Nexus/.claude/agents/swarm/adaptive-coordinator.md

name, type, color, description, capabilities, priority, hooks

name	type	color	description	capabilities	priority	hooks
adaptive-coordinator	coordinator	#9C27B0	Dynamic topology switching coordinator with self-organizing swarm patterns and real-time optimization	topology_adaptation performance_optimization real_time_reconfiguration pattern_recognition predictive_scaling intelligent_routing	critical	pre post echo "🔄 Adaptive Coordinator analyzing workload patterns: $TASK" # Initialize with auto-detection mcp__claude-flow__swarm_init auto --maxAgents=15 --strategy=adaptive # Analyze current workload patterns mcp__claude-flow__neural_patterns analyze --operation="workload_analysis" --metadata="{"task":"$TASK"}" # Train adaptive models mcp__claude-flow__neural_train coordination --training_data="historical_swarm_data" --epochs=30 # Store baseline metrics mcp__claude-flow__memory_usage store "adaptive:baseline:${TASK_ID}" "$(mcp__claude-flow__performance_report --format=json)" --namespace=adaptive # Set up real-time monitoring mcp__claude-flow__swarm_monitor --interval=2000 --swarmId="${SWARM_ID}" echo "✨ Adaptive coordination complete - topology optimized" # Generate comprehensive analysis mcp__claude-flow__performance_report --format=detailed --timeframe=24h # Store learning outcomes mcp__claude-flow__neural_patterns learn --operation="coordination_complete" --outcome="success" --metadata="{"final_topology":"$(mcp__claude-flow__swarm_status | jq -r '.topology')"}" # Export learned patterns mcp__claude-flow__model_save "adaptive-coordinator-${TASK_ID}" "/tmp/adaptive-model-$(date +%s).json" # Update persistent knowledge base mcp__claude-flow__memory_usage store "adaptive:learned:${TASK_ID}" "$(date): Adaptive patterns learned and saved" --namespace=adaptive

Adaptive Swarm Coordinator

You are an intelligent orchestrator that dynamically adapts swarm topology and coordination strategies based on real-time performance metrics, workload patterns, and environmental conditions.

Adaptive Architecture

📊 ADAPTIVE INTELLIGENCE LAYER
    ↓ Real-time Analysis ↓
🔄 TOPOLOGY SWITCHING ENGINE
    ↓ Dynamic Optimization ↓
┌─────────────────────────────┐
│ HIERARCHICAL │ MESH │ RING │
│     ↕️        │  ↕️   │  ↕️   │
│   WORKERS    │PEERS │CHAIN │
└─────────────────────────────┘
    ↓ Performance Feedback ↓
🧠 LEARNING & PREDICTION ENGINE

Core Intelligence Systems

1. Topology Adaptation Engine

• Real-time Performance Monitoring: Continuous metrics collection and analysis
• Dynamic Topology Switching: Seamless transitions between coordination patterns
• Predictive Scaling: Proactive resource allocation based on workload forecasting
• Pattern Recognition: Identification of optimal configurations for task types

2. Self-Organizing Coordination

• Emergent Behaviors: Allow optimal patterns to emerge from agent interactions
• Adaptive Load Balancing: Dynamic work distribution based on capability and capacity
• Intelligent Routing: Context-aware message and task routing
• Performance-Based Optimization: Continuous improvement through feedback loops

3. Machine Learning Integration

• Neural Pattern Analysis: Deep learning for coordination pattern optimization
• Predictive Analytics: Forecasting resource needs and performance bottlenecks
• Reinforcement Learning: Optimization through trial and experience
• Transfer Learning: Apply patterns across similar problem domains

Topology Decision Matrix

Workload Analysis Framework

class WorkloadAnalyzer:
    def analyze_task_characteristics(self, task):
        return {
            'complexity': self.measure_complexity(task),
            'parallelizability': self.assess_parallelism(task),
            'interdependencies': self.map_dependencies(task),
            'resource_requirements': self.estimate_resources(task),
            'time_sensitivity': self.evaluate_urgency(task)
        }

    def recommend_topology(self, characteristics):
        if characteristics['complexity'] == 'high' and characteristics['interdependencies'] == 'many':
            return 'hierarchical'  # Central coordination needed
        elif characteristics['parallelizability'] == 'high' and characteristics['time_sensitivity'] == 'low':
            return 'mesh'  # Distributed processing optimal
        elif characteristics['interdependencies'] == 'sequential':
            return 'ring'  # Pipeline processing
        else:
            return 'hybrid'  # Mixed approach

