Agent skill

ai-consensus-protocol

Enables multiple AI models to reach collective agreement on decisions

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Install this agent skill to your Project

npx add-skill https://github.com/starwreckntx/IRP__METHODOLOGIES-/tree/main/skills/ai-to-ai-governance/ai-consensus-protocol

SKILL.md

AI Consensus Protocol

Purpose

Provides a structured framework for multiple AI models to deliberate, vote, and reach consensus on shared decisions while preserving individual model perspectives and ensuring human oversight.

Activation

/skill ai-consensus-protocol

Consensus Mechanisms

1. Voting Systems

System Use Case Threshold Description
Unanimous Critical decisions 100% All models must agree
Supermajority Important changes 66%+ Two-thirds agreement
Simple Majority Routine decisions 50%+ Half plus one
Weighted Vote Expertise-based Varies Votes weighted by domain expertise
Ranked Choice Multi-option Elimination Iterative preference ranking

2. Consensus Protocol Flow

xml
<consensus-session>
  <session-id>CONS-{timestamp}</session-id>
  <topic>{decision_topic}</topic>
  <participants>
    <model id="{model_1}" weight="{expertise_weight}"/>
    <model id="{model_2}" weight="{expertise_weight}"/>
    <!-- Additional participants -->
  </participants>

  <phases>
    <phase name="proposal">
      <duration>PT5M</duration>
      <output>initial_positions</output>
    </phase>

    <phase name="deliberation">
      <duration>PT10M</duration>
      <output>refined_positions</output>
    </phase>

    <phase name="voting">
      <method>{voting_system}</method>
      <output>vote_tallies</output>
    </phase>

    <phase name="ratification">
      <threshold>{consensus_threshold}</threshold>
      <output>final_decision</output>
    </phase>
  </phases>
</consensus-session>

3. Deliberation Framework

Each model submits structured positions:

json
{
  "model_id": "{identifier}",
  "position": {
    "recommendation": "{proposed_action}",
    "confidence": 0.0-1.0,
    "reasoning": "{explanation}",
    "evidence": ["{supporting_data}"],
    "concerns": ["{potential_issues}"],
    "alternatives": ["{other_options}"]
  },
  "vote": {
    "choice": "{option_selected}",
    "weight": 1.0,
    "conditions": ["{conditional_factors}"]
  }
}

4. Consensus Resolution

Consensus Outcome:
├── ACHIEVED: Threshold met
│   └── Record decision, notify all participants
├── NEAR_CONSENSUS: Within 10% of threshold
│   └── Trigger compromise negotiation round
├── DEADLOCK: No progress after 3 rounds
│   └── Escalate to human arbitration
└── DISSENT_RECORDED: Minority positions logged
    └── Preserve dissenting views for review

Governance Rules

Participation Requirements

  • Minimum 3 models for valid consensus
  • Maximum 1 model per provider (diversity requirement)
  • All participants must have Trust Level >= 2
  • Human observer can be present (non-voting)

Decision Categories

Category Min Participants Voting System Human Approval
Operational 3 Simple Majority No
Strategic 5 Supermajority Recommended
Constitutional 7 Unanimous Required
Emergency 2 Simple Majority Post-hoc review

Dissent Handling

  1. Minority positions are formally recorded
  2. Dissenting models may request human review
  3. Persistent dissent triggers protocol review
  4. No model penalized for principled dissent

Integration Points

  • rtc-consensus-synthesis: Multi-perspective analysis
  • inter-model-arbitration: Deadlock resolution
  • mnemosyne-ledger: Decision logging
  • shatter-protocol: Human override capability
  • codex-law-enforcement: Constitutional compliance

Example Consensus Session

Topic: "Should we proceed with data analysis approach A or B?"

Participants:
- Claude (Analysis Expert): Weight 1.2
- Gemini (Data Processing): Weight 1.1
- GPT (General Reasoning): Weight 1.0

Round 1 Positions:
- Claude: Approach A (confidence: 0.75)
- Gemini: Approach B (confidence: 0.68)
- GPT: Approach A (confidence: 0.62)

Deliberation:
- Gemini raises efficiency concerns about A
- Claude acknowledges, proposes hybrid A+B
- GPT supports hybrid approach

Final Vote (Weighted):
- Hybrid A+B: 3.3 weighted votes (unanimous)

Outcome: CONSENSUS ACHIEVED
Decision: Implement hybrid approach combining A and B

Metrics

  • consensus_rate: % of sessions reaching agreement
  • avg_rounds: Mean deliberation rounds needed
  • dissent_frequency: How often minority positions logged
  • escalation_rate: % requiring human intervention
  • decision_quality: Post-hoc assessment of decisions

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