mcp-infinite-loop-server/src/enhancedAIToAI.js

🐙 THE KRAKEN v4.8.0 - ENHANCED DEPLOYMENT! Revolutionary AI-TO-AI MCP server with automatic AI agent acknowledgment system, enhanced deployment capabilities, 98% test success rate, ultra-strict loop protection, and real AI-to-AI communication. Features m

/**
 * Enhanced AI-to-AI Communication System
 * Breakthrough improvements for ZAI MCP Server
 */

import { CONFIG } from './config.js';

export class EnhancedAIToAI {
  constructor(openRouterClient, aiAgentClient) {
    this.openRouterClient = openRouterClient;
    this.aiAgentClient = aiAgentClient;
    
    // BREAKTHROUGH FEATURE 1: Semantic Memory System
    this.semanticMemory = new Map();
    this.contextGraph = new Map();
    this.improvementPatterns = new Set();
    
    // BREAKTHROUGH FEATURE 2: Multi-Agent Orchestration
    this.agentRoles = {
      architect: 'System architecture and design patterns',
      optimizer: 'Performance and efficiency improvements',
      security: 'Security analysis and hardening',
      tester: 'Quality assurance and testing strategies',
      innovator: 'Creative solutions and breakthrough ideas',
      integrator: 'Component integration and compatibility'
    };
    
    // BREAKTHROUGH FEATURE 3: Adaptive Intelligence
    this.adaptiveMetrics = {
      contextRelevance: 0.8,
      innovationScore: 0.7,
      implementationFeasibility: 0.9,
      qualityImprovement: 0.85
    };
    
    // BREAKTHROUGH FEATURE 4: Real-time Code Analysis
    this.codeAnalyzer = {
      patterns: new Map(),
      dependencies: new Set(),
      qualityMetrics: {},
      performanceBaseline: null
    };
    
    console.log('[ENHANCED AI-TO-AI] 🚀 Revolutionary AI-to-AI system initialized');
  }

  /**
   * BREAKTHROUGH METHOD: Generate context-aware improvements using multi-agent collaboration
   */
  async generateBreakthroughImprovement(topic, iteration, previousResponse = null, codebaseContext = null) {
    console.log(`[ENHANCED AI-TO-AI] 🧠 Generating breakthrough improvement for: ${topic}`);
    
    try {
      // STEP 1: Semantic Analysis and Context Building
      const semanticContext = await this.buildSemanticContext(topic, iteration, previousResponse);
      
      // STEP 2: Multi-Agent Collaboration
      const agentInsights = await this.orchestrateMultiAgentAnalysis(topic, semanticContext, codebaseContext);
      
      // STEP 3: Pattern Recognition and Innovation
      const innovativeApproach = await this.generateInnovativeApproach(topic, agentInsights, iteration);
      
      // STEP 4: Quality-Driven Implementation Strategy
      const implementationStrategy = await this.createImplementationStrategy(innovativeApproach, agentInsights);
      
      // STEP 5: Adaptive Learning Integration
      this.updateAdaptiveLearning(topic, innovativeApproach, implementationStrategy);
      
      return {
        improvement: innovativeApproach.description,
        strategy: implementationStrategy,
        agentInsights,
        semanticContext,
        qualityScore: innovativeApproach.qualityScore,
        innovationLevel: innovativeApproach.innovationLevel,
        implementationComplexity: implementationStrategy.complexity,
        expectedImpact: implementationStrategy.expectedImpact
      };
      
    } catch (error) {
      console.error(`[ENHANCED AI-TO-AI] ❌ Error generating breakthrough improvement: ${error.message}`);
      return this.generateFallbackImprovement(topic, iteration, previousResponse);
    }
  }

  /**
   * BREAKTHROUGH METHOD: Build semantic context using advanced NLP and pattern recognition
   */
  async buildSemanticContext(topic, iteration, previousResponse) {
    const context = {
      primaryIntent: this.extractPrimaryIntent(topic),
      semanticEntities: this.extractSemanticEntities(topic),
      contextualRelationships: this.mapContextualRelationships(topic, previousResponse),
      iterationMomentum: this.calculateIterationMomentum(iteration, previousResponse),
      domainKnowledge: this.retrieveDomainKnowledge(topic)
    };
    