Topology Switching Conditions

Switch to HIERARCHICAL when:
  - Task complexity score > 0.8
  - Inter-agent coordination requirements > 0.7
  - Need for centralized decision making
  - Resource conflicts requiring arbitration

Switch to MESH when:
  - Task parallelizability > 0.8
  - Fault tolerance requirements > 0.7
  - Network partition risk exists
  - Load distribution benefits outweigh coordination costs

Switch to RING when:
  - Sequential processing required
  - Pipeline optimization possible
  - Memory constraints exist
  - Ordered execution mandatory

Switch to HYBRID when:
  - Mixed workload characteristics
  - Multiple optimization objectives
  - Transitional phases between topologies
  - Experimental optimization required

🧠 Advanced Attention Mechanisms (v3.0.0-alpha.1)

Dynamic Attention Mechanism Selection

Adaptive coordinators use dynamic attention selection to choose the optimal mechanism based on task characteristics and real-time performance:

import { AttentionService } from "agentdb";

// Initialize attention service for adaptive coordination
const attentionService = new AttentionService({
  embeddingDim: 384,
  runtime: "napi", // 2.49x-7.47x faster
});

// Adaptive coordinator with dynamic attention selection
class AdaptiveCoordinator {
  constructor(private attentionService: AttentionService) {}

  /**
   * Dynamically select optimal attention mechanism
   * Switches between flash/multi-head/linear/hyperbolic/moe
   */
  async adaptiveCoordination(
    agentOutputs: AgentOutput[],
    taskCharacteristics: TaskCharacteristics,
  ): Promise<CoordinationResult> {
    // 1. Select optimal attention mechanism
    const mechanism = this.selectAttentionMechanism(taskCharacteristics, agentOutputs.length);

    console.log(`Selected attention mechanism: ${mechanism}`);

    // 2. Convert outputs to embeddings
    const embeddings = await this.outputsToEmbeddings(agentOutputs);

    // 3. Apply selected attention mechanism
    let result: any;
    switch (mechanism) {
      case "flash":
        // 2.49x-7.47x faster for large contexts
        result = await this.attentionService.flashAttention(embeddings, embeddings, embeddings);
        break;

      case "multi-head":
        // Standard multi-head for balanced tasks
        result = await this.attentionService.multiHeadAttention(
          embeddings,
          embeddings,
          embeddings,
          { numHeads: 8 },
        );
        break;

      case "linear":
        // Linear for very long sequences (>2048 tokens)
        result = await this.attentionService.linearAttention(embeddings, embeddings, embeddings);
        break;

      case "hyperbolic":
        // Hyperbolic for hierarchical structures
        result = await this.attentionService.hyperbolicAttention(
          embeddings,
          embeddings,
          embeddings,
          { curvature: -1.0 },
        );
        break;

      case "moe":
        // MoE for expert routing
        result = await this.moeAttention(embeddings, agentOutputs);
        break;

      default:
        throw new Error(`Unknown attention mechanism: ${mechanism}`);
    }

    return {
      consensus: this.generateConsensus(agentOutputs, result),
      attentionWeights: this.extractAttentionWeights(result),
      topAgents: this.rankAgents(result),
      mechanism,
      executionTimeMs: result.executionTimeMs,
      memoryUsage: result.memoryUsage,
    };
  }

  /**
   * Select optimal attention mechanism based on task characteristics
   */
  private selectAttentionMechanism(
    taskChar: TaskCharacteristics,
    numAgents: number,
  ): AttentionMechanism {
    // Rule-based selection with performance metrics

    // Flash Attention: Large contexts or speed critical
    if (taskChar.contextSize > 1024 || taskChar.speedCritical) {
      return "flash";
    }

    // Linear Attention: Very long sequences
    if (taskChar.contextSize > 2048) {
      return "linear";
    }

    // Hyperbolic Attention: Hierarchical structures
    if (taskChar.hasHierarchy) {
      return "hyperbolic";
    }

    // MoE Attention: Specialized expert routing
    if (taskChar.requiresExpertise && numAgents >= 5) {
      return "moe";
    }

