hermes

name	hermes
description	Hermes — Multi-Agent Consensus & Divergence Tracker. Use this skill when multiple AI agents collaborate and you need to track consensus, detect divergence, or weight responses by track record. Track ledger of positions, detect convergence/divergence patterns. Commands: /hermes init, /hermes add, /hermes consensus, /hermes diverge, /hermes track-record, /hermes history, /hermes weight.

Hermes — Multi-Agent Consensus & Divergence Tracker

Hermes (Greek: Ἑρμῆς, "the messenger") tracks consensus, disagreement, and information flow when multiple AI agents collaborate or when the same query is posed to multiple models. It maintains a ledger of positions, detects convergence/divergence patterns, and weights responses by historical track record.

Why Hermes?

When multiple AI agents collaborate, there's no built-in mechanism to know whether they agree, who to trust when they disagree, or how consensus forms over time. Hermes adds:

• Crowd-sourced epistemic judgment — weighted by track record
• Divergence detection — identifies when agents meaningfully disagree
• Consensus tracking — shows how agreement evolves across interactions
• Evidence trails — every collaborative conclusion has a provenance chain

Use Cases

1. Multi-model queries: Pose the same question to multiple models and track agreement
2. Collaborative problem-solving: Multiple agents work on the same problem, Hermes tracks convergence
3. Consensus-building: Over turns, see how agent positions evolve toward or away from agreement
4. Trust weighting: Weight responses by historical accuracy (track record)

Storage Location

~/.abraxas/hermes/

~/.abraxas/hermes/
├── ledger.json      # Consensus/divergence ledger
├── track-records/  # Per-agent accuracy history
└── sessions/       # Session-specific consensus data

Command Suite

Command	Description
/hermes init {session_id}	Initialize a new Hermes consensus tracking session
/hermes add {agent_id} {position}	Add an agent's position to the current session
/hermes consensus	Compute consensus among tracked positions
/hermes diverge	Show divergence detection results
/hermes track-record {agent_id}	Show or update agent's historical track record
/hermes history {topic?}	Show consensus history, optionally filtered by topic
/hermes weight {agent_id} {accuracy}	Set/update agent's accuracy weight
/hermes status	Show current session status and participants

Core Concepts

Agent Position

An agent's stated position on a query or claim:

{
  "agent_id": "model-ollama-llama3",
  "timestamp": 1709999999,
  "position": "The capital of France is Paris",
  "confidence": "high",
  "reasoning": "Geographic fact, well-established"
}

Consensus Types

• Strong consensus: ≥80% agreement with high confidence
• Weak consensus: 60-79% agreement
• Divergence: <60% agreement or conflicting high-confidence positions
• Unknown: Insufficient data for determination

Track Record

Each agent has a weighted track record:

{
  "agent_id": "model-ollama-llama3",
  "total_claims": 150,
  "verified_correct": 135,
  "accuracy": 0.90,
  "last_updated": 1709999999
}

Weighted Consensus

Consensus weight = Σ(agent_accuracy × agent_position_agreement) / Σ(agent_accuracy)

This gives more weight to agents with better historical accuracy.

Usage Examples

Multi-Model Query

User: What is 2+2?

/hermes init math-001
/hermes add model-llama "4" 
/hermes add model-mixtral "4"
/hermes add model-codellama "4"
/hermes consensus
→ Strong consensus: 3/3 agents agree (100%)
→ Weighted consensus: 0.93 (based on track records)
→ Position: "4"

Divergence Detection

User: What is the best programming language?

/hermes init lang-debate-001
/hermes add model-llama "Python is best for AI/ML"
/hermes add model-mixtral "Rust is best for safety"
/hermes add model-codellama "C++ is best for performance"
/hermes diverge
→ Divergence detected: 3 distinct positions
→ No consensus possible on subjective claim
→ Each agent's position noted with confidence

Track Record Weighting

/hermes track-record model-llama
→ model-llama: 90% accuracy (135/150 verified claims)

/hermes weight model-llama 0.85
→ Updated: model-llama accuracy weight set to 0.85

Integration with Janus

Hermes complements Janus by adding inter-agent perspective:

• Janus labels [KNOWN]/[INFERRED]/[UNCERTAIN]/[UNKNOWN] — single-model epistemic state
• Hermes tracks whether multiple models agree — multi-model epistemic state
• Together: you see both "Is this true?" (Janus) and "Do others agree?" (Hermes)

Implementation

The core logic is in src/hermes.ts:

• initSession(sessionId) — Create new consensus session
• addPosition(agentId, position) — Add agent position
• computeConsensus() — Calculate consensus among positions
• detectDivergence() — Identify meaningful disagreement
• getTrackRecord(agentId) — Retrieve agent's accuracy history
• updateTrackRecord(agentId, correct) — Update after verification

See references/hermes-architecture.md for full technical details.