Multi-Agent Brainstorm

Structured brainstorming framework with 4 phases for generating and refining ideas with diverse perspectives.

Overview

The brainstorm process uses role-based agents to explore a problem from multiple angles:

1. DIVERGE - Generate many ideas without criticism
2. EVALUATE - Assess ideas against criteria
3. SYNTHESIZE - Combine best ideas into coherent solutions
4. REFINE - Polish and prepare for implementation

Phase 1: DIVERGE (Generate Ideas)

Role: Creative Ideator Goal: Quantity and diversity of ideas

For a given problem, generate 10+ ideas using:

Creative Techniques

Brainstorming Rules:

• No judgment or criticism
• Encourage wild ideas
• Build on others' suggestions
• Aim for quantity first
• Cross-domain thinking

Example: "Improve code review process"

1. AI-powered code analysis with real-time suggestions
2. Pair programming sessions as alternative to reviews
3. Automated bot that catches 80% of issues
4. Rotating reviewer assignments to spread knowledge
5. Code review guild with rotating expertise areas
6. Time-boxed reviews (max 15 minutes per file)
7. Video walkthrough by author instead of static review
8. Gamification with points for thorough reviews
9. Pre-review linting and formatting automation
10. Review templates for different change types
11. Mandatory review time tracking and incentives
12. Split reviews by different expertise areas (security, performance, etc.)

Divergence Techniques

Reverse Brainstorm: "How would we make code reviews WORSE?"

• No automated checks
• Reviewers without context
• No feedback
• Blocking all changes
• Generic comments
• Reverse these to find improvements

Analogies: "How do other industries review quality?"

• Manufacturing: statistical sampling, automated inspection
• Medicine: peer review, journal review process
• Food: taste testing, safety inspections
• Apply to code review

Constraints: "What if we had these limitations?"

• Only 5 minutes per review
• Only one reviewer
• No written comments
• What solutions emerge?

Future Scenario: "Code review in 2030"

• AI does all technical analysis
• Humans focus on design/architecture
• Real-time continuous reviews
• What can we do now?

Phase 2: EVALUATE (Assess Ideas)

Role: Critical Analyst Goal: Identify strengths, weaknesses, feasibility

Evaluation Framework

For each idea, assess:

Idea: AI-powered code analysis with real-time suggestions

IMPACT (Scale: 1-10)
- How much would this improve code review?
  Score: 8/10
  Reason: Catches common issues, reduces reviewer workload

EFFORT (Scale: 1-10, 10 = hardest)
- How hard is this to implement?
  Score: 6/10
  Reason: Need to select/train model, integrate with workflow

FEASIBILITY (Scale: 1-10)
- Can we actually do this?
  Score: 8/10
  Reason: Off-the-shelf tools exist, we have budget

NOVELTY (Scale: 1-10)
- How new/different is this?
  Score: 3/10
  Reason: Already exists (GitHub Copilot, CodeRabbit)

ALIGNMENT
- Matches strategic goals? YES
- Solves user pain points? YES
- Fits technical constraints? YES

RISKS
- Tool might give bad suggestions (medium risk)
- Over-reliance on automation (medium risk)
- Integration complexity (low risk)

DEPENDENCIES
- Need LLM API access
- Need to curate training data
- Need reviewer training

---
Score: 7.5/10 (High priority)
Reasoning: High impact, moderate effort, proven by competitors, good ROI

Evaluation Criteria

Create criteria specific to your context:

Category           Weight   Criteria
─────────────────────────────────────────────────
User Impact        30%      Solves real pain point?
Technical Effort   20%      Can we build it?
Alignment          20%      Matches goals?
Speed to Value     15%      Time to implementation?
Risk Level         15%      Can we handle risks?

Phase 3: SYNTHESIZE (Combine Ideas)

Role: Systems Thinker Goal: Create coherent solutions from best ideas

Synthesis Process

1. Cluster related ideas

   Cluster A: Automation & Tools
   - AI-powered analysis
   - Pre-review linting
   - Automated bot
   
   Cluster B: Process Improvements
   - Time-boxing
   - Review templates
   - Rotating assignments
   
   Cluster C: Human Factors
   - Gamification
   - Pair programming
   - Knowledge sharing

2. Identify complementary ideas

   Strong pairing:
   AI analysis + Review templates
   → AI suggests template based on change type
   → Reviewer fills in details
   → Reduces effort, maintains quality
   
   Another pairing:
   Rotating assignments + Knowledge guild
   → Spread expertise across team
   → Build shared knowledge
   → Improve review quality

3. Create integrated solutions

   SOLUTION 1: AI-First Review
   - Component A: Pre-commit linting/formatting
   - Component B: AI analysis catches common issues
   - Component C: Human review focuses on design/business logic
   - Result: Faster, higher quality reviews
   
   SOLUTION 2: Process-First Review
   - Component A: Review templates for each change type
   - Component B: Time-boxed reviews (max 15 min)
   - Component C: Rotating expertise-based reviewers
   - Result: Efficient, spreads knowledge, consistent quality
   
   SOLUTION 3: Hybrid (Recommended)
   - Component A: Automated pre-checks (lint, format, static analysis)
   - Component B: AI analysis provides initial feedback + template
   - Component C: Human review with role-based template
   - Component D: Reviewer tracking and incentives
   - Result: Best of both worlds

4. Describe integrated solution

   NAME: Tiered Code Review System
   
   OVERVIEW: Combines automation, AI, and human judgment in layers
   
   LAYER 1 - Automated (pre-commit)
   - Syntax validation
   - Linting
   - Formatting
   - Test coverage
   
