World-Building with Persistence: Narrative Layers in AI Agents

Standard AI models are great at vibes, but terrible at truth. You can tell an agent that the sky is toxic and the main character is a debt-ridden deck-runner — but three sessions later, that context has drifted. The agent starts hallucinating a blue sky and a rich hero. This happens because most memory systems treat “The Plot” the same as “The Last Chat Message.” Everything lands in a single flat context bucket, and the most recent tokens always win. VEKTOR solves this with Narrative Partitioning — organizing your agent’s history into four logical layers using the MAGMA graph and metadata tags. Each layer has different retrieval rules, different persistence guarantees, and a different role in your agent’s cognition. This is your baseline. Facts that should never be forgotten or pruned. The axioms of your universe — the laws of physics, the political factions, the state of the sky. Store with importance: 1.0 and layer: “world”. High-importance nodes are protected from the REM consolidat