I don’t quite get what makes it Kubernetes for AI agents. Is the idea to pool hardware together to distribute AI agents tasking? Is the idea to sandbox agents in a safe runtime with configuration management? Is the idea something else entirely? Both? I couldn’t figure it out by the README alone.

Mostly the second, plus fleet management. Each agent runs in an isolated namespace with its own config, channels, and skills. You manage them declaratively like you would pods, but the unit of work is an AI agent instead of a container. The Kubernetes analogy is about the operational model: clusters for org isolation, namespaces for team isolation, declarative deploys, central monitoring. Not about hardware scheduling. I'll improve the README to make this clearer, good feedback.