How AI Agents Actually Help With Cloud Migration and Infrastructure Modernization (And Where They Still Need Guardrails)

AI agents can accelerate cloud migration and infrastructure modernization by automating discovery, dependency mapping, code and database refactoring, infrastructure-as-code drafting, test generation, and ongoing cost optimization. Their strongest use cases involve reading and rewriting artifacts such as repositories, SQL, Terraform, Kubernetes manifests, documentation, and tests, while production-changing actions—including data cutovers, DNS updates, traffic shifts, and rollbacks—still require human approval. The main risks are incomplete discovery of undocumented dependencies, convincing but flawed configurations such as overly broad permissions or incorrect networking, inconsistent outputs between runs, excessive provisioning costs, and large security risks from long-lived administrative credentials. Recommended safeguards include narrowly scoped short-lived identities, plan-and-approve workflows, ephemeral preview environments, policy-as-code checks, full audit logging, reversible changes, and standardized deployment processes. The article distinguishes migration and code-transformation tools from the runtime platform needed to deploy and operate the resulting artifacts, presenting Qovery as a multi-cloud, bring-your-own-cloud deployment and guardrail layer alongside tools from AWS, Microsoft, IBM, Google, and HashiCorp. It concludes that agents offer the clearest value for large, repetitive legacy modernization efforts but shift the bottleneck toward review, testing, platform readiness, and careful human governance.

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