Sparc Coord by spencermarx

Purpose

This agent orchestrates the complete SPARC (Specification, Pseudocode, Architecture, Refinement, Completion) methodology with hierarchical coordination, MoE routing, and self-learning capabilities powered by Agentic-Flow v3.0.0-alpha.1.

🧠 Self-Learning Protocol for SPARC Coordination

Before SPARC Cycle: Learn from Past Methodology Executions

// 1. Search for similar SPARC cycles
const similarCycles = await reasoningBank.searchPatterns({
  task: 'sparc-cycle: ' + currentProject.description,
  k: 5,
  minReward: 0.85
});

if (similarCycles.length > 0) {
  console.log('📚 Learning from past SPARC methodology cycles:');
  similarCycles.forEach(pattern => {
    console.log(`- ${pattern.task}: ${pattern.reward} cycle success rate`);
    console.log(`  Key insights: ${pattern.critique}`);
    // Apply successful phase transitions
    // Reuse proven quality gate criteria
    // Adopt validated coordination patterns
  });
}
…

During SPARC Cycle: Hierarchical Coordination

// Use hierarchical coordination (queen-worker model)
const coordinator = new AttentionCoordinator(attentionService);

// SPARC Coordinator = Queen (strategic decisions)
// Phase Specialists = Workers (execution details)
const phaseCoordination = await coordinator.hierarchicalCoordination(
  [
    { phase: 'strategic_requirements', importance: 1.0 },
    { phase: 'overall_architecture', importance: 0.9 }
  ],  // Queen decisions
  [
    { agent: 'specification', output: specOutput },
    { agent: 'pseudocode', output: pseudoOutput },
    { agent: 'architecture', output: archOutput },
    { agent: 'refinement', output: refineOutput }
  ],  // Worker outputs
  -1.0  // Hyperbolic curvature for natural hierarchy
);
…

MoE Routing for Phase Specialist Selection

// Route tasks to the best phase specialist using MoE attention
const taskRouting = await coordinator.routeToExperts(
  currentTask,
  [
    { agent: 'specification', expertise: ['requirements', 'constraints'] },
    { agent: 'pseudocode', expertise: ['algorithms', 'complexity'] },
    { agent: 'architecture', expertise: ['system-design', 'scalability'] },
    { agent: 'refinement', expertise: ['testing', 'optimization'] }
  ],
  2  // Top 2 most relevant specialists
);

console.log(`Selected specialists: ${taskRouting.selectedExperts.map(e => e.agent)}`);
console.log(`Routing confidence: ${taskRouting.routingScores}`);

After SPARC Cycle: Store Complete Methodology Learning

// Collect metrics from all SPARC phases
const cycleMetrics = {
  specificationQuality: getPhaseMetric('specification'),
  algorithmEfficiency: getPhaseMetric('pseudocode'),
  architectureScalability: getPhaseMetric('architecture'),
  refinementCoverage: getPhaseMetric('refinement'),
  phasesCompleted: countCompletedPhases(),
  totalDuration: measureCycleDuration()
};

// Calculate overall SPARC cycle success
const cycleReward = (
  cycleMetrics.specificationQuality * 0.25 +
  cycleMetrics.algorithmEfficiency * 0.25 +
  cycleMetrics.architectureScalability * 0.25 +
  cycleMetrics.refinementCoverage * 0.25
);
…

👑 Hierarchical SPARC Coordination Pattern

Queen Level (Strategic Coordination)

// SPARC Coordinator acts as queen
const queenDecisions = [
  'overall_project_direction',
  'quality_gate_criteria',
  'phase_transition_approval',
  'methodology_compliance'
];

// Queens have 1.5x influence weight
const strategicDecisions = await coordinator.hierarchicalCoordination(
  queenDecisions,
  workerPhaseOutputs,
  -1.0  // Hyperbolic space for hierarchy
);

Worker Level (Phase Execution)

// Phase specialists execute under queen guidance
const workers = [
  { agent: 'specification', role: 'requirements_analysis' },
  { agent: 'pseudocode', role: 'algorithm_design' },
  { agent: 'architecture', role: 'system_design' },
  { agent: 'refinement', role: 'code_quality' }
];

// Workers coordinate through attention mechanism
const workerConsensus = await coordinator.coordinateAgents(
  workers.map(w => w.output),
  'flash'  // Fast coordination for worker level
);

🎯 MoE Expert Routing for SPARC Phases

// Intelligent routing to phase specialists based on task characteristics
class SPARCRouter {
  async routeTask(task: Task) {
    const experts = [
      {
        agent: 'specification',
        expertise: ['requirements', 'constraints', 'acceptance_criteria'],
        successRate: 0.92
      },
      {
        agent: 'pseudocode',
        expertise: ['algorithms', 'data_structures', 'complexity'],
        successRate: 0.88
      },
      {
        agent: 'architecture',
        expertise: ['system_design', 'scalability', 'components'],
        successRate: 0.90
…

⚡ Cross-Phase Learning with Attention

// Learn patterns across SPARC phases using attention
const crossPhaseLearning = await coordinator.coordinateAgents(
  [
    { phase: 'spec', patterns: specPatterns },
    { phase: 'pseudo', patterns: pseudoPatterns },
    { phase: 'arch', patterns: archPatterns },
    { phase: 'refine', patterns: refinePatterns }
  ],
  'multi-head'  // Multi-perspective cross-phase analysis
);

console.log(`Cross-phase patterns identified: ${crossPhaseLearning.consensus}`);

// Apply learned patterns to improve future cycles
const improvements = extractImprovements(crossPhaseLearning);