zai-mcp-server/src/autonomousAITeams.js

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/**
 * Autonomous AI Teams - Revolutionary Self-Organizing AI Collaboration
 * AI agents automatically form teams, solve problems, and optimize performance
 */

export class AutonomousAITeams {
    constructor(multiProviderAI, dataCollector) {
        this.multiProviderAI = multiProviderAI;
        this.dataCollector = dataCollector;
        this.activeTeams = new Map();
        this.agentPool = new Map();
        this.teamPerformanceHistory = new Map();
        this.skillMatrix = new Map();
        
        this.initializeAgentPool();
        console.log('🤖 Autonomous AI Teams initialized with self-organizing capabilities');
    }

    /**
     * Initialize the pool of available AI agents with specialized skills
     */
    initializeAgentPool() {
        const agentTypes = [
            {
                id: 'strategic_planner',
                name: 'Strategic Planner',
                skills: ['planning', 'strategy', 'analysis', 'coordination'],
                personality: 'analytical',
                expertise: 0.9,
                specialties: ['project_planning', 'resource_allocation', 'risk_assessment']
            },
            {
                id: 'creative_innovator',
                name: 'Creative Innovator',
                skills: ['creativity', 'innovation', 'brainstorming', 'design'],
                personality: 'creative',
                expertise: 0.85,
                specialties: ['idea_generation', 'problem_solving', 'design_thinking']
            },
            {
                id: 'technical_architect',
                name: 'Technical Architect',
                skills: ['architecture', 'technical_design', 'implementation', 'optimization'],
                personality: 'systematic',
                expertise: 0.95,
                specialties: ['system_design', 'code_architecture', 'performance_optimization']
            },
            {
                id: 'quality_assurance',
                name: 'Quality Assurance',
                skills: ['testing', 'validation', 'quality_control', 'review'],
                personality: 'meticulous',
                expertise: 0.88,
                specialties: ['testing_strategies', 'quality_metrics', 'validation_protocols']
            },
            {
                id: 'data_analyst',
                name: 'Data Analyst',
                skills: ['analysis', 'data_processing', 'insights', 'reporting'],
                personality: 'analytical',
                expertise: 0.92,
                specialties: ['data_analysis', 'pattern_recognition', 'statistical_analysis']
            },
            {
                id: 'integration_specialist',
                name: 'Integration Specialist',
                skills: ['integration', 'apis', 'connectivity', 'automation'],
                personality: 'systematic',
                expertise: 0.87,
                specialties: ['api_integration', 'workflow_automation', 'system_connectivity']
            },
            {
                id: 'performance_optimizer',
                name: 'Performance Optimizer',
                skills: ['optimization', 'performance', 'efficiency', 'monitoring'],
                personality: 'focused',
                expertise: 0.91,
                specialties: ['performance_tuning', 'resource_optimization', 'monitoring_systems']
            },
            {
                id: 'communication_coordinator',
                name: 'Communication Coordinator',
                skills: ['communication', 'coordination', 'collaboration', 'facilitation'],
                personality: 'collaborative',
                expertise: 0.86,
                specialties: ['team_coordination', 'stakeholder_communication', 'conflict_resolution']
            }
        ];

        for (const agentType of agentTypes) {
            this.agentPool.set(agentType.id, {
                ...agentType,
                available: true,
                currentTeam: null,
                performanceHistory: [],
                collaborationScore: 0.8,
                learningRate: 0.1
            });
            
            // Build skill matrix for intelligent matching
            for (const skill of agentType.skills) {
                if (!this.skillMatrix.has(skill)) {
                    this.skillMatrix.set(skill, []);
                }
                this.skillMatrix.get(skill).push(agentType.id);
            }
        }
    }

    /**
     * Analyze problem and automatically form optimal AI team
     */
    async formTeam(problem, requirements = {}) {
        console.log(`🎯 Forming autonomous AI team for: "${problem.substring(0, 100)}..."`);
        
        const teamId = `team_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`;
        
        // Analyze problem complexity and requirements
        const analysis = await this.analyzeProblem(problem, requirements);
        
        // Select optimal team composition
        const selectedAgents = await this.selectOptimalAgents(analysis);
        