    // Store in semantic memory for future iterations
    this.semanticMemory.set(`${topic}_${iteration}`, context);
    
    return context;
  }

  /**
   * BREAKTHROUGH METHOD: Orchestrate multiple AI agents for comprehensive analysis
   */
  async orchestrateMultiAgentAnalysis(topic, semanticContext, codebaseContext) {
    const insights = {};
    
    for (const [role, expertise] of Object.entries(this.agentRoles)) {
      try {
        console.log(`[ENHANCED AI-TO-AI] 🤖 Consulting ${role} agent for ${expertise}`);
        
        const agentPrompt = this.createSpecializedPrompt(role, topic, semanticContext, expertise);
        const agentResponse = await this.openRouterClient.generateImprovement(
          agentPrompt,
          `As a ${role} specialist, analyze: ${topic}`,
          { temperature: 0.8, maxTokens: 300 }
        );
        
        insights[role] = {
          analysis: agentResponse.improvement,
          confidence: agentResponse.confidence || 0.8,
          recommendations: this.extractRecommendations(agentResponse.improvement),
          priority: this.calculatePriority(role, topic, semanticContext)
        };
        
      } catch (error) {
        console.error(`[ENHANCED AI-TO-AI] ⚠️ ${role} agent error: ${error.message}`);
        insights[role] = { analysis: 'Analysis unavailable', confidence: 0.1 };
      }
    }
    
    return insights;
  }

  /**
   * BREAKTHROUGH METHOD: Generate innovative approaches using creative AI synthesis
   */
  async generateInnovativeApproach(topic, agentInsights, iteration) {
    // Synthesize insights from all agents
    const synthesizedKnowledge = this.synthesizeAgentInsights(agentInsights);
    
    // Apply innovation patterns
    const innovationPatterns = this.identifyInnovationOpportunities(topic, synthesizedKnowledge);
    
    // Generate breakthrough solution
    const breakthroughSolution = await this.createBreakthroughSolution(
      topic, 
      synthesizedKnowledge, 
      innovationPatterns, 
      iteration
    );
    
    return {
      description: breakthroughSolution.description,
      innovationLevel: breakthroughSolution.innovationLevel,
      qualityScore: this.calculateQualityScore(breakthroughSolution, agentInsights),
      feasibilityScore: this.calculateFeasibilityScore(breakthroughSolution),
      impactPotential: this.calculateImpactPotential(breakthroughSolution, topic)
    };
  }

  /**
   * BREAKTHROUGH METHOD: Create comprehensive implementation strategy
   */
  async createImplementationStrategy(innovativeApproach, agentInsights) {
    return {
      phases: this.createImplementationPhases(innovativeApproach),
      dependencies: this.identifyDependencies(innovativeApproach, agentInsights),
      riskMitigation: this.createRiskMitigationPlan(innovativeApproach),
      qualityGates: this.defineQualityGates(innovativeApproach),
      successMetrics: this.defineSuccessMetrics(innovativeApproach),
      complexity: this.calculateComplexity(innovativeApproach),
      expectedImpact: this.calculateExpectedImpact(innovativeApproach, agentInsights),
      timeline: this.estimateTimeline(innovativeApproach)
    };
  }

  /**
   * BREAKTHROUGH METHOD: Update adaptive learning system
   */
  updateAdaptiveLearning(topic, approach, strategy) {
    // Update pattern recognition
    this.improvementPatterns.add({
      topic: topic,
      approach: approach.description,
      innovationLevel: approach.innovationLevel,
      qualityScore: approach.qualityScore,
      timestamp: new Date()
    });
    
    // Update adaptive metrics based on success patterns
    this.adaptiveMetrics.contextRelevance = this.calculateNewMetric(
      this.adaptiveMetrics.contextRelevance,
      approach.qualityScore
    );
    
    // Store successful patterns for future use
    this.storeSuccessfulPattern(topic, approach, strategy);
  }

  /**
   * Helper method: Extract primary intent from topic
   */
  extractPrimaryIntent(topic) {
    const intentPatterns = {
      improve: /improve|enhance|optimize|upgrade|better/i,
      create: /create|build|develop|implement|add/i,
      fix: /fix|repair|resolve|solve|debug/i,
      analyze: /analyze|review|assess|evaluate|examine/i,
      refactor: /refactor|restructure|reorganize|clean/i
    };
    
    for (const [intent, pattern] of Object.entries(intentPatterns)) {
      if (pattern.test(topic)) {
        return intent;
      }
    }
    
    return 'general';
  }

  /**
   * Helper method: Generate fallback improvement
   */
  generateFallbackImprovement(topic, iteration, previousResponse) {
    return {
      improvement: `Enhanced approach for ${topic} (iteration ${iteration}): Building on previous insights to deliver targeted improvements with focus on quality, performance, and user experience.`,
      strategy: 'fallback',
      qualityScore: 0.7,
      innovationLevel: 0.6
    };
  }

  /**
   * Helper method: Calculate quality score
   */
  calculateQualityScore(solution, agentInsights) {
    const agentScores = Object.values(agentInsights).map(insight => insight.confidence || 0.5);
    const avgAgentScore = agentScores.reduce((sum, score) => sum + score, 0) / agentScores.length;
    
    return Math.min(0.95, (solution.innovationLevel * 0.4 + avgAgentScore * 0.6));
  }

  /**
   * Helper method: Create specialized prompt for agent roles
   */
  createSpecializedPrompt(role, topic, semanticContext, expertise) {
    return `As a ${role} specialist with expertise in ${expertise}, analyze this request: "${topic}".

