Google Cloud Introduces GKE Agentic Migration to Automate EKS Transfers

Google Cloud has released GKE Agentic Migration, an open-source agent plugin and local Model Context Protocol (MCP) server engineered to automate migrations from Amazon Elastic Kubernetes Service (AWS EKS) to Google Kubernetes Engine (GKE). The tool ingests source Infrastructure-as-Code (IaC) templates and Kubernetes manifests, uses a structured state graph to manage migration context, and outputs validated GKE landing zones, configuration runbooks, and pull requests directly into developer repositories. Cross-cloud container migration is notoriously fraught. While general-purpose AI coding assistants can draft manifest translations, raw LLM outputs frequently hallucinate non-existent attributes, fail to translate cloud-specific ingress or identity semantics (such as moving from AWS IAM Roles for Service Accounts to GKE Workload Identity), and break inter-manifest dependencies. Conversely, legacy backup and restore utilities preserve source cloud dependencies rather than translating them natively. GKE Agentic Migration solves this by coupling agent reasoning with deterministic compiler-style validation, ensuring every synthesized configuration complies with Google Cloud architectural rules before opening a pull request. This development fits into a broader industry movement where cloud providers are embedding agentic workflows and standard protocols like MCP into enterprise migration tooling. With enterprises consolidating workloads onto specific clouds to exploit localized AI acceleration—such as GKE's specialized GPU slicing, Cloud Storage FUSE optimizations, and dynamic compute classes—the friction of migrating off incumbent clouds has become the primary bottleneck to infrastructure modernization. By standardizing migration automation through transparent GitOps artifacts rather than opaque SaaS migration appliances, platform teams retain full auditability and infrastructure ownership. For platform and DevOps engineers, this tooling changes how multi-cluster workload conversions are planned and executed. Teams should begin by testing the MCP plugin against isolated staging namespaces to evaluate how effectively it maps proprietary AWS controller annotations to native GKE constructs. Although the automated generation of pull requests dramatically reduces the hours spent drafting boilerplate IaC, teams must still enforce strict automated linting and staging deployments to validate security policies, network routing, and volume claims under real-world traffic.