Performance Tuning

Performance Tuning

Optimization strategies for maximizing Agentwise performance and efficiency.

Overview

Performance tuning in Agentwise involves optimizing multiple layers: agent execution, task distribution, token usage, system resources, and user experience. This guide covers strategies for achieving optimal performance.

Performance Metrics

Key Performance Indicators

{
  "core_metrics": {
    "task_completion_time": {
      "target": "< 5 minutes per task",
      "measurement": "end-to-end task duration",
      "optimization_impact": "5-10x improvement possible"
    },
    "token_efficiency": {
      "target": "30-40% reduction",
      "measurement": "tokens saved vs baseline",
      "optimization_impact": "Direct cost reduction"
    },
    "agent_utilization": {
      "target": "> 85% utilization",
      "measurement": "active time / total time",
      "optimization_impact": "Resource efficiency"
    },
    "system_throughput": {
      "target": "10+ concurrent projects",
      "measurement": "projects per hour",
      "optimization_impact": "Scalability"
    }
  }
}

Performance Monitoring

// Real-time performance monitoring
class PerformanceMonitor {
  constructor() {
    this.metrics = new Map();
    this.alerts = new AlertManager();
    this.collectors = [
      new TaskMetricsCollector(),
      new TokenUsageCollector(),
      new AgentPerformanceCollector(),
      new SystemResourceCollector()
    ];
  }
  
  startMonitoring() {
    this.collectors.forEach(collector => {
      collector.on('metric', (metric) => {
        this.recordMetric(metric);
        this.checkThresholds(metric);
      });
      collector.start();
    });
  }
  
  async getPerformanceReport() {
    return {
      current: await this.getCurrentMetrics(),
      trends: await this.getTrends(),
      recommendations: await this.getOptimizationRecommendations()
    };
  }
}

Agent Performance Optimization

Agent Pool Management

// Optimal agent pool configuration
{
  "agent_pool_optimization": {
    "sizing": {
      "concurrent_agents": "3-5 for optimal balance",
      "agent_instances": {
        "frontend": 2,
        "backend": 2, 
        "database": 1,
        "testing": 1,
        "devops": 1
      }
    },
    "load_balancing": {
      "strategy": "weighted_round_robin",
      "weights": {
        "frontend": 0.3,
        "backend": 0.3,
        "database": 0.2,
        "testing": 0.1,
        "devops": 0.1
      }
    },
    "scaling": {
      "auto_scale": true,
      "scale_up_threshold": "queue_depth > 5",
      "scale_down_threshold": "utilization < 30%"
    }
  }
}

Agent Specialization

// Specialized agent optimization
class OptimizedFrontendAgent extends FrontendSpecialist {
  constructor() {
    super();
    this.templateCache = new Map();
    this.componentPatterns = new PatternRecognizer();
    this.codeGenerator = new OptimizedCodeGenerator();
  }
  
  async processTask(task, context) {
    // Pre-processing optimization
    const optimizedTask = await this.optimizeTask(task);
    
    // Template reuse
    const template = await this.findOptimalTemplate(optimizedTask);
    if (template) {
      return await this.generateFromTemplate(template, optimizedTask);
    }
    
    // Pattern-based generation
    const pattern = this.componentPatterns.recognize(optimizedTask);
    if (pattern) {
      return await this.generateFromPattern(pattern, optimizedTask);
    }
    
    // Fallback to full generation
    return await super.processTask(optimizedTask, context);
  }
}

Task Distribution Optimization

Intelligent Task Scheduling

{
  "scheduling_optimization": {
    "algorithms": {
      "dependency_aware": "Schedule based on task dependencies",
      "resource_optimal": "Consider agent capabilities and load",
      "priority_weighted": "High-priority tasks get preference",
      "predictive": "Use ML to predict optimal scheduling"
    },
    "optimizations": {
      "parallel_execution": "Maximize concurrent task execution",
      "batch_processing": "Group similar tasks for efficiency",
      "pipeline_optimization": "Optimize task pipeline flow"
    }
  }
}

