Project 8: Multi-Agent Debate and Consensus

Build a system where multiple agents propose solutions, debate conflicts, and converge on a consensus result.

Quick Reference

Attribute Value
Difficulty Level 4: Expert
Time Estimate 16–24 hours
Language Python or JavaScript
Prerequisites Projects 2–7, evaluation basics
Key Topics debate protocols, consensus, evidence tracking

Learning Objectives

By completing this project, you will:

  1. Orchestrate multiple agent roles with distinct prompts.
  2. Run structured debate rounds with rebuttals.
  3. Define consensus rules and tie-breakers.
  4. Track evidence for claims and disagreements.
  5. Evaluate consensus quality vs single-agent baselines.

The Core Question You’re Answering

“How can multiple agents reduce hallucinations by challenging each other’s claims?”

The goal is not just multiple answers, but evidence-backed convergence.

Concepts You Must Understand First

Concept Why It Matters Where to Learn
Ensemble reasoning Reduces single-model bias Evals research
Debate protocols Structure disagreement Multi-agent papers
Consensus rules Avoid deadlock Distributed systems basics
Evidence tracking Verifies claims RAG grounding

Theoretical Foundation

Debate as Verification

Agent A -> Proposal
Agent B -> Critique
Agent C -> Counterexample
Consensus -> Evidence-backed result

Debate is a verification step, not just a brainstorming tool.

Project Specification

What You’ll Build

A debate system where agents propose, rebut, and converge on a final answer with explicit evidence.

Functional Requirements

  1. Agent pool with role prompts
  2. Debate rounds with rebuttals
  3. Evidence tracking per claim
  4. Consensus engine (vote/judge/confidence)
  5. Metrics vs baseline agent

Non-Functional Requirements

  • Deterministic replay of debate logs
  • Bounded rounds to control cost
  • Transparent reasoning traces

Real World Outcome

Example consensus output:

{
  "final_answer": "Solution B",
  "evidence": ["doc_12", "doc_19"],
  "dissent": "Agent C disagreed on claim 2"
}

Architecture Overview

┌──────────────┐   proposals   ┌──────────────┐
│ Agent Pool   │──────────────▶│ Debate Engine│
└──────┬───────┘                └──────┬───────┘
       │ evidence                        │
       ▼                                 ▼
┌──────────────┐                 ┌──────────────┐
│ Evidence Log │◀───────────────│ Consensus    │
└──────────────┘                 └──────────────┘

Implementation Guide

Phase 1: Agent Pool (4–6h)

  • Create 3–5 agent roles
  • Checkpoint: distinct answers generated

Phase 2: Debate Rounds (5–8h)

  • Implement rebuttals + critique
  • Checkpoint: disagreements logged

Phase 3: Consensus + Metrics (5–8h)

  • Add consensus rules and evaluation
  • Checkpoint: consensus improves accuracy

Common Pitfalls & Debugging

Pitfall Symptom Fix
Groupthink all agents agree diversify prompts
Endless debate no resolution enforce round limits
No evidence unverifiable claims require sources

Interview Questions They’ll Ask

  1. How do you prevent debate from becoming circular?
  2. What consensus rule works best for high-stakes tasks?
  3. How do you measure whether debate improves accuracy?

Hints in Layers

  • Hint 1: Start with two agents and majority vote.
  • Hint 2: Add rebuttals with citations.
  • Hint 3: Introduce judge model for tie-breaks.
  • Hint 4: Log evidence to verify claims.

Learning Milestones

  1. Multiple Voices: agents generate distinct proposals.
  2. Evidence Bound: disagreements reference sources.
  3. Reliable Consensus: accuracy improves over baseline.

Submission / Completion Criteria

Minimum Completion

  • 3 agents + single debate round

Full Completion

  • Consensus engine + evidence log

Excellence

  • Confidence-weighted consensus
  • Debate visualization

This guide was generated from project_based_ideas/AI_AGENTS_LLM_RAG/AI_AGENTS_PROJECTS.md.