The Thesis
Today’s AI systems are optimised for single-turn performance, not for long-running, multi-agent reliability. The result: drift, breakage, and unrecoverable state loss.
PCS explores a neutral stability layer that helps AI systems stay coherent, recoverable, and auditable — across time, vendors, and environments.
STATE · DELTA · STABILITY
AI workflows fail silently. State is lost. Models drift. No infrastructure layer exists to preserve continuity across distributed, long-running autonomous operations.
Orchestration routes tasks. Observability logs results. Neither preserves state, detects drift, or coordinates recovery across heterogeneous agents and environments.
PCS is a neutral stability and synchronisation layer — not a model, not an orchestrator. It preserves state, reduces drift, and coordinates across heterogeneous systems.