agentprivacy-holonic-architect

Identity-independent data structures separate data identity (GUID) from storage location and principal identity (DID/VRC), ensuring AI agent state survives across different providers and environments. This Skill designs these structures using holonic architecture patterns.

Multi-provider persistence layers implement robust data persistence strategies across diverse backends like IPFS, MongoDB, Zcash, and Ethereum. This Skill builds these layers using patterns like HyperDrive to ensure data longevity and cross-environment state survival.

Shared-parent architectures enable O(1) scaling by structuring collective entities like guilds and intel pools under common parents, allowing efficient scaling without pairwise coupling. This Skill designs and implements these holonic patterns for AI agent infrastructure.

Privacy-aware routing is satisfied by strictly separating data identity, relationship structures, and principal identities (DID/VRC) within the holonic architecture. This Skill designs systems that enforce these separation of concerns boundaries.

Holonic architecture is needed when AI agent state must survive TEE rotations, chain migrations, or provider failures while maintaining distinct operational boundaries. This Skill solves these data longevity and cross-environment integrity challenges.

This approach works for multi-agent systems by designing persistent memory where agents with different operational focuses maintain separate state boundaries. The Skill uses shared-parent patterns to structure collective data without coupling individual agent states.