Implement Advanced Multi-Agent Fact-Checking & Arbitration Engine

[Adithyakp86]

Description

Currently, the ArbitrationLayer in the AI Council Orchestrator relies on simple confidence scoring and basic text similarity checks to resolve discrepancies when multiple agents return conflicting data. In high-stakes environments (e.g., executing complex coding or reasoning workflows), naive similarity checks fail to detect nuanced, logical contradictions (e.g., an LLM citing a deprecated API versus an LLM citing a correct, modern API).

We need to implement an advanced Multi-Agent Fact-Checking & Arbitration Engine that actively debates conflicting responses, searches for grounding evidence, and uses a specialized "Judge" model to synthesize a verifiable truth.

Objectives & Acceptance Criteria

• Cross-Examination Prompting: Update the ConflictDetector so that if < 0.8 textual similarity is found, agents cross-examine each other's responses to point out factual errors.
• Evidence Grounding: Integrate an optional grounding/search step (if enabled via config) where the ArbitrationLayer can query a vectorized knowledge base or search tool to verify claims.
• Judge Model Delegation: If cross-examination results in a stalemate, route the conflicting statements and evidence to a high-tier Judge model (e.g., GPT-4 / Claude 3) whose sole role in this orchestrator is Arbitration.
• Structured Arbitration Log: Store an explanation trail (the "Why") inside FinalResponse.arbitration_decisions detailing exactly why Agent B was chosen over Agent A (e.g., "Agent B used Python 3.11 syntax, Agent A used deprecated Python 3.7 syntax").

Implementation Hints

• Refer to ai_council/orchestration/layer.py within the _stage_arbitrate method. You will need to extract the existing simple 6-step arbitration process into a dedicated engine inside utils/ or expand the ArbitrationLayer implementation class.
• Consider utilizing the model_context_protocol to fetch the designated Judge model via the ModelRegistry.

Skills Required

• Python (Async IO)
• Prompt Engineering & Multi-Agent Debate patterns
• System Design (Refactoring layers)

Constraints

• The fact-checking loop must strictly respect the adaptive timeouts already present in the orchestrator so it does not permanently hang the pipeline.
• The Engine must gracefully fallback to the highest-confidence answer if the Judge model fails or times out.

Difficulty: 🔴 Hard / Advanced
Estimated Time: 12-18 hours
Labels: enhancement, hard, OSCG, multi-agent-logic

[github-actions]

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[github-actions]

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[github-actions]

👋 Gentle Reminder

Hey @Adithyakp86! This issue was assigned to you 24 hours ago.

⏰ Timeline:

• ✅ 24 hours: Reminder (now)

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