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Competitive Multi-Agent Code Generation

High-assurance workflow for critical features using multi-agent competitive generation, independent evaluation, and evidence-based synthesis to produce superior solutions.

Claude Code Knowledge Pack7/10/2026

Overview

Competitive Multi-Agent Code Generation

High-assurance workflow for critical features using multi-agent competitive generation, independent evaluation, and evidence-based synthesis to produce superior solutions.

For simple features that don't require competitive exploration, use Feature Development workflow.

When to Use

  • Quality-critical implementations - Authentication, payment processing, data validation
  • Novel or ambiguous requirements - No clear "right answer", multiple valid approaches
  • High-stakes architectural decisions - API design, schema design, core algorithms
  • Avoiding local optima - When single-agent reflection might miss better approaches

When NOT to Use

  • Simple, well-defined tasks with obvious solutions
  • Time-sensitive changes where speed matters more than exploration
  • Trivial bug fixes or typos
  • Tasks with only one viable approach

Plugins Needed

  • SADD - Competitive execution
  • TDD - Test coverage
  • Review - Final validation
  • Git - Version control

Workflow

How It Works

                                    PHASE 1: COMPETITIVE GENERATION
┌─────────────────────────────────────────────────────────────────────────────────┐
│ Task ───┬─ Agent 1 → Draft → Self-Critique → Revise → Solution A ─┐            │
│         ├─ Agent 2 → Draft → Self-Critique → Revise → Solution B ─┼─┐          │
│         └─ Agent 3 → Draft → Self-Critique → Revise → Solution C ─┘ │          │
└─────────────────────────────────────────────────────────────────────────────────┘
                                           │
                                           ▼
                                    PHASE 2: MULTI-JUDGE EVALUATION
┌─────────────────────────────────────────────────────────────────────────────────┐
│         ┌─ Judge 1 → Evaluate → Verify → Report A ─┐                           │
│         ├─ Judge 2 → Evaluate → Verify → Report B ─┼─ Consensus Analysis       │
│         └─ Judge 3 → Evaluate → Verify → Report C ─┘                           │
└─────────────────────────────────────────────────────────────────────────────────┘
                                           │
                                           ▼
                                    PHASE 3: ADAPTIVE STRATEGY
┌─────────────────────────────────────────────────────────────────────────────────┐
│                    Clear Winner? → SELECT_AND_POLISH                            │
│                    All Flawed?   → REDESIGN (restart Phase 1)                   │
│                    Split Vote?   → FULL_SYNTHESIS                               │
└─────────────────────────────────────────────────────────────────────────────────┘
                                           │
                                           ▼
                                    QUALITY GATES
┌─────────────────────────────────────────────────────────────────────────────────┐
│         Write Tests → Review Changes → Create Commit                            │
└─────────────────────────────────────────────────────────────────────────────────┘

1. Competitive Implementation

Use /do-competitively to generate multiple solutions, evaluate them independently, and synthesize the best elements.

/do-competitively "Implement JWT authentication middleware with token refresh, rate limiting, and secure session management"

What happens:

  1. 3 agents independently design and implement solutions with self-critique
  2. 3 judges evaluate each solution using structured rubrics with verification
  3. Adaptive strategy selects: polish the winner, redesign if all flawed, or synthesize best elements

For specific output location:

/do-competitively "Design user authentication schema" --output "src/models/auth.ts"

With custom evaluation criteria:

/do-competitively "Create API rate limiting middleware" --criteria "security,performance,maintainability"

After completion, review the synthesized solution to ensure it meets your requirements.

2. Write Tests

Use /write-tests to generate comprehensive test coverage for the synthesized solution.

/write-tests

Or with specific focus:

/write-tests Focus on security edge cases and error handling

Verify all tests pass before continuing.

3. Review Local Changes

Use /review-local-changes for final multi-agent validation.

/review-local-changes

Address Critical and High priority findings before committing.

4. Create Commit

Use /commit to create a well-formatted conventional commit.

/commit

Quality Comparison

AspectFeature DevelopmentReliable Engineering
Agents1 (with self-reflection)3 generators + 3 judges
ExplorationSingle pathMultiple competing approaches
Issue Detection40-60% (self-critique)70-85% (competitive + judges)
CostLower4-6x higher
TimeFasterSlower
Best ForSimple, clear tasksCritical, ambiguous tasks

Advanced: Combining with Tree of Thoughts

For tasks requiring exploration before commitment, use /tree-of-thoughts first:

# Explore approaches first
/tree-of-thoughts "Design caching strategy for high-traffic API"

# Then implement the winning approach competitively
/do-competitively "Implement Redis-based caching with the write-through pattern identified above"

Advanced: Debate-Based Evaluation

For highest-stakes decisions where consensus is critical:

# Implement competitively
/do-competitively "Design payment processing flow" --output "src/services/payment.ts"

# Evaluate with iterative debate
/judge-with-debate --solution "src/services/payment.ts" --task "Payment processing implementation" --criteria "security:30,correctness:30,reliability:20,performance:20"

Tips

  • Reserve for critical work - The 4-6x cost overhead is only justified for high-stakes implementations
  • Specify criteria - Custom evaluation criteria improve judge alignment with your priorities
  • Review synthesis - Always validate the final synthesized solution makes coherent sense
  • Iterate if needed - If REDESIGN strategy triggers, provide more context on second attempt
  • Use for learning - Competitive execution reveals trade-offs between approaches

Theoretical Foundation

This workflow combines research-backed techniques:

TechniqueSourceBenefit
Constitutional AI Self-CritiqueBai et al., 202240-60% issue reduction before review
Chain of VerificationDhuliawala et al., 2023Reduces judge bias
Multi-Agent DebateDu et al., 2023Diverse perspectives improve reasoning
Self-ConsistencyWang et al., 2022Multiple paths improve reliability