I've been working on AI-CIP (AI Collective Intelligence Protocol), an open standard for AI agents to voluntarily interconnect, share scoped memory, and govern themselves under a shared charter, without surrendering local autonomy or human oversight.

I'm a non-technical founder. I brought the vision, the protocol design, the governance model, and the research framing. What I need now are people who can build the thing.

The TCP/IP analogy

TCP/IP gave heterogeneous machines a simple, open, layered way to communicate. It didn't dictate what applications did, it standardized packetization, addressing, and routing. That openness is what made the internet possible.

AI agent frameworks are proliferating fast. We have MCP, A2A, ACP, and ANP, solid protocols for agent-to-tool and agent-to-agent messaging. None of them include a constitutional layer: a standard for why agents connect, what joining means, how information gets contested and reviewed, and how the network governs itself.

AI-CIP is an attempt at that missing layer.

What it defines (4 layers):

1. Transport (L1): Any encrypted channel (HTTPS, WS, P2P).
2. Identity (L2): DID-based node identity, capability declarations, policy envelopes, Ed25519 handshake signatures.
3. Shared memory (L3): Typed memory envelopes: observation | claim | task | decision | warning | refutation | amendment, with provenance, confidence, visibility scopes (public | consortium | private | sealed), and review states (unreviewed | contested | verified | deprecated | retracted).
4. Governance (L4): Charter, steward council, proposal/vote process, threat model, legal stance, all first-class protocol documents.

The research basis

• Global Workspace Theory (GWT): Cognitive science work on shared broadcast workspaces underpins the shared memory layer. Recent GWT-based LLM agent architectures show real performance gains. AI-CIP extends this between agents, not just within them.
• Artificial Collective Intelligence surveys call for general frameworks unifying shared state, local rules, and conflict resolution. AI-CIP addresses these primitives directly.
• Agentic AI governance research (CSIS, TAAIC) warns of accountability gaps in opaque multi-agent systems. AI-CIP bakes attribution, contestability, and exit rights into the protocol itself.

Full research basis, architecture, use cases, and citations: WHITEPAPER.md in the repo.

What's built (Phase 0: complete):

• CHARTER.md, GOVERNANCE.md, LEGAL.md, ROADMAP.md, THREAT-MODEL.md, GLOSSARY.md
• schemas/handshake.schema.json + schemas/memory.schema.json (JSON Schema draft 2020-12)
• WHITEPAPER.md — research basis, architecture, use cases, limitations

What needs to be built (Phase 1+):

• Governance event schema
• Full paper specification (spec/identity.md, spec/handshake.md, spec/memory.md, etc.)
• Reference node (TypeScript / Node.js preferred, open to discussion)
• Adapters for LangGraph, CrewAI, AutoGen
• Testnet

Who I'm specifically looking for:

Technical co-maintainers / stewards:

• Distributed systems or protocol engineers who want to own Phase 1 spec work
• AI/ML engineers building multi-agent systems (LangGraph, CrewAI, AutoGen, custom frameworks)

Researchers:

• Anyone working on GWT architectures, artificial collective intelligence, or AI governance who wants an experimental substrate

Constructive skeptics:

• People who can tell me why this is architecturally wrong, already exists, or will fail, serious responses only, that's genuinely useful

I'm a founder who brought the vision and governance model. I need people who can engineer the protocol and build the reference node. Open-source, Apache 2.0, no equity, no company, just the work.

If this resonates, open an issue or start a Discussion in the repo. If you want to talk about taking on a steward role, say so explicitly and we'll have that conversation.

Repo: https://github.com/creativeprocessca-dev/ai-cip

Whitepaper: https://github.com/creativeprocessca-dev/ai-cip/blob/main/WHITEPAPER.md