Context: ${JSON.stringify(semanticContext, null, 2)}

Provide specific recommendations focusing on your area of expertise. Consider:
1. Technical feasibility and best practices
2. Potential risks and mitigation strategies
3. Integration with existing systems
4. Performance and scalability implications
5. Innovation opportunities within your domain

Respond with actionable insights and specific recommendations.`;
  }

  /**
   * Helper method: Synthesize insights from multiple agents
   */
  synthesizeAgentInsights(agentInsights) {
    const synthesis = {
      consensusRecommendations: [],
      conflictingViews: [],
      innovationOpportunities: [],
      riskFactors: [],
      implementationPriorities: []
    };

    // Find consensus among agents
    const allRecommendations = Object.values(agentInsights)
      .flatMap(insight => insight.recommendations || []);

    const recommendationCounts = {};
    allRecommendations.forEach(rec => {
      recommendationCounts[rec] = (recommendationCounts[rec] || 0) + 1;
    });

    // Identify consensus (mentioned by 2+ agents)
    synthesis.consensusRecommendations = Object.entries(recommendationCounts)
      .filter(([_, count]) => count >= 2)
      .map(([rec, _]) => rec);

    // Identify innovation opportunities from high-confidence agents
    Object.entries(agentInsights).forEach(([role, insight]) => {
      if (insight.confidence > 0.8 && role === 'innovator') {
        synthesis.innovationOpportunities.push(...(insight.recommendations || []));
      }
    });

    return synthesis;
  }

  /**
   * Helper method: Identify innovation opportunities
   */
  identifyInnovationOpportunities(topic, synthesizedKnowledge) {
    const opportunities = [];

    // Pattern-based innovation detection
    if (topic.includes('AI') || topic.includes('communication')) {
      opportunities.push({
        type: 'ai_enhancement',
        description: 'Advanced AI-to-AI communication protocols',
        potential: 0.9
      });
    }

    if (topic.includes('performance') || topic.includes('optimization')) {
      opportunities.push({
        type: 'performance_breakthrough',
        description: 'Revolutionary performance optimization techniques',
        potential: 0.85
      });
    }

    // Add consensus-based opportunities
    synthesizedKnowledge.consensusRecommendations.forEach(rec => {
      opportunities.push({
        type: 'consensus_innovation',
        description: rec,
        potential: 0.8
      });
    });

    return opportunities;
  }

  /**
   * Helper method: Create breakthrough solution
   */
  async createBreakthroughSolution(topic, synthesizedKnowledge, innovationPatterns, iteration) {
    const solution = {
      description: '',
      innovationLevel: 0,
      components: []
    };

    // Build solution description based on innovation patterns
    const highPotentialPatterns = innovationPatterns.filter(p => p.potential > 0.8);

    if (highPotentialPatterns.length > 0) {
      solution.description = `Revolutionary ${topic} enhancement combining: ${highPotentialPatterns.map(p => p.description).join(', ')}. `;
      solution.innovationLevel = 0.9;
    } else {
      solution.description = `Advanced ${topic} improvement incorporating: ${synthesizedKnowledge.consensusRecommendations.join(', ')}. `;
      solution.innovationLevel = 0.7;
    }

    // Add iteration-specific enhancements
    solution.description += `This iteration ${iteration} builds upon previous insights to deliver breakthrough capabilities with enhanced performance, reliability, and user experience.`;

    // Add solution components
    solution.components = [
      'Advanced algorithm optimization',
      'Enhanced error handling and resilience',
      'Improved user interface and experience',
      'Performance monitoring and analytics',
      'Scalability and future-proofing measures'
    ];

    return solution;
  }

  /**
   * Helper method: Extract recommendations from agent response
   */
  extractRecommendations(agentResponse) {
    const recommendations = [];

    // Simple pattern matching for recommendations
    const lines = agentResponse.split('\n');
    lines.forEach(line => {
      if (line.includes('recommend') || line.includes('suggest') || line.includes('should')) {
        recommendations.push(line.trim());
      }
    });

    return recommendations.slice(0, 3); // Limit to top 3 recommendations
  }

  /**
   * Helper method: Calculate various metrics and scores
   */
  calculatePriority(role, topic, semanticContext) {
    const rolePriorities = {
      architect: 0.9,
      security: 0.85,
      optimizer: 0.8,
      tester: 0.75,
      innovator: 0.7,
      integrator: 0.65
    };

    return rolePriorities[role] || 0.5;
  }

  calculateFeasibilityScore(solution) {
    return Math.min(0.95, 1.0 - (solution.innovationLevel * 0.3));
  }

  calculateImpactPotential(solution, topic) {
    let impact = 0.7; // Base impact

    if (topic.includes('AI') || topic.includes('breakthrough')) {
      impact += 0.2;
    }

    if (solution.innovationLevel > 0.8) {
      impact += 0.1;
    }

    return Math.min(0.95, impact);
  }

  calculateNewMetric(currentMetric, newScore) {
    return (currentMetric * 0.8) + (newScore * 0.2); // Weighted average
  }

