Most AI systems today rely on probabilistic recall: RAG, embeddings, and prompt-based memory.

This makes it hard to enforce invariants, audit facts, or maintain a clear separation between reasoning and ground truth.

I built a minimal proof-of-concept showing a different approach: a deterministic symbolic memory layer accessible via MCP.

Instead of storing “memory inside the model”, knowledge is resolved just-in-time from an explicit symbolic layer.

The goal is not to replace RAG or assistant memory, but to provide a missing infrastructure layer: a controllable knowledge backbone for AI systems.

This repo demonstrates the minimal viable form of that idea.