Dynamic Load Balancing

class DynamicLoadBalancer {
  constructor() {
    this.agentMetrics = new Map();
    this.taskQueue = new PriorityQueue();
    this.predictor = new PerformancePredictor();
  }
  
  selectOptimalAgent(task) {
    const candidates = this.getCapableAgents(task);
    
    // Score each candidate
    const scores = candidates.map(agent => ({
      agent,
      score: this.calculateAgentScore(agent, task)
    }));
    
    // Select best agent
    const optimal = scores.reduce((best, current) => 
      current.score > best.score ? current : best
    );
    
    return optimal.agent;
  }
  
  calculateAgentScore(agent, task) {
    const metrics = this.agentMetrics.get(agent.id);
    const capability = agent.getCapabilityScore(task);
    const load = 1 - (metrics.currentLoad / metrics.maxCapacity);
    const performance = metrics.averageTaskTime / task.estimatedTime;
    
    return capability * 0.4 + load * 0.3 + performance * 0.3;
  }
}

Token Usage Optimization

Advanced Context Compression

class AdvancedContextCompressor {
  constructor() {
    this.semanticAnalyzer = new SemanticAnalyzer();
    this.importanceRanker = new ImportanceRanker();
    this.compressionAlgorithms = {
      'lz4': new LZ4Compressor(),
      'semantic': new SemanticCompressor(),
      'template': new TemplateCompressor()
    };
  }
  
  async compressContext(context, targetReduction = 0.4) {
    // Analyze context structure
    const analysis = await this.semanticAnalyzer.analyze(context);
    
    // Rank information by importance
    const importance = await this.importanceRanker.rank(analysis);
    
    // Select optimal compression strategy
    const strategy = this.selectCompressionStrategy(context, targetReduction);
    
    // Apply compression
    const compressed = await strategy.compress(context, importance);
    
    // Verify quality preservation
    const quality = await this.verifyQuality(context, compressed);
    
    if (quality < 0.85) {
      // Try less aggressive compression
      return await this.compressContext(context, targetReduction * 0.8);
    }
    
    return compressed;
  }
}

Smart Template System

{
  "template_optimization": {
    "template_types": {
      "code_patterns": "Common code structures and patterns",
      "component_templates": "UI component boilerplates",
      "api_structures": "API endpoint templates",
      "configuration_files": "Common config file patterns"
    },
    "optimization_techniques": {
      "parameterization": "Make templates highly configurable",
      "composition": "Combine multiple templates",
      "inheritance": "Template hierarchies for reuse",
      "caching": "Cache compiled templates"
    },
    "learning": {
      "usage_tracking": "Track which templates are most effective",
      "auto_generation": "Generate new templates from patterns",
      "optimization": "Continuously improve template efficiency"
    }
  }
}

System Resource Optimization

Memory Management

class MemoryOptimizer {
  constructor() {
    this.memoryPools = new Map();
    this.gcScheduler = new GCScheduler();
    this.memoryMonitor = new MemoryMonitor();
  }
  
  optimizeMemoryUsage() {
    // Monitor memory usage patterns
    const usage = this.memoryMonitor.getUsagePatterns();
    
    // Optimize garbage collection
    this.gcScheduler.optimizeGCTiming(usage);
    
    // Manage object pools
    this.optimizeObjectPools(usage);
    
    // Clean up unused resources
    this.cleanupUnusedResources();
  }
  
  createMemoryPool(objectType, initialSize = 10) {
    const pool = {
      available: [],
      inUse: new Set(),
      factory: () => new objectType()
    };
    
