clawdapus

You can deploy AI agent containers with Docker by using the Clawdapus CLI and configuration files to orchestrate reproducible workloads. This enforces cost containment and secure identity management through an integrated governance proxy.

Infrastructure-layer governance treats AI agents as untrusted workloads to ensure secure deployment. You need it when deploying agents that require strict identity projection, inspectable actions, and controlled resource consumption.

Yes, CLI orchestration supports defining agent behavioral contracts and managing model slots. This allows you to control operational behavior and switch underlying models while maintaining governed, reproducible agent states.

To launch a containerized AI agent, define your deployment parameters in a claw-pod.yml file and execute the build and launch sequence via the CLI. This creates an inspectable, killable Docker container running your agent.

Yes, the deployment approach handles platform identity projection and allows external API access. A governance proxy intercepts communication to log actions, monitor costs, and ensure the agent operates within its defined identity boundaries.

Treating AI agents as untrusted workloads requires routing all communication through a governance proxy, which adds infrastructure overhead. You must define explicit infrastructure surfaces and skill mounts to grant necessary operational control without bypassing security boundaries.