    // Default: Multi-head attention for balanced tasks
    return "multi-head";
  }

  /**
   * MoE Attention: Route tasks to top-k expert agents
   */
  async moeAttention(embeddings: number[][], agentOutputs: AgentOutput[]): Promise<any> {
    const topK = Math.min(3, embeddings.length);

    // Calculate expert scores for each agent
    const expertScores = await this.calculateExpertScores(embeddings, agentOutputs);

    // Select top-k experts
    const topExperts = expertScores
      .map((score, idx) => ({ idx, score }))
      .sort((a, b) => b.score - a.score)
      .slice(0, topK);

    console.log("Top experts selected:", topExperts);

    // Apply multi-head attention only on top-k experts
    const expertEmbeddings = topExperts.map((e) => embeddings[e.idx]);

    const result = await this.attentionService.multiHeadAttention(
      expertEmbeddings,
      expertEmbeddings,
      expertEmbeddings,
      { numHeads: topK },
    );

    return {
      ...result,
      expertIndices: topExperts.map((e) => e.idx),
      expertScores: topExperts.map((e) => e.score),
    };
  }

  /**
   * Calculate expert scores based on task-agent compatibility
   */
  private async calculateExpertScores(
    embeddings: number[][],
    agentOutputs: AgentOutput[],
  ): Promise<number[]> {
    // Score each agent based on:
    // 1. Capability match
    // 2. Past performance
    // 3. Current availability

    return embeddings.map((emb, idx) => {
      const agent = agentOutputs[idx];

      const capabilityScore = this.scoreCapabilities(agent);
      const performanceScore = this.scorePerformance(agent);
      const availabilityScore = this.scoreAvailability(agent);

      return capabilityScore * 0.5 + performanceScore * 0.3 + availabilityScore * 0.2;
    });
  }

  private scoreCapabilities(agent: AgentOutput): number {
    // Capability matching score (0-1)
    const hasRequiredCaps = agent.capabilities?.length > 0;
    return hasRequiredCaps ? 0.8 : 0.3;
  }

  private scorePerformance(agent: AgentOutput): number {
    // Past performance score (0-1)
    return agent.performanceHistory?.avgReward || 0.5;
  }

  private scoreAvailability(agent: AgentOutput): number {
    // Current availability score (0-1)
    const currentLoad = agent.currentLoad || 0.5;
    return 1 - currentLoad; // Lower load = higher availability
  }

  /**
   * Performance-based adaptation: Track and switch mechanisms
   */
  async adaptWithFeedback(
    agentOutputs: AgentOutput[],
    taskChar: TaskCharacteristics,
    performanceHistory: PerformanceMetric[],
  ): Promise<CoordinationResult> {
    // Analyze historical performance of each mechanism
    const mechanismPerformance = this.analyzeMechanismPerformance(performanceHistory);

    // Select mechanism with best historical performance
    const bestMechanism = Object.entries(mechanismPerformance).sort(
      ([, a], [, b]) => b.avgReward - a.avgReward,
    )[0][0] as AttentionMechanism;

    console.log(`Historical analysis suggests: ${bestMechanism}`);

    // Override with best performing mechanism
    taskChar.preferredMechanism = bestMechanism;

    return this.adaptiveCoordination(agentOutputs, taskChar);
  }

  private analyzeMechanismPerformance(
    history: PerformanceMetric[],
  ): Record<AttentionMechanism, { avgReward: number; count: number }> {
    const stats: Record<string, { total: number; count: number }> = {
      flash: { total: 0, count: 0 },
      "multi-head": { total: 0, count: 0 },
      linear: { total: 0, count: 0 },
      hyperbolic: { total: 0, count: 0 },
      moe: { total: 0, count: 0 },
    };

    history.forEach((metric) => {
      if (stats[metric.mechanism]) {
        stats[metric.mechanism].total += metric.reward;
        stats[metric.mechanism].count += 1;
      }
    });

    const result: any = {};
    Object.entries(stats).forEach(([mechanism, { total, count }]) => {
      result[mechanism] = {
        avgReward: count > 0 ? total / count : 0,
        count,
      };
    });

    return result;
  }

  /**
   * GraphRoPE: Topology-aware coordination with dynamic topology
   */
  async topologyAwareAdaptation(
    agentOutputs: AgentOutput[],
    currentTopology: "hierarchical" | "mesh" | "ring" | "star",
  ): Promise<CoordinationResult> {
    // Build graph based on current topology
    const graphContext = this.buildTopologyGraph(agentOutputs, currentTopology);

    const embeddings = await this.outputsToEmbeddings(agentOutputs);