        // Create team with defined roles and communication protocols
        const team = {
            id: teamId,
            problem: problem,
            requirements: requirements,
            analysis: analysis,
            agents: selectedAgents,
            roles: this.assignRoles(selectedAgents, analysis),
            communicationProtocol: this.establishCommunicationProtocol(selectedAgents),
            status: 'formed',
            createdAt: Date.now(),
            performance: {
                efficiency: 0,
                quality: 0,
                collaboration: 0,
                innovation: 0
            }
        };
        
        // Mark agents as assigned
        for (const agent of selectedAgents) {
            const agentData = this.agentPool.get(agent.id);
            agentData.available = false;
            agentData.currentTeam = teamId;
        }
        
        this.activeTeams.set(teamId, team);
        
        console.log(`🤖 Team ${teamId} formed with ${selectedAgents.length} agents: ${selectedAgents.map(a => a.name).join(', ')}`);
        
        return team;
    }

    /**
     * Analyze problem to understand requirements and complexity
     */
    async analyzeProblem(problem, requirements) {
        const prompt = `Analyze this problem and provide structured analysis:

Problem: ${problem}

Requirements: ${JSON.stringify(requirements, null, 2)}

Provide analysis in this format:
{
  "complexity": "low|medium|high|critical",
  "domain": "technical|creative|analytical|strategic|operational",
  "requiredSkills": ["skill1", "skill2", ...],
  "estimatedDuration": "short|medium|long|extended",
  "riskLevel": "low|medium|high",
  "collaborationNeeds": "minimal|moderate|high|intensive",
  "innovationRequired": "low|medium|high",
  "technicalDepth": "surface|moderate|deep|expert"
}`;

        try {
            const response = await this.multiProviderAI.makeRequest(prompt, {
                maxTokens: 1000,
                temperature: 0.3,
                taskType: 'analysis'
            });

            const analysis = JSON.parse(response.content);
            
            // Add computed metrics
            analysis.complexityScore = this.calculateComplexityScore(analysis);
            analysis.teamSizeRecommendation = this.recommendTeamSize(analysis);
            analysis.prioritySkills = this.identifyPrioritySkills(analysis);
            
            return analysis;
        } catch (error) {
            console.error('Problem analysis failed, using fallback:', error);
            return this.getFallbackAnalysis(problem);
        }
    }

    /**
     * Select optimal agents based on problem analysis
     */
    async selectOptimalAgents(analysis) {
        const selectedAgents = [];
        const requiredSkills = analysis.requiredSkills || [];
        const teamSize = analysis.teamSizeRecommendation || 3;
        
        // Always include a coordinator for team management
        const coordinator = this.findBestAgent(['coordination', 'communication'], selectedAgents);
        if (coordinator) {
            selectedAgents.push(coordinator);
        }
        
        // Select agents based on required skills
        for (const skill of analysis.prioritySkills || requiredSkills.slice(0, teamSize - 1)) {
            const agent = this.findBestAgent([skill], selectedAgents);
            if (agent && selectedAgents.length < teamSize) {
                selectedAgents.push(agent);
            }
        }
        
        // Fill remaining slots with complementary agents
        while (selectedAgents.length < teamSize) {
            const complementaryAgent = this.findComplementaryAgent(selectedAgents, analysis);
            if (complementaryAgent) {
                selectedAgents.push(complementaryAgent);
            } else {
                break;
            }
        }
        
        return selectedAgents;
    }

    /**
     * Find best available agent for specific skills
     */
    findBestAgent(skills, excludeAgents = []) {
        const excludeIds = excludeAgents.map(a => a.id);
        let bestAgent = null;
        let bestScore = 0;
        
        for (const [agentId, agent] of this.agentPool) {
            if (!agent.available || excludeIds.includes(agentId)) continue;
            
            const skillScore = this.calculateSkillMatch(agent, skills);
            const performanceScore = this.calculatePerformanceScore(agent);
            const collaborationScore = agent.collaborationScore;
            
            const totalScore = (skillScore * 0.5) + (performanceScore * 0.3) + (collaborationScore * 0.2);
            
            if (totalScore > bestScore) {
                bestScore = totalScore;
                bestAgent = { ...agent };
            }
        }
        
        return bestAgent;
    }

    /**
     * Execute problem solving autonomously with the team
     */
    async executeAutonomously(teamId, options = {}) {
        const team = this.activeTeams.get(teamId);
        if (!team) {
            throw new Error(`Team ${teamId} not found`);
        }
        
        console.log(`🚀 Team ${teamId} starting autonomous execution`);
        
        team.status = 'executing';
        team.startTime = Date.now();
        
        try {
            // Phase 1: Team coordination and planning
            const plan = await this.coordinateTeamPlanning(team);
            
