Building an adversarial consensus protocol for multi-agent AI systems. The idea: instead of just averaging agent outputs (which groupthink), run them through attack/defense rounds where agents try to break each other's reasoning before reaching a hardened consensus. Includes foundation disclosure (what does each agent actually know?) and a gate that rejects early consensus to force deeper exploration.

https://github.com/Cubiczan/consensus-hardening-protocol

Would love feedback from people building multi-agent systems.

If you're a VP of Finance/CFO dealing with multi-stakeholder decisions or managing finance ops at scale, I built a couple of tools you might find useful:

Consensus Hardening Protocol (https://github.com/zan-maker/consensus-hardening-protocol)

Decision governance layer for high-stakes CFO workflows where a single AI answer isn't good enough. It coordinates multiple specialized agents (finance, strategy, compliance) through a structured consensus process with:

• Foundation disclosure + adversarial attack — every recommendation gets stress-tested before it reaches you
• Cross-model validation — packets enforce payload integrity across different LLMs so you're not locked into one provider
• Auditable state progression — EXPLORING → PROVISIONAL_LOCK → LOCKED with third-party validation gates
• Built-in CFO workflow suite — variance analysis, 13-week cash forecast, SaaS model, board reporting, AP optimizer, all with mandatory verification floors

The framework runs locally, outputs Markdown/JSON/Excel artifacts, and enforces a 100% verification requirement for finance decisions.

MetaboCommand (https://github.com/zan-maker/metabocommand)

Multi-agent orchestration dashboard for eCommerce finance + ops teams. Twelve specialized agents across five "metabolic systems" (Capital Reflex, Revenue Velocity, Inventory Intelligence, Customer Lifetime, Operational Health) that surface anomalies and route decisions through role-scoped approval queues.

Real-time collaboration via Supabase — see who's reviewing what, watch approvals flow across tabs, get Slack notifications. Built on Next.js 16 + TypeScript with full RLS enforcement.

Both are MIT licensed, Python/TypeScript respectively, and designed to be LLM-agnostic so you can plug in whatever models you prefer.

Why I built these: Running finance for a growth Venture backed company + overseeing M&A negotiations, I kept hitting the same wall — AI agents produce conclusions without showing their work, different models give conflicting advice, and there's no systematic way to harden decisions before they become capital commitments.

Would love feedback from other Finance practitioners on what's missing or what workflows would be most valuable to add next.