What infrastructure do AI agents need to run code safely?

Safe AI-agent code execution requires a policy-controlled architecture that separates model orchestration and authorization from untrusted runtime environments, rather than relying on containers alone. The proposed eight-layer approach covers external policy decisions, sandbox lifecycle management, isolation and resource limits, secure software supply chains, explicit state and artifact handling, scoped identities and restricted networking, fleet-level quotas and cleanup, and correlated audit evidence with independent containment controls. Execution requests should be authenticated, approved, provisioned with predefined limits, given temporary credentials only when needed, monitored, and fully cleaned up after approved artifacts are exported. Code produced in a sandbox should move to production only through a separate, reproducible release process with testing, review, monitoring, and rollback mechanisms. Northflank is presented as a platform offering sandbox environments, Kubernetes-oriented workload infrastructure, networking, storage, audit tools, deployment workflows, and options for managed cloud, customer cloud accounts, or eligible self-operated Kubernetes clusters, while emphasizing that organizations remain responsible for configuring policies, authorization, lifecycle rules, and data-residency considerations.

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