Which Platforms Enforce Policy Guardrails When AI Agents Provision Cloud Infrastructure?

AI agents provisioning cloud infrastructure require layered guardrails because no single platform fully controls identity, cost, security, runtime behavior, provisioning scope, and auditing. The recommended approach combines policy engines such as Open Policy Agent or Styra, IaC governance tools including Spacelift, env0, HCP Terraform with Sentinel, or Pulumi CrossGuard, runtime AI security products such as Operant AI, constrained internal developer platforms such as Qovery, Humanitec, or Port, and cloud-native controls like AWS SCPs, budgets, quotas, Azure Policy, GCP Organization Policies, and Kubernetes admission controls. The central argument is that narrowly scoped, template-based provisioning APIs are more reliable than broad cloud credentials, since they prevent agents from expressing unsafe or expensive infrastructure requests rather than merely detecting violations afterward. Effective budget controls combine plan-time cost checks, provisioning-time caps, cloud-side backstops, and automatic cleanup of idle resources, while security requires distinct non-human identities, short-lived OIDC-federated credentials, approval gates for sensitive changes, deny-by-default templates, and admission policies. Reliable governance also depends on immutable, correlated audit records linking each agent identity, request, policy decision, approval, and resulting cloud change. Qovery is positioned as the constrained provisioning layer, offering scoped API access, environment templates, per-environment RBAC, preview environments, auto-stop, and deployment history while relying on complementary policy, runtime, and cloud-native controls for full coverage.

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