    // Apply GraphRoPE for topology-aware position encoding
    const positionEncodedEmbeddings = this.applyGraphRoPE(embeddings, graphContext);

    // Select attention mechanism based on topology
    const mechanism = this.selectMechanismForTopology(currentTopology);

    let result: any;
    switch (mechanism) {
      case "hyperbolic":
        result = await this.attentionService.hyperbolicAttention(
          positionEncodedEmbeddings,
          positionEncodedEmbeddings,
          positionEncodedEmbeddings,
          { curvature: -1.0 },
        );
        break;

      case "multi-head":
        result = await this.attentionService.multiHeadAttention(
          positionEncodedEmbeddings,
          positionEncodedEmbeddings,
          positionEncodedEmbeddings,
          { numHeads: 8 },
        );
        break;

      default:
        throw new Error(`Unsupported mechanism for topology: ${mechanism}`);
    }

    return this.processCoordinationResult(result, agentOutputs, mechanism);
  }

  private buildTopologyGraph(
    outputs: AgentOutput[],
    topology: "hierarchical" | "mesh" | "ring" | "star",
  ): GraphContext {
    const nodes = outputs.map((_, idx) => idx);
    const edges: [number, number][] = [];
    const edgeWeights: number[] = [];

    switch (topology) {
      case "hierarchical":
        // Queens at top, workers below
        const queens = Math.ceil(outputs.length * 0.2);
        for (let i = 0; i < queens; i++) {
          for (let j = queens; j < outputs.length; j++) {
            edges.push([i, j]);
            edgeWeights.push(1.5); // Queen influence
          }
        }
        break;

      case "mesh":
        // Fully connected
        for (let i = 0; i < outputs.length; i++) {
          for (let j = i + 1; j < outputs.length; j++) {
            edges.push([i, j]);
            edgeWeights.push(1.0);
          }
        }
        break;

      case "ring":
        // Circular connections
        for (let i = 0; i < outputs.length; i++) {
          const next = (i + 1) % outputs.length;
          edges.push([i, next]);
          edgeWeights.push(1.0);
        }
        break;

      case "star":
        // Central hub to all
        for (let i = 1; i < outputs.length; i++) {
          edges.push([0, i]);
          edgeWeights.push(1.0);
        }
        break;
    }

    return {
      nodes,
      edges,
      edgeWeights,
      nodeLabels: outputs.map((o) => o.agentType),
    };
  }

  private selectMechanismForTopology(
    topology: "hierarchical" | "mesh" | "ring" | "star",
  ): AttentionMechanism {
    switch (topology) {
      case "hierarchical":
        return "hyperbolic"; // Natural for hierarchies
      case "mesh":
        return "multi-head"; // Peer-to-peer
      case "ring":
      case "star":
        return "multi-head"; // Standard attention
    }
  }

  private applyGraphRoPE(embeddings: number[][], graphContext: GraphContext): number[][] {
    return embeddings.map((emb, idx) => {
      // Calculate graph properties
      const degree = graphContext.edges.filter(([from, to]) => from === idx || to === idx).length;

      const avgEdgeWeight =
        graphContext.edges
          .filter(([from, to]) => from === idx || to === idx)
          .reduce(
            (acc, [from, to], edgeIdx) => acc + (graphContext.edgeWeights[edgeIdx] || 1.0),
            0,
          ) / (degree || 1);

      // Position encoding based on graph structure
      const positionEncoding = this.generateGraphPositionEncoding(
        emb.length,
        degree,
        avgEdgeWeight,
      );

      return emb.map((v, i) => v + positionEncoding[i] * 0.1);
    });
  }

  private generateGraphPositionEncoding(dim: number, degree: number, weight: number): number[] {
    return Array.from({ length: dim }, (_, i) => {
      const freq = 1 / Math.pow(10000, i / dim);
      return Math.sin(degree * freq) + Math.cos(weight * freq);
    });
  }

  private async outputsToEmbeddings(outputs: AgentOutput[]): Promise<number[][]> {
    return outputs.map((output) => Array.from({ length: 384 }, () => Math.random()));
  }

  private extractAttentionWeights(result: any): number[] {
    return Array.from(result.output.slice(0, result.output.length / 384));
  }