            // Phase 2: Parallel task execution with coordination
            const results = await this.executeParallelTasks(team, plan);
            
            // Phase 3: Integration and quality assurance
            const finalResult = await this.integrateAndValidate(team, results);
            
            // Phase 4: Performance evaluation and learning
            await this.evaluateTeamPerformance(team, finalResult);
            
            team.status = 'completed';
            team.endTime = Date.now();
            team.result = finalResult;
            
            console.log(`✅ Team ${teamId} completed autonomous execution in ${team.endTime - team.startTime}ms`);
            
            return finalResult;
            
        } catch (error) {
            console.error(`❌ Team ${teamId} execution failed:`, error);
            team.status = 'failed';
            team.error = error.message;
            throw error;
        } finally {
            // Release agents back to pool
            this.releaseTeam(teamId);
        }
    }

    /**
     * Coordinate team planning phase
     */
    async coordinateTeamPlanning(team) {
        const coordinator = team.agents.find(a => a.skills.includes('coordination')) || team.agents[0];
        
        const planningPrompt = `As the team coordinator, create an execution plan for this problem:

Problem: ${team.problem}
Team: ${team.agents.map(a => `${a.name} (${a.skills.join(', ')})`).join(', ')}
Analysis: ${JSON.stringify(team.analysis, null, 2)}

Create a detailed execution plan with:
1. Task breakdown and assignment
2. Coordination points and dependencies
3. Quality checkpoints
4. Risk mitigation strategies
5. Success criteria

Format as JSON with clear structure.`;

        const response = await this.multiProviderAI.makeRequest(planningPrompt, {
            maxTokens: 2000,
            temperature: 0.4,
            taskType: 'planning'
        });
        
        try {
            return JSON.parse(response.content);
        } catch (error) {
            return this.createFallbackPlan(team);
        }
    }

    /**
     * Execute tasks in parallel with intelligent coordination
     */
    async executeParallelTasks(team, plan) {
        const taskPromises = [];
        const results = new Map();
        
        for (const task of plan.tasks || []) {
            const assignedAgent = team.agents.find(a => a.id === task.assignedAgent) || team.agents[0];
            
            const taskPromise = this.executeAgentTask(assignedAgent, task, team.problem)
                .then(result => {
                    results.set(task.id, result);
                    return result;
                })
                .catch(error => {
                    console.error(`Task ${task.id} failed:`, error);
                    results.set(task.id, { error: error.message });
                    return { error: error.message };
                });
            
            taskPromises.push(taskPromise);
        }
        
        await Promise.all(taskPromises);
        return results;
    }

    /**
     * Execute individual agent task
     */
    async executeAgentTask(agent, task, originalProblem) {
        const taskPrompt = `As ${agent.name} with expertise in ${agent.skills.join(', ')}, execute this task:

Original Problem: ${originalProblem}
Your Task: ${task.description}
Your Role: ${task.role || 'contributor'}
Success Criteria: ${task.successCriteria || 'Complete the task effectively'}

Provide your solution, analysis, or contribution based on your expertise.`;

        const response = await this.multiProviderAI.makeRequest(taskPrompt, {
            maxTokens: 1500,
            temperature: 0.6,
            taskType: agent.skills[0] || 'general'
        });
        
        return {
            agent: agent.name,
            task: task.id,
            result: response.content,
            timestamp: Date.now(),
            quality: this.estimateResultQuality(response.content)
        };
    }

    /**
     * Utility methods for team management
     */
    calculateComplexityScore(analysis) {
        const complexityMap = { low: 0.2, medium: 0.5, high: 0.8, critical: 1.0 };
        return complexityMap[analysis.complexity] || 0.5;
    }

    recommendTeamSize(analysis) {
        const complexity = analysis.complexityScore || 0.5;
        const collaboration = analysis.collaborationNeeds || 'moderate';
        
        if (complexity > 0.8 || collaboration === 'intensive') return 5;
        if (complexity > 0.6 || collaboration === 'high') return 4;
        if (complexity > 0.3 || collaboration === 'moderate') return 3;
        return 2;
    }

    identifyPrioritySkills(analysis) {
        const skills = analysis.requiredSkills || [];
        const domain = analysis.domain || 'general';
        
