evolve-scale

To scale a single-host AICP deployment to a multi-host fleet, you plan and execute architecture-level changes that extend coordination across multiple machines. This involves deploying per-host agent servers, clustering LocalAI instances, and replicating DLQ and state.

Orchestrating multi-operator concurrency across multiple hosts requires extending AICP to a fleet architecture. This is achieved by deploying per-host agent servers and replicating state and DLQ to support coordinated multi-operator workflows safely.

Yes, extending AICP to a multi-host fleet requires replicated DLQ and state. This replication is necessary to support multi-operator concurrency and ensure coordinated LocalAI clusters across the expanded architecture.

You can coordinate LocalAI clusters across multiple machines by applying the evolve-scale workflow. It plans architecture evolution to extend AICP from a single host to a fleet, enabling cross-host LocalAI clustering and per-host agent servers.

Scaling architecture without a phase-driven rollout risks unsafe deployment and invalid state surfaces. The evolve-scale approach mandates validating each phase before rollout and documenting topology changes to ensure secure multi-host extension.