Multi Agent Debate Review

Multi-Agent Debate Review

Run 6+ specialized agents in parallel against a codebase, compare findings, re-invoke agents on points of disagreement, and produce a consolidated report with prioritized actions.

Mode Selection

Ask the user which mode to use, or infer from context:

• Direct mode (default): You coordinate — launch agents, collect results, run debates, write report. More interactive, allows user to discuss findings between phases.
• Delegated mode: Launch a single general-purpose agent that coordinates the entire debate autonomously. Hands-off — user gets a final report. Use when user says "run it hands-off", "do it all automatically", or wants minimal interaction.

Direct Mode

Step 1: Load Standards

Load relevant skills to give agents better baselines. Select based on project language:

• Go: coding-standards, golang-patterns, security-review, backend-patterns
• Python: coding-standards, python-patterns, security-review
• TypeScript/JS: coding-standards, frontend-patterns, security-review

Step 2: Launch Agents in Parallel

Launch all agents with mode: "background". Each agent gets a focused prompt with explicit instructions to provide severity ratings, file references, and suggested fixes.

Select agents based on the project. See references/agent-configs.md for the full prompt templates for each agent type.

• Go projects: go-reviewer, go-security-reviewer, go-refactor-cleaner, code-reviewer, database-reviewer, architect
• Python projects: python-reviewer, python-security-reviewer, python-refactor-cleaner, code-reviewer, database-reviewer, architect
• JS/TS projects: code-reviewer, js-security-reviewer, js-refactor-cleaner, database-reviewer, architect

Always include code-reviewer, database-reviewer, and architect regardless of language.

Model Selection

Use the model parameter on each agent to assign different models. Two strategies:

Cost-optimized (default): Match model capability to task complexity.

Diversity-optimized: Use different model families to get genuinely different perspectives. Model diversity reduces shared blind spots — if Claude and GPT both flag the same issue independently, confidence is very high. If only one flags it, the debate round is more informative.

Ask the user which strategy they prefer, or default to cost-optimized.

Step 3: Track Findings

After collecting results, insert all findings into a SQL table:

CREATE TABLE findings (
  id TEXT PRIMARY KEY,
  category TEXT NOT NULL,    -- SECURITY, DATABASE, CODE, OBSERVABILITY, etc.
  severity TEXT NOT NULL,    -- CRITICAL, HIGH, MEDIUM, LOW
  title TEXT NOT NULL,
  agents_agreeing TEXT NOT NULL,    -- comma-separated agent names
  agents_disagreeing TEXT DEFAULT '',
  status TEXT DEFAULT 'open',
  consensus TEXT DEFAULT 'pending'  -- UNANIMOUS, RESOLVED, SINGLE_AGENT, UNRESOLVED
);

Mark consensus:

• UNANIMOUS: 3+ agents independently flagged the same issue
• SINGLE_AGENT: Only one agent found it (may be novel or false positive)
• UNRESOLVED: Agents actively disagree

Step 4: Identify Debates

Look for these patterns:

• Severity disagreement: One agent says CRITICAL, another says it's fine
• Approach conflict: One says "remove X", another says "keep X"
• Scope tension: One says "fix now", another says "fix later"

Step 5: Re-invoke Disagreeing Agents

For each debate, re-invoke the agents with the opposing position stated explicitly.

Prompt template:

You are participating in a multi-agent debate review.

[Agent A] found: [their position with reasoning]
[Agent B] found: [opposing position with reasoning]

Review the actual code at [path]. Then give your FINAL POSITION with reasoning
in 3-8 sentences. Acknowledge what the other agent got right.

Update the findings table with resolved positions.

Step 6: Consolidated Report

Structure the final report in this order:

1. Executive Summary — overall grade, agent scores table
2. Unanimous Findings — issues 3+ agents agree on (highest confidence)
3. Resolved Debates — disagreements settled through re-invocation
4. Unresolved Trade-offs — genuine tensions with no clear winner
5. Unique Insights — single-agent findings worth noting
6. Prioritized Action Plan — grouped by urgency with effort estimates
7. Strengths — what all agents praised (important for morale/context)

Save the report to the session artifacts directory.

Delegated Mode

Launch a single general-purpose agent that acts as debate coordinator. This agent has full access to the task tool and can launch sub-agents itself.

Use agent_type: "general-purpose" — this is the only agent type that can spawn sub-agents.

Prompt the coordinator with the full workflow. See references/delegated-coordinator-prompt.md for the complete prompt template.

Key points for the coordinator prompt:

• Tell it which agent types to launch (based on project language)
• Tell it to launch all review agents in parallel using mode: "background"
• Tell it to collect all results via read_agent
• Tell it to identify disagreements and re-invoke with opposing positions
• Tell it to produce the consolidated report in the standard format
• Give it the codebase path and any project-specific context

The coordinator runs autonomously — it launches reviewers as sub-agents, collects findings, runs the debate rounds, and returns the final consolidated report.

When to Prefer Delegated Mode

• User wants a fully autonomous review with no interaction
• Context window is getting full and you want to offload the work
• Running as part of an automated pipeline (e.g., pre-push hook, CI trigger)

Limitations of Delegated Mode

• No interactive discussion between phases — user gets final report only
• Coordinator context window is separate, so it won't have prior conversation history
• Coordinator must be given all relevant context in its prompt (project language, paths, focus areas)