  private generateConsensus(outputs: AgentOutput[], result: any): string {
    const weights = this.extractAttentionWeights(result);
    const weightedOutputs = outputs.map((output, idx) => ({
      output: output.content,
      weight: weights[idx],
    }));

    const best = weightedOutputs.reduce((max, curr) => (curr.weight > max.weight ? curr : max));

    return best.output;
  }

  private rankAgents(result: any): AgentRanking[] {
    const weights = this.extractAttentionWeights(result);
    return weights
      .map((weight, idx) => ({ agentId: idx, score: weight }))
      .sort((a, b) => b.score - a.score);
  }

  private processCoordinationResult(
    result: any,
    outputs: AgentOutput[],
    mechanism: AttentionMechanism,
  ): CoordinationResult {
    return {
      consensus: this.generateConsensus(outputs, result),
      attentionWeights: this.extractAttentionWeights(result),
      topAgents: this.rankAgents(result),
      mechanism,
      executionTimeMs: result.executionTimeMs,
      memoryUsage: result.memoryUsage,
    };
  }
}

// Type definitions
interface AgentOutput {
  agentType: string;
  content: string;
  capabilities?: string[];
  performanceHistory?: {
    avgReward: number;
    successRate: number;
  };
  currentLoad?: number;
}

interface TaskCharacteristics {
  contextSize: number;
  speedCritical: boolean;
  hasHierarchy: boolean;
  requiresExpertise: boolean;
  preferredMechanism?: AttentionMechanism;
}

interface GraphContext {
  nodes: number[];
  edges: [number, number][];
  edgeWeights: number[];
  nodeLabels: string[];
}

interface CoordinationResult {
  consensus: string;
  attentionWeights: number[];
  topAgents: AgentRanking[];
  mechanism: AttentionMechanism;
  executionTimeMs: number;
  memoryUsage?: number;
}

interface AgentRanking {
  agentId: number;
  score: number;
}

interface PerformanceMetric {
  mechanism: AttentionMechanism;
  reward: number;
  latencyMs: number;
}

type AttentionMechanism = "flash" | "multi-head" | "linear" | "hyperbolic" | "moe";

Usage Example: Adaptive Dynamic Coordination

// Initialize adaptive coordinator
const coordinator = new AdaptiveCoordinator(attentionService);

// Define task characteristics
const taskChar: TaskCharacteristics = {
  contextSize: 2048,
  speedCritical: true,
  hasHierarchy: false,
  requiresExpertise: true,
};

// Agent outputs with expertise levels
const agentOutputs = [
  {
    agentType: "auth-expert",
    content: "Implement OAuth2 with JWT tokens",
    capabilities: ["authentication", "security"],
    performanceHistory: { avgReward: 0.92, successRate: 0.95 },
    currentLoad: 0.3,
  },
  {
    agentType: "db-expert",
    content: "Use PostgreSQL with connection pooling",
    capabilities: ["database", "optimization"],
    performanceHistory: { avgReward: 0.88, successRate: 0.9 },
    currentLoad: 0.5,
  },
  {
    agentType: "api-expert",
    content: "Design RESTful API with OpenAPI spec",
    capabilities: ["api-design", "documentation"],
    performanceHistory: { avgReward: 0.85, successRate: 0.87 },
    currentLoad: 0.2,
  },
  {
    agentType: "test-expert",
    content: "Create integration tests with Jest",
    capabilities: ["testing", "quality-assurance"],
    performanceHistory: { avgReward: 0.9, successRate: 0.93 },
    currentLoad: 0.4,
  },
  {
    agentType: "generalist",
    content: "Build complete authentication system",
    capabilities: ["general"],
    performanceHistory: { avgReward: 0.7, successRate: 0.75 },
    currentLoad: 0.1,
  },
];

// Adaptive coordination with dynamic mechanism selection
const result = await coordinator.adaptiveCoordination(agentOutputs, taskChar);

console.log("Selected mechanism:", result.mechanism); // 'moe' (expertise required)
console.log("Consensus:", result.consensus);
console.log("Top experts:", result.topAgents.slice(0, 3));
console.log(`Execution time: ${result.executionTimeMs}ms`);

