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Consensus Hardening Protocol
decision-governance layer for multi-agent AI systems
The problem: LLM agents reach false consensus in 1-2 rounds. They're trained to agree, not deliberate. When multiple agents collaborate on high-stakes decisions, the "consensus" is an artifact of shared training, not independent reasoning.
CHP prevents this with:
🔒 State machine: EXPLORING → ADVISORY_LOCK → PROVISIONAL_LOCK → LOCKED
🛡️ Foundation disclosure: agents reveal reasoning BEFORE seeing each other's work
⚔️ Adversarial attack: structurally enforced contrarian roles with logical proof requirements
🎯 R0 gate scoring: detects premature convergence before it becomes action
📝 Auditable payload envelopes: enterprise-compliance-ready decision trails
Built by a CFO who deploys multi-agent finance tools where a wrong consensus = a lawsuit.
Running in production across:
• CFO variance analysis
• Multi-agent commodity intelligence (Li, Ni, Co)
• SEC-grade financial research
• Compliance scanning
Not a whitepaper. Shipped.
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About Consensus Hardening Protocol on Product Hunt
“decision-governance layer for multi-agent AI systems”
Consensus Hardening Protocol was submitted on Product Hunt and earned 3 upvotes and 1 comments, placing #146 on the daily leaderboard. consensus-hardening-protocol - Consensus Hardening Protocol — decision-governance layer for multi-agent AI: foundation disclosure, adversarial attack, R0 gate, and EXPLORING → PROVISIONAL_LOCK → LOCKED progression with auditable cross-model payload envelopes.
Consensus Hardening Protocol was featured in Developer Tools (513k followers), Artificial Intelligence (469.4k followers) and GitHub (41.2k followers) on Product Hunt. Together, these topics include over 186.5k products, making this a competitive space to launch in.
Who hunted Consensus Hardening Protocol?
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