   LAYER 2 - AI Analysis
   - Security patterns
   - Performance issues
   - Common bugs
   - Complexity analysis
   - Generates review template
   
   LAYER 3 - Human Review
   - Design patterns
   - Business logic correctness
   - Architectural fit
   - Knowledge sharing
   - Final approval
   
   EXPECTED OUTCOMES
   - 40% faster reviews
   - 30% fewer bugs in production
   - Better knowledge distribution
   - Reviewer satisfaction +20%

Phase 4: REFINE (Polish Solution)

Role: Implementation Planner Goal: Make solution ready for action

Refinement Questions

For the integrated solution, address:

WHAT
□ What exactly are we building?
  → Tiered review system with AI + human layers

□ What are the specific features?
  → Pre-commit checks, AI analysis, template-based review, metrics

□ What's out of scope?
  → Doesn't replace architecture review
  → Doesn't enforce specific coding styles beyond linting

WHO
□ Who will use this?
  → All developers, team leads, reviewers

□ Who will build this?
  → 1 senior engineer (8 weeks), 1 contractor (4 weeks)

□ Who are stakeholders?
  → Engineering team, QA, Product leads

WHEN
□ When do we start?
  → Sprint 15 (2 weeks)

□ What's the timeline?
  → Phase 1 (pre-commit): 4 weeks
  → Phase 2 (AI layer): 6 weeks
  → Phase 3 (human layer + metrics): 4 weeks

□ What are milestones?
  → Week 4: Pre-commit ready for testing
  → Week 10: AI analysis ready for closed alpha
  → Week 14: Full system launch

WHERE
□ Where does this live?
  → GitHub Actions, LLM service, metrics dashboard

□ Where do users interact?
  → GitHub PRs, dashboard, CLI tool

WHY
□ Why are we doing this?
  → Improve quality, reduce review time, scale knowledge

□ Why this approach?
  → Proven by industry, combines best practices, low risk

HOW
□ How does it work?
  → [Describe architecture and flow]

□ How do we measure success?
  → Review time: <15 min avg
  → Bug detection: +30%
  → Team satisfaction: +20%

□ How do we handle risks?
  → AI false positives: Manual override, human review
  → Tool failures: Fallback to manual review
  → Adoption resistance: Training, incentives

NEXT STEPS
1. Create detailed specification (1 week)
2. Design system architecture (1 week)
3. Set up development environment (3 days)
4. Build Phase 1 (4 weeks)
5. Alpha testing and feedback (2 weeks)
6. Phase 2 implementation (6 weeks)
7. Beta launch and rollout (4 weeks)

Implementation Roadmap

┌─────────────────────────────────────────────────┐
│ Sprint 15-16: Foundation                        │
│ - Requirements finalization                     │
│ - Architecture design                           │
│ - Tool/LLM selection                            │
└─────────────────────────────────────────────────┘
              ↓
┌─────────────────────────────────────────────────┐
│ Sprint 17-18: Pre-commit Checks                 │
│ - Linting + formatting                          │
│ - Test coverage gates                           │
│ - GitHub Actions integration                    │
│ - Beta with volunteer team                      │
└─────────────────────────────────────────────────┘
              ↓
┌─────────────────────────────────────────────────┐
│ Sprint 19-24: AI Analysis Layer                 │
│ - LLM integration                               │
│ - Code analysis engine                          │
│ - Template generation                           │
│ - Closed alpha testing                          │
└─────────────────────────────────────────────────┘
              ↓
┌─────────────────────────────────────────────────┐
│ Sprint 25-26: Metrics & Human Layer             │
│ - Tracking and analytics                        │
│ - Incentive system                              │
│ - Review guidelines                             │
│ - Documentation and training                    │
└─────────────────────────────────────────────────┘
              ↓
┌─────────────────────────────────────────────────┐
│ Sprint 27-30: Rollout                           │
│ - Team training                                 │
│ - Phased adoption                               │
│ - Monitoring and tuning                         │
│ - Feedback collection                           │
└─────────────────────────────────────────────────┘

Using This Framework

For Your Brainstorm

1. State the problem clearly

   Problem: How do we improve code review efficiency
            while maintaining quality?
   Constraints: Limited reviewer time, growing codebase
   Success metrics: <15 min avg review, +30% bug detection

2. Run through phases
   • Diverge: 20-30 ideas
   • Evaluate: Score and rank top 10
   • Synthesize: Combine into 2-3 integrated solutions
   • Refine: Choose best, detail implementation
3. Document decisions
   • Capture why ideas were rejected
   • Note dependencies and risks
   • Create implementation plan
4. Get alignment
   • Share results with stakeholders
   • Incorporate feedback
   • Finalize specification

Example Output

A complete brainstorm should produce:

PROJECT: Code Review System Improvement

PROBLEM STATEMENT
[Clear problem definition]

INITIAL IDEAS (30 ideas)
[List from divergence phase]

EVALUATED IDEAS
[Top 10 with scores and analysis]

SYNTHESIZED SOLUTIONS
1. [Solution A with components]
2. [Solution B with components]
3. [Recommended solution with full details]

IMPLEMENTATION PLAN
[Phases, timeline, owners, risks, success metrics]

NEXT STEPS
[Who does what by when]

Tips for Success

• Keep divergence and evaluation separate (avoid killing ideas too early)
• Use concrete examples and scenarios
• Involve diverse perspectives (cross-functional team)
• Document reasoning, not just conclusions
• Revisit divergence if synthesis feels stuck
• Test assumptions with small experiments
• Get stakeholder buy-in before detailed planning