams that embrace that methodology are trying to enable developers to manage the CI process themselves, in a way that better isolates CD workflows typically managed by software engineers.
It’s not clear how many organizations have embraced Argo, but they typically tend to be DevOps teams that prefer to have direct control over how DevOps platforms are deployed and managed, noted Rodriguez. Regardless of approach, however, IaC code is just another example of how generative AI platforms are transforming the way DevOps workflows are managed. A recent Techstrong Research report finds a third (33%) are working for organizations that already make use of AI to one degree or another to build software, while another 42% are considering it. Only 6% said they have no plans to use AI.
The challenge, as always, is not just mastering any emerging technology but ultimately changing the DevOps culture. While it’s still early as far as the adoption of AI tools and platforms is concerned, there is little doubt they won’t be pervasively employed to build and deploy applications. The challenge now is finding a way to safely achieve that goal.