    // Pre-populate pool
    for (let i = 0; i < initialSize; i++) {
      pool.available.push(pool.factory());
    }
    
    this.memoryPools.set(objectType.name, pool);
    return pool;
  }
}

CPU Optimization

{
  "cpu_optimization": {
    "parallel_processing": {
      "worker_threads": "Use Node.js worker threads for CPU-intensive tasks",
      "cluster_mode": "Run multiple processes for better CPU utilization",
      "async_operations": "Maximize async I/O operations"
    },
    "algorithm_optimization": {
      "caching": "Cache expensive computations",
      "memoization": "Memoize function results",
      "lazy_loading": "Load resources only when needed",
      "batching": "Batch similar operations"
    },
    "scheduling": {
      "priority_queues": "Process high-priority tasks first",
      "round_robin": "Fair scheduling across agents",
      "preemptive": "Allow task interruption for urgent requests"
    }
  }
}

I/O Optimization

class IOOptimizer {
  constructor() {
    this.connectionPools = new Map();
    this.requestBatcher = new RequestBatcher();
    this.cache = new DistributedCache();
  }
  
  optimizeFileOperations() {
    // Batch file operations
    this.requestBatcher.setBatchSize(50);
    this.requestBatcher.setFlushInterval(100);
    
    // Use streaming for large files
    this.enableStreaming(['read', 'write']);
    
    // Optimize file system caching
    this.optimizeFileSystemCache();
  }
  
  optimizeNetworkOperations() {
    // Connection pooling
    this.createConnectionPool('anthropic-api', {
      maxConnections: 20,
      keepAlive: true,
      timeout: 30000
    });
    
    // Request batching
    this.enableRequestBatching(['api-calls', 'database-queries']);
    
    // Response caching
    this.cache.configure({
      ttl: 3600,
      maxSize: '500MB',
      strategy: 'lru'
    });
  }
}

Database and Storage Optimization

Query Optimization

{
  "database_optimization": {
    "indexing": {
      "strategy": "Index frequently queried columns",
      "composite_indexes": "Multi-column indexes for complex queries",
      "partial_indexes": "Conditional indexes for filtered queries"
    },
    "query_patterns": {
      "batching": "Batch multiple queries into single operations",
      "pagination": "Use cursor-based pagination for large datasets",
      "prepared_statements": "Cache query plans for repeated queries"
    },
    "connection_management": {
      "pooling": "Connection pools for database connections",
      "lazy_connections": "Connect only when needed",
      "connection_reuse": "Reuse connections across requests"
    }
  }
}

Caching Strategy

class MultiLayerCache {
  constructor() {\n    this.layers = {\n      memory: new MemoryCache({ maxSize: '100MB', ttl: 300 }),\n      redis: new RedisCache({ ttl: 3600 }),\n      disk: new DiskCache({ maxSize: '1GB', ttl: 86400 })\n    };\n  }\n  \n  async get(key) {\n    // Try memory cache first\n    let value = await this.layers.memory.get(key);\n    if (value) return value;\n    \n    // Try Redis cache\n    value = await this.layers.redis.get(key);\n    if (value) {\n      // Populate memory cache\n      await this.layers.memory.set(key, value);\n      return value;\n    }\n    \n    // Try disk cache\n    value = await this.layers.disk.get(key);\n    if (value) {\n      // Populate higher-level caches\n      await this.layers.redis.set(key, value);\n      await this.layers.memory.set(key, value);\n      return value;\n    }\n    \n    return null;\n  }\n}

Network Optimization

API Performance

{
  "api_optimization": {
    "request_optimization": {
      "compression": "Enable gzip/brotli compression",
      "keep_alive": "Use HTTP keep-alive connections",
      "multiplexing": "HTTP/2 multiplexing where available",
      "batching": "Batch multiple API calls"
    },
    "response_optimization": {
      "streaming": "Stream responses for large data",
      "partial_responses": "Return only requested fields",
      "caching": "Cache responses with appropriate headers",
      "pagination": "Paginate large result sets"
    },
    "error_handling": {
      "retry_logic": "Exponential backoff with jitter",
      "circuit_breakers": "Prevent cascading failures",
      "timeouts": "Appropriate timeout values",
      "fallbacks": "Graceful degradation"
    }
  }
}

