End-to-End Agentic Migrations to GCP: What AI Agents Automate, and Where They Break

Agentic migration to Google Cloud Platform is presented as a gated workflow in which AI agents automate repetitive tasks such as estate discovery, dependency mapping, Terraform and Kubernetes manifest generation, service validation, and post-migration cost and configuration optimization, while humans retain authority over data cutover, network and identity design, compliance signoff, and destructive actions. The proposed practical target for 2026 is automating 60–80% of migration toil rather than achieving fully autonomous production migrations, due to risks including inaccurate infrastructure configurations, excessive IAM permissions, missed stateful dependencies, regional or cost errors, and insufficient reliability of coding agents. The recommended process moves from workload-specific scope selection and agent-led discovery through architect-approved target design, policy-checked infrastructure generation, isolated preview-environment testing, human-managed data migration using services such as Database Migration Service and Storage Transfer Service, and ongoing optimization. It emphasizes safeguards including least-privilege service accounts, separate projects by environment, approval gates, policy-as-code, cost estimates, audit logs, rollback plans, and measurable operational exit criteria. Google tools such as Migration Center, Migrate to Containers, Gemini Cloud Assist, and database migration services support assessment and implementation, while the article promotes Qovery Skills as an execution layer that connects coding agents such as Claude Code, Cursor, and Codex to scoped deployment workflows across GCP and other cloud platforms.

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