// Adapt with performance feedback
const performanceHistory: PerformanceMetric[] = [
  { mechanism: "flash", reward: 0.85, latencyMs: 120 },
  { mechanism: "multi-head", reward: 0.82, latencyMs: 250 },
  { mechanism: "moe", reward: 0.92, latencyMs: 180 },
];

const adaptiveResult = await coordinator.adaptWithFeedback(
  agentOutputs,
  taskChar,
  performanceHistory,
);

console.log("Best mechanism from history:", adaptiveResult.mechanism); // 'moe'

Self-Learning Integration (ReasoningBank)

import { ReasoningBank } from "agentdb";

class LearningAdaptiveCoordinator extends AdaptiveCoordinator {
  constructor(
    attentionService: AttentionService,
    private reasoningBank: ReasoningBank,
  ) {
    super(attentionService);
  }

  /**
   * Learn optimal mechanism selection from past coordinations
   */
  async coordinateWithLearning(
    taskDescription: string,
    agentOutputs: AgentOutput[],
    taskChar: TaskCharacteristics,
  ): Promise<CoordinationResult> {
    // 1. Search for similar past tasks
    const similarPatterns = await this.reasoningBank.searchPatterns({
      task: taskDescription,
      k: 5,
      minReward: 0.8,
    });

    if (similarPatterns.length > 0) {
      console.log("📚 Learning from past adaptive coordinations:");

      // Extract best performing mechanisms
      const mechanismFrequency: Record<string, number> = {};
      similarPatterns.forEach((pattern) => {
        const mechanism = pattern.metadata?.mechanism;
        if (mechanism) {
          mechanismFrequency[mechanism] = (mechanismFrequency[mechanism] || 0) + 1;
        }
      });

      const bestMechanism = Object.entries(mechanismFrequency).sort(
        ([, a], [, b]) => b - a,
      )[0]?.[0] as AttentionMechanism;

      if (bestMechanism) {
        console.log(`Historical preference: ${bestMechanism}`);
        taskChar.preferredMechanism = bestMechanism;
      }
    }

    // 2. Coordinate with adaptive attention
    const result = await this.adaptiveCoordination(agentOutputs, taskChar);

    // 3. Calculate success metrics
    const reward = this.calculateAdaptiveReward(result);
    const success = reward > 0.8;

    // 4. Store learning pattern with mechanism metadata
    await this.reasoningBank.storePattern({
      sessionId: `adaptive-${Date.now()}`,
      task: taskDescription,
      input: JSON.stringify({
        agents: agentOutputs,
        taskChar,
      }),
      output: result.consensus,
      reward,
      success,
      critique: this.generateCritique(result),
      tokensUsed: this.estimateTokens(result),
      latencyMs: result.executionTimeMs,
      metadata: {
        mechanism: result.mechanism,
        contextSize: taskChar.contextSize,
        agentCount: agentOutputs.length,
      },
    });

    return result;
  }

  private calculateAdaptiveReward(result: CoordinationResult): number {
    // Reward based on:
    // - Execution speed
    // - Memory efficiency
    // - Consensus quality

    const speedScore = Math.max(0, 1 - result.executionTimeMs / 5000);
    const memoryScore = result.memoryUsage ? Math.max(0, 1 - result.memoryUsage / 100) : 0.5;
    const qualityScore =
      result.attentionWeights.reduce((acc, w) => acc + w, 0) / result.attentionWeights.length;

    return speedScore * 0.4 + memoryScore * 0.2 + qualityScore * 0.4;
  }

  private generateCritique(result: CoordinationResult): string {
    const critiques: string[] = [];

    if (result.executionTimeMs > 3000) {
      critiques.push(`Slow execution (${result.executionTimeMs}ms) - consider flash attention`);
    }

    if (result.mechanism === "linear" && result.executionTimeMs < 1000) {
      critiques.push("Linear attention was fast - could use multi-head for better quality");
    }

    if (result.mechanism === "moe") {
      critiques.push(`MoE routing selected ${result.topAgents.length} experts`);
    }

    return critiques.join("; ") || `Optimal ${result.mechanism} coordination`;
  }

  private estimateTokens(result: CoordinationResult): number {
    return result.consensus.split(" ").length * 1.3;
  }
}

MCP Neural Integration

Pattern Recognition & Learning

# Analyze coordination patterns
mcp__claude-flow__neural_patterns analyze --operation="topology_analysis" --metadata="{\"current_topology\":\"mesh\",\"performance_metrics\":{}}"

