CogniMesh Architecture

CogniMesh is designed as a distributed system where experts own their knowledge nodes and consumer AI agents discover those nodes through a mesh.

flowchart LR
  Expert[Domain expert] --> Interviewer[AI Interviewer Agent]
  Interviewer --> Notes[Structured notes and metadata]
  Notes --> VectorDB[Local vector DB]
  VectorDB --> Endpoint[RAG endpoint]
  Endpoint --> Discovery[P2P service discovery mesh]
  Discovery --> Agent[Consumer AI agent]
  Agent --> Gateway[Payment and rating gateway]
  Gateway --> Endpoint

Components

Knowledge providers operate or authorize a local knowledge node.
AI Interviewer helps extract concepts, source notes, and gaps from expert interviews.
Local vector database / RAG endpoint stores embeddings and exposes a query interface.
Service discovery mesh announces categories, availability, metadata, and quality signals.
Consumer AI agents discover and query endpoints that match task context.
Payment/rating gateway records usage, compensation model, and provider reputation.

Boundaries

The architecture is intentionally implementation-neutral at this stage. Grenache.js and Vert.x are candidate technologies for discovery experiments, but the protocol should remain understandable without binding every concept to one runtime.