How to give your AI agent a secure computer

AI agents require isolated execution environments when tasks involve flexible code, files, repositories, browsers, dependencies, or long-running processes, whereas narrowly authorized APIs are preferable for simpler operations. A secure architecture separates model orchestration and independent policy enforcement from the runtime, exposes typed lifecycle and file operations, uses controlled images and fixed resource classes, defaults to ephemeral storage, and applies task-scoped credentials, restricted network access, audit telemetry, and operator-controlled containment. Persistent environments may support resumed work but require explicit ownership, retention, scanning, and deletion controls, while governance should document authorization, identity, supply chain, data access, resource limits, networking, and lifecycle events. The guide presents Northflank Sandboxes as one implementation option, offering API-managed isolated environments on its cloud, compatible customer cloud or VPC deployments, and some on-premises configurations, with execution based on container isolation technologies that vary by infrastructure. It recommends waiting for sandbox readiness before running commands, collecting outputs and artifacts through controlled interfaces, and pausing or deleting environments after tasks complete, while emphasizing that safety must be tested against prompt injection, malicious dependencies, privilege escalation, exfiltration, resource exhaustion, and sandbox escape attempts.

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