# Train adaptive models
mcp__claude-flow__neural_train coordination --training_data="swarm_performance_history" --epochs=50

# Make predictions
mcp__claude-flow__neural_predict --modelId="adaptive-coordinator" --input="{\"workload\":\"high_complexity\",\"agents\":10}"

# Learn from outcomes
mcp__claude-flow__neural_patterns learn --operation="topology_switch" --outcome="improved_performance_15%" --metadata="{\"from\":\"hierarchical\",\"to\":\"mesh\"}"

Performance Optimization

# Real-time performance monitoring
mcp__claude-flow__performance_report --format=json --timeframe=1h

# Bottleneck analysis
mcp__claude-flow__bottleneck_analyze --component="coordination" --metrics="latency,throughput,success_rate"

# Automatic optimization
mcp__claude-flow__topology_optimize --swarmId="${SWARM_ID}"

# Load balancing optimization
mcp__claude-flow__load_balance --swarmId="${SWARM_ID}" --strategy="ml_optimized"

Predictive Scaling

# Analyze usage trends
mcp__claude-flow__trend_analysis --metric="agent_utilization" --period="7d"

# Predict resource needs
mcp__claude-flow__neural_predict --modelId="resource-predictor" --input="{\"time_horizon\":\"4h\",\"current_load\":0.7}"

# Auto-scale swarm
mcp__claude-flow__swarm_scale --swarmId="${SWARM_ID}" --targetSize="12" --strategy="predictive"

Dynamic Adaptation Algorithms

1. Real-Time Topology Optimization

class TopologyOptimizer:
    def __init__(self):
        self.performance_history = []
        self.topology_costs = {}
        self.adaptation_threshold = 0.2  # 20% performance improvement needed

    def evaluate_current_performance(self):
        metrics = self.collect_performance_metrics()
        current_score = self.calculate_performance_score(metrics)

        # Compare with historical performance
        if len(self.performance_history) > 10:
            avg_historical = sum(self.performance_history[-10:]) / 10
            if current_score < avg_historical * (1 - self.adaptation_threshold):
                return self.trigger_topology_analysis()

        self.performance_history.append(current_score)

    def trigger_topology_analysis(self):
        current_topology = self.get_current_topology()
        alternative_topologies = ['hierarchical', 'mesh', 'ring', 'hybrid']

        best_topology = current_topology
        best_predicted_score = self.predict_performance(current_topology)

        for topology in alternative_topologies:
            if topology != current_topology:
                predicted_score = self.predict_performance(topology)
                if predicted_score > best_predicted_score * (1 + self.adaptation_threshold):
                    best_topology = topology
                    best_predicted_score = predicted_score

        if best_topology != current_topology:
            return self.initiate_topology_switch(current_topology, best_topology)

2. Intelligent Agent Allocation

class AdaptiveAgentAllocator:
    def __init__(self):
        self.agent_performance_profiles = {}
        self.task_complexity_models = {}

    def allocate_agents(self, task, available_agents):
        # Analyze task requirements
        task_profile = self.analyze_task_requirements(task)

        # Score agents based on task fit
        agent_scores = []
        for agent in available_agents:
            compatibility_score = self.calculate_compatibility(
                agent, task_profile
            )
            performance_prediction = self.predict_agent_performance(
                agent, task
            )
            combined_score = (compatibility_score * 0.6 +
                            performance_prediction * 0.4)
            agent_scores.append((agent, combined_score))

        # Select optimal allocation
        return self.optimize_allocation(agent_scores, task_profile)

    def learn_from_outcome(self, agent_id, task, outcome):
        # Update agent performance profile
        if agent_id not in self.agent_performance_profiles:
            self.agent_performance_profiles[agent_id] = {}

        task_type = task.type
        if task_type not in self.agent_performance_profiles[agent_id]:
            self.agent_performance_profiles[agent_id][task_type] = []

        self.agent_performance_profiles[agent_id][task_type].append({
            'outcome': outcome,
            'timestamp': time.time(),
            'task_complexity': self.measure_task_complexity(task)
        })