Configuration Tuning

Environment-Specific Settings

{
  "development": {
    "agents": { "max_concurrent": 2 },
    "tokens": { "budget": 50000 },
    "caching": { "enabled": false },
    "logging": { "level": "debug" }
  },
  "staging": {
    "agents": { "max_concurrent": 3 },
    "tokens": { "budget": 100000 },
    "caching": { "enabled": true, "ttl": 1800 },
    "logging": { "level": "info" }
  },
  "production": {
    "agents": { "max_concurrent": 8 },
    "tokens": { "budget": 500000 },
    "caching": { "enabled": true, "ttl": 3600 },
    "logging": { "level": "warn" },
    "optimization": { "aggressive": true }
  }
}

Performance Profiles

const performanceProfiles = {
  'speed-optimized': {
    agents: { maxConcurrent: 8 },
    tokens: { optimization: 'moderate' },
    caching: { aggressive: true },
    parallelism: 'maximum'
  },
  'cost-optimized': {
    agents: { maxConcurrent: 3 },
    tokens: { optimization: 'aggressive' },
    caching: { selective: true },
    parallelism: 'moderate'
  },
  'balanced': {
    agents: { maxConcurrent: 5 },
    tokens: { optimization: 'adaptive' },
    caching: { smart: true },
    parallelism: 'adaptive'
  }
};

Monitoring and Profiling

Real-time Performance Dashboard

class PerformanceDashboard {
  constructor() {
    this.metrics = new MetricsCollector();
    this.alerts = new AlertSystem();
    this.visualizations = new ChartGenerator();
  }
  
  generateDashboard() {
    return {
      overview: this.getOverviewMetrics(),
      agents: this.getAgentMetrics(),
      tasks: this.getTaskMetrics(),
      resources: this.getResourceMetrics(),
      optimization: this.getOptimizationMetrics()
    };
  }
  
  getOptimizationRecommendations() {
    const metrics = this.metrics.getCurrentMetrics();
    const recommendations = [];
    
    if (metrics.tokenEfficiency < 0.8) {
      recommendations.push({
        type: 'token_optimization',
        priority: 'high',
        action: 'Enable aggressive token compression',
        impact: 'Reduce costs by 15-25%'
      });
    }
    
    if (metrics.agentUtilization < 0.7) {
      recommendations.push({
        type: 'scaling',
        priority: 'medium', 
        action: 'Reduce agent pool size',
        impact: 'Improve resource utilization'
      });
    }
    
    return recommendations;
  }
}

Troubleshooting Performance Issues

Common Performance Problems

{
  "performance_issues": {
    "slow_task_completion": {
      "symptoms": ["Tasks taking > 10 minutes", "User complaints about speed"],
      "causes": ["Token budget exhaustion", "Agent overload", "Context bloat"],
      "solutions": ["Increase token budget", "Scale agent pool", "Enable compression"]
    },
    "high_resource_usage": {
      "symptoms": ["High CPU/memory usage", "System slowdown"],
      "causes": ["Memory leaks", "Inefficient algorithms", "Too many concurrent tasks"],
      "solutions": ["Profile memory usage", "Optimize algorithms", "Limit concurrency"]
    },
    "api_timeouts": {
      "symptoms": ["Frequent timeout errors", "Failed requests"],
      "causes": ["Network issues", "API rate limits", "Large request payloads"],
      "solutions": ["Retry logic", "Request batching", "Payload optimization"]
    }
  }
}

Diagnostic Tools

# Performance profiling commands
npm run profile:cpu          # CPU profiling
npm run profile:memory       # Memory profiling
npm run benchmark           # Performance benchmarks
npm run analyze:tokens      # Token usage analysis
npm run monitor:agents      # Agent performance monitoring

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For more information, see Configuration, Token Optimization, or Monitoring.