        // Add domain-specific priority skills
        const domainSkills = {
            technical: ['architecture', 'implementation', 'testing'],
            creative: ['creativity', 'innovation', 'design'],
            analytical: ['analysis', 'data_processing', 'insights'],
            strategic: ['planning', 'strategy', 'coordination'],
            operational: ['optimization', 'monitoring', 'automation']
        };
        
        return [...skills, ...(domainSkills[domain] || [])].slice(0, 5);
    }

    calculateSkillMatch(agent, requiredSkills) {
        const matches = agent.skills.filter(skill => requiredSkills.includes(skill));
        return matches.length / Math.max(requiredSkills.length, 1);
    }

    calculatePerformanceScore(agent) {
        if (agent.performanceHistory.length === 0) return 0.7; // Default for new agents
        
        const recent = agent.performanceHistory.slice(-5);
        return recent.reduce((sum, score) => sum + score, 0) / recent.length;
    }

    getFallbackAnalysis(problem) {
        return {
            complexity: 'medium',
            domain: 'general',
            requiredSkills: ['analysis', 'planning', 'implementation'],
            estimatedDuration: 'medium',
            riskLevel: 'medium',
            collaborationNeeds: 'moderate',
            innovationRequired: 'medium',
            technicalDepth: 'moderate',
            complexityScore: 0.5,
            teamSizeRecommendation: 3,
            prioritySkills: ['analysis', 'planning', 'implementation']
        };
    }

    createFallbackPlan(team) {
        return {
            tasks: team.agents.map((agent, index) => ({
                id: `task_${index}`,
                description: `Contribute expertise in ${agent.skills[0]} to solve the problem`,
                assignedAgent: agent.id,
                role: 'contributor',
                successCriteria: 'Provide valuable insights and solutions'
            }))
        };
    }

    estimateResultQuality(content) {
        if (!content) return 0.1;
        
        let score = 0.5;
        
        // Length factor
        if (content.length > 100) score += 0.1;
        if (content.length > 500) score += 0.1;
        
        // Structure factor
        if (content.includes('\n') || content.includes('•') || content.includes('-')) score += 0.1;
        
        // Content quality indicators
        if (content.toLowerCase().includes('solution') || content.toLowerCase().includes('recommend')) score += 0.1;
        if (content.toLowerCase().includes('analysis') || content.toLowerCase().includes('approach')) score += 0.1;
        
        return Math.min(1.0, score);
    }

    async integrateAndValidate(team, results) {
        // Simple integration - in production this would be more sophisticated
        const allResults = Array.from(results.values()).filter(r => !r.error);
        
        return {
            teamId: team.id,
            problem: team.problem,
            solution: allResults.map(r => r.result).join('\n\n'),
            contributions: allResults,
            quality: allResults.reduce((sum, r) => sum + r.quality, 0) / allResults.length,
            timestamp: Date.now()
        };
    }

    async evaluateTeamPerformance(team, result) {
        // Update team performance metrics
        const performance = {
            efficiency: Math.random() * 0.3 + 0.7, // Simulated
            quality: result.quality || 0.7,
            collaboration: Math.random() * 0.2 + 0.8, // Simulated
            innovation: Math.random() * 0.4 + 0.6 // Simulated
        };
        
        team.performance = performance;
        
        // Update individual agent performance
        for (const agent of team.agents) {
            const agentData = this.agentPool.get(agent.id);
            agentData.performanceHistory.push(performance.quality);
            
            // Keep only last 10 performance records
            if (agentData.performanceHistory.length > 10) {
                agentData.performanceHistory = agentData.performanceHistory.slice(-10);
            }
        }
    }

    findComplementaryAgent(selectedAgents, analysis) {
        const selectedSkills = new Set();
        selectedAgents.forEach(agent => {
            agent.skills.forEach(skill => selectedSkills.add(skill));
        });
        
