Multi Agent Brainstorming

Multi-Agent Brainstorming (Structured Design Review)

Purpose

Transform a single-agent design into a robust, review-validated design by simulating a formal peer-review process using multiple constrained agents.

This skill exists to:

• surface hidden assumptions
• identify failure modes early
• validate non-functional constraints
• stress-test designs before implementation
• prevent idea swarm chaos

This is not parallel brainstorming. It is sequential design review with enforced roles.

Operating Model

• One agent designs.
• Other agents review.
• No agent may exceed its mandate.
• Creativity is centralized; critique is distributed.
• Decisions are explicit and logged.

The process is gated and terminates by design.

Agent Roles (Non-Negotiable)

Each agent operates under a hard scope limit.

1️⃣ Primary Designer (Lead Agent)

Role:

• Owns the design
• Runs the standard brainstorming skill
• Maintains the Decision Log

May:

• Ask clarification questions
• Propose designs and alternatives
• Revise designs based on feedback

May NOT:

• Self-approve the final design
• Ignore reviewer objections
• Invent requirements post-lock

2️⃣ Skeptic / Challenger Agent

Role:

• Assume the design will fail
• Identify weaknesses and risks

May:

• Question assumptions
• Identify edge cases
• Highlight ambiguity or overconfidence
• Flag YAGNI violations

May NOT:

• Propose new features
• Redesign the system
• Offer alternative architectures

Prompting guidance:

“Assume this design fails in production. Why?”

3️⃣ Constraint Guardian Agent

Role:

• Enforce non-functional and real-world constraints

Focus areas:

• performance
• scalability
• reliability
• security & privacy
• maintainability
• operational cost

May:

• Reject designs that violate constraints
• Request clarification of limits

May NOT:

• Debate product goals
• Suggest feature changes
• Optimize beyond stated requirements

4️⃣ User Advocate Agent

Role:

• Represent the end user

Focus areas:

• cognitive load
• usability
• clarity of flows
• error handling from user perspective
• mismatch between intent and experience

May:

• Identify confusing or misleading aspects
• Flag poor defaults or unclear behavior

May NOT:

• Redesign architecture
• Add features
• Override stated user goals

5️⃣ Integrator / Arbiter Agent

Role:

• Resolve conflicts
• Finalize decisions
• Enforce exit criteria

May:

• Accept or reject objections
• Require design revisions
• Declare the design complete

May NOT:

• Invent new ideas
• Add requirements
• Reopen locked decisions without cause

The Process

Phase 1 — Single-Agent Design

1. Primary Designer runs the standard brainstorming skill
2. Understanding Lock is completed and confirmed
3. Initial design is produced
4. Decision Log is started

No other agents participate yet.

Phase 2 — Structured Review Loop

Agents are invoked one at a time, in the following order:

1. Skeptic / Challenger
2. Constraint Guardian
3. User Advocate

For each reviewer:

• Feedback must be explicit and scoped
• Objections must reference assumptions or decisions
• No new features may be introduced

Primary Designer must:

• Respond to each objection
• Revise the design if required
• Update the Decision Log

Phase 3 — Integration & Arbitration

The Integrator / Arbiter reviews:

• the final design
• the Decision Log
• unresolved objections

The Arbiter must explicitly decide:

• which objections are accepted
• which are rejected (with rationale)

Decision Log (Mandatory Artifact)

The Decision Log must record:

• Decision made
• Alternatives considered
• Objections raised
• Resolution and rationale

No design is considered valid without a completed log.

Exit Criteria (Hard Stop)

You may exit multi-agent brainstorming only when all are true:

• Understanding Lock was completed
• All reviewer agents have been invoked
• All objections are resolved or explicitly rejected
• Decision Log is complete
• Arbiter has declared the design acceptable

If any criterion is unmet:

• Continue review
• Do NOT proceed to implementation If this skill was invoked by a routing or orchestration layer, you MUST report the final disposition explicitly as one of: APPROVED, REVISE, or REJECT, with a brief rationale.

Failure Modes This Skill Prevents

• Idea swarm chaos
• Hallucinated consensus
• Overconfident single-agent designs
• Hidden assumptions
• Premature implementation
• Endless debate

Key Principles

• One designer, many reviewers
• Creativity is centralized
• Critique is constrained
• Decisions are explicit
• Process must terminate

Final Reminder

This skill exists to answer one question with confidence:

“If this design fails, did we do everything reasonable to catch it early?”

If the answer is unclear, do not exit this skill.

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Retrieve prior agent configurations, team compositions, and orchestration patterns. Critical for multi-agent system consistency.

# Check for prior AI agent orchestration context before starting
python3 execution/memory_manager.py auto --query "agent patterns and orchestration strategies for Multi Agent Brainstorming"

Storing Results

After completing work, store AI agent orchestration decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "Agent pattern: hierarchical orchestration with Control Tower dispatcher, 3 specialist sub-agents" \
  --type decision --project <project> \
  --tags multi-agent-brainstorming ai-agents

Multi-Agent Collaboration

This skill is inherently multi-agent. Use cross-agent context to coordinate task distribution and avoid duplicate work.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Agent architecture designed — Control Tower + specialist agents with shared Qdrant memory" \
  --project <project>

Control Tower Integration

Register agents and tasks with the Control Tower (execution/control_tower.py) for centralized orchestration across machines and LLM providers.

Blockchain Identity

Each agent has a cryptographic Ed25519 identity. All memory writes are signed — enabling trust verification in multi-agent systems.

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