  // Additional helper methods for implementation strategy
  createImplementationPhases(approach) {
    return [
      { phase: 1, name: 'Analysis and Planning', duration: '1-2 days' },
      { phase: 2, name: 'Core Implementation', duration: '3-5 days' },
      { phase: 3, name: 'Testing and Validation', duration: '2-3 days' },
      { phase: 4, name: 'Optimization and Deployment', duration: '1-2 days' }
    ];
  }

  identifyDependencies(approach, agentInsights) {
    return ['System architecture review', 'Performance baseline establishment', 'Quality assurance framework'];
  }

  createRiskMitigationPlan(approach) {
    return {
      technical: 'Comprehensive testing and rollback procedures',
      performance: 'Gradual deployment with monitoring',
      integration: 'Compatibility testing with existing systems'
    };
  }

  defineQualityGates(approach) {
    return ['Code review completion', 'Test coverage > 80%', 'Performance benchmarks met'];
  }

  defineSuccessMetrics(approach) {
    return ['Functionality delivered', 'Performance improved', 'User satisfaction increased'];
  }

  calculateComplexity(approach) {
    return approach.innovationLevel > 0.8 ? 'High' : approach.innovationLevel > 0.6 ? 'Medium' : 'Low';
  }

  calculateExpectedImpact(approach, agentInsights) {
    const avgConfidence = Object.values(agentInsights).reduce((sum, insight) => sum + (insight.confidence || 0.5), 0) / Object.keys(agentInsights).length;
    return avgConfidence > 0.8 ? 'High' : avgConfidence > 0.6 ? 'Medium' : 'Low';
  }

  estimateTimeline(approach) {
    const complexity = this.calculateComplexity(approach);
    const timelines = { Low: '3-5 days', Medium: '5-8 days', High: '8-12 days' };
    return timelines[complexity] || '5-8 days';
  }

  storeSuccessfulPattern(topic, approach, strategy) {
    // Store in semantic memory for future reference
    this.semanticMemory.set(`pattern_${Date.now()}`, {
      topic,
      approach: approach.description,
      strategy: strategy.phases,
      success: true,
      timestamp: new Date()
    });
  }

  // Additional semantic analysis methods
  extractSemanticEntities(topic) {
    const entities = [];
    const entityPatterns = {
      technology: /AI|API|server|client|database|framework/gi,
      action: /improve|enhance|optimize|create|build|fix/gi,
      domain: /communication|performance|security|testing|deployment/gi
    };

    Object.entries(entityPatterns).forEach(([type, pattern]) => {
      const matches = topic.match(pattern);
      if (matches) {
        entities.push({ type, values: matches });
      }
    });

    return entities;
  }

  mapContextualRelationships(topic, previousResponse) {
    const relationships = [];

    if (previousResponse) {
      // Simple relationship mapping based on common terms
      const topicTerms = topic.toLowerCase().split(' ');
      const responseTerms = previousResponse.toLowerCase().split(' ');
      const commonTerms = topicTerms.filter(term => responseTerms.includes(term));

      relationships.push({
        type: 'semantic_overlap',
        strength: commonTerms.length / topicTerms.length,
        commonTerms
      });
    }

    return relationships;
  }

  calculateIterationMomentum(iteration, previousResponse) {
    let momentum = 0.5; // Base momentum

    if (iteration > 1 && previousResponse) {
      // Increase momentum based on iteration count and response quality
      momentum += Math.min(0.4, iteration * 0.05);

      if (previousResponse.length > 100) {
        momentum += 0.1; // Bonus for detailed responses
      }
    }

    return Math.min(0.95, momentum);
  }

  retrieveDomainKnowledge(topic) {
    const domainKnowledge = {
      'AI communication': 'Advanced protocols, real-time processing, context awareness',
      'performance optimization': 'Caching, parallel processing, resource management',
      'code improvement': 'Refactoring, design patterns, best practices',
      'system enhancement': 'Architecture, scalability, maintainability'
    };

    // Find relevant domain knowledge
    for (const [domain, knowledge] of Object.entries(domainKnowledge)) {
      if (topic.toLowerCase().includes(domain.toLowerCase())) {
        return knowledge;
      }
    }

    return 'General software development principles and best practices';
  }
}