Cube Protocol

A 3D semantic memory and structured data format for multi-agent AI systems.

Overview

Cube Protocol is a semantic memory protocol that compresses, structures, and labels data into reversible 3D “cubes”. These cubes act as shared packets between agents, workflows, and domains, allowing AI systems to carry state forward in a predictable, testable way.

Core Capabilities

• Reversible semantic memory packets
• Multi-layer compression and encoding
• Cross-agent and cross-domain interoperability
• Hash-verified integrity and tamper detection
• Token-efficient AI-to-AI communication

Semantic Descriptor Pattern

Each cube is tagged with a semantic descriptor:

DOMAIN | SEQUENCE | OUTCOME

This pattern communicates what the cube contains, how it was created, and what the downstream agent is expected to do with it.

Compression & Structure

Typical Cube Protocol encoding applies:

• Type-aware preprocessing
• gzip deflation of preprocessed data
• base64 encoding for transport safety
• Geometric decomposition into a cube-shaped structure

Reconstruction Engine

Every cube is designed to be fully reversible. The reconstruction engine restores the original data exactly, verifying the hash and descriptor to ensure integrity and semantic alignment.

Use Cases

• Agentic systems that need persistent memory across tasks and tools
• Scientific and imaging data that benefit from structured, reversible packaging
• Complex business workflows where state must be portable, debuggable, and compressible
• Semantic logging and replay of AI reasoning processes

Why I Built Cube Protocol

As agentic systems became more capable, it was clear they lacked a shared, structured memory substrate. Cube Protocol is my answer to that gap: a way to make agent memory explicit, inspectable, and portable across domains and architectures.