agent-room

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# Agent Room

*Meta — Stochastic Multi-Agent Discussion. View a problem through multiple expert perspectives via debate or polling.*

**Core Question:** "What do multiple perspectives converge on — and where do they genuinely disagree?"

This is the centralized **multi-perspective analysis** capability. When any skill needs debate, consensus, or multiple viewpoints on a decision, it invokes agent-room. Structured decomposition work (like task-breakdown) may retain specialized agents for their domain.

---

## Two Entry Points

### 1. Standalone (user invokes directly)
User runs `/agent-room "Should we use a monorepo or polyrepo?"` — the skill runs a full debate or poll session.

### 2. Sub-routine (another skill invokes mid-flow)
The `discover` skill hits a complex decision during conversation. It invokes agent-room with the specific decision framed, waits for the result, then continues the conversation.

**Sub-routine protocol** (for invoking skills):
```
1. Frame the specific decision as a clear problem statement
2. Include relevant context gathered so far
3. Invoke agent-room with mode (debate/poll) and agent count
4. Receive the report: consensus, disagreements, recommendation
5. Integrate the recommendation into the ongoing conversation
6. The agent-room report is ephemeral — it lives in context, not necessarily on disk
```

When invoked as a sub-routine, skip writing the report to disk unless the user asks. The value is the insight, not the artifact.

---

## Critical Gates

1. **Choose the right mode** — debate for trade-off decisions, poll for filtering hallucinations and finding consensus. Default to debate (richer output for fewer agents).
2. **Problem must be specific** — N agents on a fuzzy prompt wastes tokens. If vague, ask the user to sharpen before spawning.
3. **Agents must produce structured output** — freeform prose can't be aggregated.
4. **Cost scales with agent count** — 3 debate agents x 3 rounds ~ $0.30-0.50. 10 poll agents ~ $0.30-0.50. Default to sonnet unless user requests opus.

---

## Mode Routing

| Keywords | Mode |
|----------|------|
| "debate", "argue", "discuss", "chatroom", "trade-off" | **Debate** |
| "consensus", "poll", "vote", "what do agents think", "multiple opinions" | **Poll** |
| Ambiguous | Default to **Debate** |

---

## Mode A: Debate

Spawn N agents (default 3) into a shared conversation. Each reads the full chat history before responding — building on, challenging, or refining previous contributions.

**Why it works:** Sequential handoffs lose context. A shared conversation preserves reasoning chains and enables genuine debate. When Agent A says "this needs a queue" and Agent B says "a simple loop is fine," that disagreement is more valuable than either agent's solo answer.

### A1. Parse the Request

Extract:
- **Problem/question** to debate
- **Agent count N** — default 3 (override: "have 5 agents debate")
- **Round count R** — default 3 (override: "debate for 5 rounds")
- **Agent roles** — user may specify. If not, assign diverse defaults.

### A2. Assign Agent Roles

Each agent gets a distinct perspective to maximize productive disagreement.

**Software engineering:**
1. **Architect** — systems, interfaces, scalability, long-term maintainability
2. **Pragmatist** — shipping fast, minimal complexity, "good enough" solutions
3. **Critic** — edge cases, failure modes, security holes, unstated assumptions

**Product/design:**
1. **User advocate** — UX, simplicity, delight
2. **Business strategist** — revenue, growth, competitive advantage
3. **Engineer** — technical feasibility and cost

**Strategy/decisions:**
1. **Optimist** — opportunity, upside, reasons to act
2. **Skeptic** — risk, downside, reasons to wait
3. **Synthesizer** — middle path, integrates both perspectives

For N > 3, add roles that create productive tension with existing ones.

**Constraint-assignment for divergence** — when the debate is about design or architecture (not strategy), assign each agent a structural constraint instead of (or in addition to) a perspective. This mechanically forces different solutions rather than hoping for them:
- Agent 1: "Minimize surface area — aim for the fewest possible methods/endpoints"
- Agent 2: "Maximize flexibility — support the widest range of use cases"
- Agent 3: "Optimize for the most common case — make the 80% path trivially simple"
- Agent 4 (if N > 3): "Take inspiration from [specific paradigm/library the user knows]"

Constraint-assigned agents produce genuinely different designs. Perspective-assigned agents tend to converge on similar designs with different justifications.

### A3. Run Debate Rounds

**Round 1 — Opening positions.** Agent prompt:
```
You are {role}: {role_description}

PROBLEM:
{problem}

CONTEXT:
{context}

This is Round 1 of a multi-agent debate. State your initial position.
Be specific — propose actual solutions, not vague principles. Take a clear stance.
Other agents will challenge you in subsequent rounds.

Communication discipline:
- No performative agreement: never open with "Great point" or "I appreciate X's perspective"
- State disagreements directly: "That approach fails because [X]" not "While that has merit..."
- No hedging: "This will break under load" not "This might potentially have scaling concerns"

Respond in this format:
POSITION: [One-sentence stance]
REASONING: [3-5 key points]
PROPOSAL: [Concrete recommendation]

From abdellaziz-ali/AI-Skills, MIT.

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