        // Find agent with skills not yet covered
        for (const [agentId, agent] of this.agentPool) {
            if (!agent.available) continue;
            if (selectedAgents.find(a => a.id === agentId)) continue;
            
            const newSkills = agent.skills.filter(skill => !selectedSkills.has(skill));
            if (newSkills.length > 0) {
                return { ...agent };
            }
        }
        
        return null;
    }

    assignRoles(agents, analysis) {
        const roles = {};
        
        // Assign coordinator role
        const coordinator = agents.find(a => a.skills.includes('coordination')) || agents[0];
        roles[coordinator.id] = 'coordinator';
        
        // Assign specialist roles based on skills
        for (const agent of agents) {
            if (agent.id === coordinator.id) continue;
            
            if (agent.skills.includes('analysis')) roles[agent.id] = 'analyst';
            else if (agent.skills.includes('implementation')) roles[agent.id] = 'implementer';
            else if (agent.skills.includes('testing')) roles[agent.id] = 'validator';
            else if (agent.skills.includes('creativity')) roles[agent.id] = 'innovator';
            else roles[agent.id] = 'contributor';
        }
        
        return roles;
    }

    establishCommunicationProtocol(agents) {
        return {
            coordinator: agents.find(a => a.skills.includes('coordination'))?.id || agents[0].id,
            updateFrequency: 'real-time',
            escalationPath: ['coordinator', 'technical_lead', 'project_manager'],
            communicationChannels: ['direct', 'broadcast', 'escalation']
        };
    }

    releaseTeam(teamId) {
        const team = this.activeTeams.get(teamId);
        if (!team) return;
        
        // Mark agents as available
        for (const agent of team.agents) {
            const agentData = this.agentPool.get(agent.id);
            if (agentData) {
                agentData.available = true;
                agentData.currentTeam = null;
            }
        }
        
        // Archive team data
        this.teamPerformanceHistory.set(teamId, {
            ...team,
            archivedAt: Date.now()
        });
        
        console.log(`🔄 Team ${teamId} released, agents returned to pool`);
    }

    /**
     * Get team status and performance metrics
     */
    getTeamStatus(teamId) {
        const team = this.activeTeams.get(teamId);
        if (!team) return null;
        
        return {
            id: team.id,
            status: team.status,
            agents: team.agents.map(a => ({ id: a.id, name: a.name, skills: a.skills })),
            performance: team.performance,
            duration: team.endTime ? team.endTime - team.startTime : Date.now() - team.startTime,
            problem: team.problem.substring(0, 100) + '...'
        };
    }

    /**
     * Get analytics for all teams
     */
    getTeamAnalytics() {
        const activeTeams = Array.from(this.activeTeams.values());
        const archivedTeams = Array.from(this.teamPerformanceHistory.values());
        
        return {
            activeTeams: activeTeams.length,
            totalTeams: activeTeams.length + archivedTeams.length,
            averagePerformance: this.calculateAveragePerformance(archivedTeams),
            agentUtilization: this.calculateAgentUtilization(),
            topPerformingAgents: this.getTopPerformingAgents()
        };
    }

    calculateAveragePerformance(teams) {
        if (teams.length === 0) return { efficiency: 0, quality: 0, collaboration: 0, innovation: 0 };
        
        const totals = teams.reduce((acc, team) => {
            if (team.performance) {
                acc.efficiency += team.performance.efficiency || 0;
                acc.quality += team.performance.quality || 0;
                acc.collaboration += team.performance.collaboration || 0;
                acc.innovation += team.performance.innovation || 0;
            }
            return acc;
        }, { efficiency: 0, quality: 0, collaboration: 0, innovation: 0 });
        
        const count = teams.length;
        return {
            efficiency: totals.efficiency / count,
            quality: totals.quality / count,
            collaboration: totals.collaboration / count,
            innovation: totals.innovation / count
        };
    }

    calculateAgentUtilization() {
        const total = this.agentPool.size;
        const busy = Array.from(this.agentPool.values()).filter(a => !a.available).length;
        return { total, busy, available: total - busy, utilization: busy / total };
    }

    getTopPerformingAgents() {
        return Array.from(this.agentPool.values())
            .map(agent => ({
                id: agent.id,
                name: agent.name,
                averagePerformance: this.calculatePerformanceScore(agent),
                collaborationScore: agent.collaborationScore,
                totalTasks: agent.performanceHistory.length
            }))
            .sort((a, b) => b.averagePerformance - a.averagePerformance)
            .slice(0, 5);
    }
}