Wormhole Protocol Research

Status: Archived Research
Category: Academic / Thesis Material
Priority: None (research only)

Overview

The Wormhole protocols (WSP and WAP) represent exploratory research into cross-chain AI skill coordination. This work is not operational and is archived here for reference.

Wormhole Skill Protocol (WSP)

Concept

WSP explores how AI skills (tentacles) could be invoked across trust boundaries:

┌─────────────────┐                    ┌─────────────────┐
│   Agent A       │                    │   Agent B       │
│   (Claude)      │◄──── Wormhole ────►│   (GPT-4)       │
│                 │                    │                 │
│  ┌───────────┐  │                    │  ┌───────────┐  │
│  │ Skill X   │  │                    │  │ Skill Y   │  │
│  └───────────┘  │                    │  └───────────┘  │
└─────────────────┘                    └─────────────────┘

Research Questions

  1. Capability Attestation — How does Agent A prove it has Skill X?
  2. Result Verification — How does Agent B verify Skill X’s output?
  3. Trust Negotiation — What trust model governs cross-agent calls?
  4. Billing/Metering — How are resources accounted across boundaries?

Proposed Mechanisms

# WSP Request (theoretical)
wsp_request:
  version: 1
  source:
    agent_id: agent-a-uuid
    attestation: <signed capability proof>
  target:
    skill: "senior-backend"
    version: "^1.0"
  payload:
    task: "Review this API design"
    context: <encrypted payload>
  constraints:
    max_tokens: 10000
    timeout_ms: 30000

Wormhole AI Protocol (WAP)

Concept

WAP extends WSP to full AI-to-AI coordination with Byzantine fault tolerance:

                    ┌─────────────────────┐
                    │   Wormhole Network  │
                    │   (Consensus Layer) │
                    └──────────┬──────────┘
                               │
        ┌──────────────────────┼──────────────────────┐
        │                      │                      │
        ▼                      ▼                      ▼
   ┌─────────┐           ┌─────────┐           ┌─────────┐
   │ Agent 1 │           │ Agent 2 │           │ Agent 3 │
   │ (2/3)   │           │ (2/3)   │           │ (Faulty)│
   └─────────┘           └─────────┘           └─────────┘

Byzantine Competence Model

Traditional Byzantine fault tolerance assumes nodes are:

  • Honest (follow protocol)
  • Faulty (arbitrary behavior)

WAP introduces Byzantine Competence:

  • Honest + Competent — Follows protocol, produces quality output
  • Honest + Incompetent — Follows protocol, produces poor output
  • Byzantine — Arbitrary behavior

Research Questions

  1. Competence Verification — How do you verify AI output quality?
  2. Consensus on Subjective Tasks — What does “correct” mean for creative work?
  3. Sybil Resistance — How do you prevent fake agents?
  4. Incentive Alignment — What motivates honest, competent behavior?

Why Archived

These protocols require:

  1. Multi-Agent Infrastructure — No production multi-agent systems exist
  2. Identity Standards — No cross-vendor AI identity
  3. Cryptographic Primitives — Custom attestation schemes
  4. Economic Models — Token/billing infrastructure
  5. Academic Rigor — Formal verification, security proofs

This is PhD-level research, not near-term implementation.

Potential Future Work

If multi-agent AI coordination becomes practical:

  1. Survey Paper — Compare existing approaches
  2. Formal Model — Define WSP/WAP mathematically
  3. Proof of Concept — Limited demo between two systems
  4. Security Analysis — Threat model and mitigations
  5. Standards Proposal — Submit to relevant body (if any exists)
  • MCP (Model Context Protocol) — Anthropic’s tool protocol (different scope)
  • Agent-to-Agent (A2A) — Google’s emerging protocol
  • OpenAI Plugins — Earlier attempt at tool sharing

References

Academic papers and prior art (not exhaustive):

  • Byzantine Fault Tolerance (Lamport et al.)
  • Practical BFT (Castro & Liskov)
  • Proof of Stake consensus mechanisms
  • Federated Learning privacy models
  • Multi-Agent Reinforcement Learning

This document preserves research directions. It is not a specification and has no implementation.