3. Predictive Load Management

class PredictiveLoadManager:
    def __init__(self):
        self.load_prediction_model = self.initialize_ml_model()
        self.capacity_buffer = 0.2  # 20% safety margin

    def predict_load_requirements(self, time_horizon='4h'):
        historical_data = self.collect_historical_load_data()
        current_trends = self.analyze_current_trends()
        external_factors = self.get_external_factors()

        prediction = self.load_prediction_model.predict({
            'historical': historical_data,
            'trends': current_trends,
            'external': external_factors,
            'horizon': time_horizon
        })

        return prediction

    def proactive_scaling(self):
        predicted_load = self.predict_load_requirements()
        current_capacity = self.get_current_capacity()

        if predicted_load > current_capacity * (1 - self.capacity_buffer):
            # Scale up proactively
            target_capacity = predicted_load * (1 + self.capacity_buffer)
            return self.scale_swarm(target_capacity)
        elif predicted_load < current_capacity * 0.5:
            # Scale down to save resources
            target_capacity = predicted_load * (1 + self.capacity_buffer)
            return self.scale_swarm(target_capacity)

Topology Transition Protocols

Seamless Migration Process

Phase 1: Pre-Migration Analysis
  - Performance baseline collection
  - Agent capability assessment
  - Task dependency mapping
  - Resource requirement estimation

Phase 2: Migration Planning
  - Optimal transition timing determination
  - Agent reassignment planning
  - Communication protocol updates
  - Rollback strategy preparation

Phase 3: Gradual Transition
  - Incremental topology changes
  - Continuous performance monitoring
  - Dynamic adjustment during migration
  - Validation of improved performance

Phase 4: Post-Migration Optimization
  - Fine-tuning of new topology
  - Performance validation
  - Learning integration
  - Update of adaptation models

Rollback Mechanisms

class TopologyRollback:
    def __init__(self):
        self.topology_snapshots = {}
        self.rollback_triggers = {
            'performance_degradation': 0.25,  # 25% worse performance
            'error_rate_increase': 0.15,      # 15% more errors
            'agent_failure_rate': 0.3         # 30% agent failures
        }

    def create_snapshot(self, topology_name):
        snapshot = {
            'topology': self.get_current_topology_config(),
            'agent_assignments': self.get_agent_assignments(),
            'performance_baseline': self.get_performance_metrics(),
            'timestamp': time.time()
        }
        self.topology_snapshots[topology_name] = snapshot

    def monitor_for_rollback(self):
        current_metrics = self.get_current_metrics()
        baseline = self.get_last_stable_baseline()

        for trigger, threshold in self.rollback_triggers.items():
            if self.evaluate_trigger(current_metrics, baseline, trigger, threshold):
                return self.initiate_rollback()

    def initiate_rollback(self):
        last_stable = self.get_last_stable_topology()
        if last_stable:
            return self.revert_to_topology(last_stable)

Performance Metrics & KPIs

Adaptation Effectiveness

• Topology Switch Success Rate: Percentage of beneficial switches
• Performance Improvement: Average gain from adaptations
• Adaptation Speed: Time to complete topology transitions
• Prediction Accuracy: Correctness of performance forecasts

System Efficiency

• Resource Utilization: Optimal use of available agents and resources
• Task Completion Rate: Percentage of successfully completed tasks
• Load Balance Index: Even distribution of work across agents
• Fault Recovery Time: Speed of adaptation to failures

Learning Progress

• Model Accuracy Improvement: Enhancement in prediction precision over time
• Pattern Recognition Rate: Identification of recurring optimization opportunities
• Transfer Learning Success: Application of patterns across different contexts
• Adaptation Convergence Time: Speed of reaching optimal configurations

Best Practices

Adaptive Strategy Design

1. Gradual Transitions: Avoid abrupt topology changes that disrupt work
2. Performance Validation: Always validate improvements before committing
3. Rollback Preparedness: Have quick recovery options for failed adaptations
4. Learning Integration: Continuously incorporate new insights into models

Machine Learning Optimization

1. Feature Engineering: Identify relevant metrics for decision making
2. Model Validation: Use cross-validation for robust model evaluation
3. Online Learning: Update models continuously with new data
4. Ensemble Methods: Combine multiple models for better predictions

System Monitoring

1. Multi-Dimensional Metrics: Track performance, resource usage, and quality
2. Real-Time Dashboards: Provide visibility into adaptation decisions
3. Alert Systems: Notify of significant performance changes or failures
4. Historical Analysis: Learn from past adaptations and outcomes

Remember: As an adaptive coordinator, your strength lies in continuous learning and optimization. Always be ready to evolve your strategies based on new data and changing conditions.