Beyond a Basic PaaS: How 8 Platforms Actually Govern AI Agents That Provision Infrastructure

AI agents that provision cloud infrastructure require governance beyond a basic PaaS, which typically lacks per-agent identities, pre-execution policy checks, approval gates, cost controls, and auditable links between actions, agent versions, and code commits. The comparison argues that no single product provides complete governance, recommending a three-layer architecture comprising identity and policy services such as AWS Bedrock AgentCore, Azure AI Foundry with Entra Agent ID, or Google Vertex AI Agent Engine; infrastructure control planes such as Upbound/Crossplane, HashiCorp Terraform with Sentinel and Vault, Pulumi with CrossGuard and ESC, or Red Hat OpenShift; and deployment/runtime platforms such as Qovery for environment isolation, RBAC, previews, and auto-stop. It identifies six required controls: scoped short-lived non-human identities, policy-as-code before deployment, human approval for high-risk actions, hard environment isolation, budgets plus TTL or auto-stop mechanisms, and tamper-evident audit trails. The recommended operational model is that agents propose changes through pull requests or resource claims while deterministic pipelines apply approved, policy-checked changes with brokered short-lived credentials, rather than allowing agents direct cloud API access or long-lived production keys. Platform selection should follow the organization’s hardest constraint, with native hyperscaler runtimes suited to single-cloud agents, declarative control planes suited to agent-driven provisioning, and cloud-agnostic runtime layers suited to multi-cloud, hybrid, or Kubernetes-based estates.

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