AI Agents and Cloud Migration: The 4 Stages They Actually Help With (And the 5 They Break)

AI agents can assist cloud migrations most reliably with discovery and dependency mapping, code and infrastructure-as-code translation, characterization test generation, documentation, and post-migration cost analysis, where they produce reviewable reports, diffs, or plans. They remain poorly suited to high-context decisions such as target architecture, data cutover, network and identity design, compliance accountability, and defining the operating model, which require human ownership. The recommended control model is “propose-approve-apply,” using read-only agent credentials, pull requests, policy-as-code checks, staged validation in ephemeral environments, approval requirements scaled to production risk, immutable audit trails, and deterministic rollback rather than direct production access. The article argues that no single migration tool covers every task, so teams commonly combine assessment services, code assistants, Terraform and policy tools, and a developer platform such as Qovery or a self-managed Backstage, Argo CD, and Terraform stack. Although vendors report substantial productivity improvements, independent research suggests outcomes vary and can be offset by review, remediation, and incident costs, making net benefit dependent on governance and validation. Lasting modernization also requires a post-migration self-service developer path, standardized deployment practices, cost controls, lifecycle ownership, and RBAC, while regulated or air-gapped deployments require particular attention to model hosting, prompt data residency, and comprehensive auditing.

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