Enterprises are increasingly standardizing on Google Kubernetes Engine (GKE) to run their most critical and AI-driven workloads. From Cloud Storage FUSE for high-throughput data access to custom compute classes (CCC) and advanced GPU slicing, GKE provides the scale and efficiency required for modern applications.

However, migrating complex Kubernetes environments from AWS EKS to GKE has traditionally been a daunting, high-friction engineering endeavor. Your platform teams must manually dissect sprawling infrastructure-as-code (IaC), navigate cloud-specific architectural differences, and build custom translation scripts.

While your engineering teams often experiment with general-purpose LLMs to draft conversions, ad-hoc prompting quickly can become an operational trap. Raw models hallucinate non-existent resource properties, drop critical network or identity configurations, and lose context across interdependent files. The time platform engineers spend auditing, untangling, and debugging model errors ends up cannibalizing any upfront speed gains, creating manual toil and unpredictability.

Today, we are excited to announce the open-source release of GKE agentic migration, a purpose-built agent plugin that replaces brittle, ad-hoc prompting with an AI-assisted migration pipeline protected by deterministic guardrails.

“For large enterprise clients, the biggest barrier to cloud modernization is execution risk and unpredictability. Unlike raw chat prompts that lose context and hallucinate configurations, Google’s GKE agentic migration pairs the speed of generative AI with the deterministic guardrails enterprises need: structured state persistence, multi-persona boundaries between platform and app teams, and non-negotiable human approval gates. It gives our global engineering practice a provable, compiler-grade migration factory that slashes delivery risk.- Rahul Shrivastava , EVP, Persistent

When talking to customers about their infrastructure migration journeys, we consistently hear about several governance challenges:

The automation trust gap: Refactoring Kubernetes configurations manually can be agonizingly slow. Yet, using generic AI coding assistants introduces unacceptable risk. Standard LLMs can hallucinate infrastructure code, use deprecated API fields, or omit critical security rules. Generating code that is "almost right" simply shifts the bottleneck from writing code to debugging it.

The danger of live cluster mutability (ClickOps): Legacy migration tools often connect directly to live clusters and deploy via API calls. This bypasses the organization's Git repository (the true source of truth), breaks CI/CD pipelines, and makes rollbacks incredibly difficult.