JAEGIS Agent Activation Logic System Implementation

System Overview

The Agent Activation Logic System provides intelligent, automatic agent activation based on workflow requirements, project characteristics, and full team participation settings. It ensures optimal agent utilization while maintaining workflow efficiency.

Core Architecture

1. Agent Classification Framework

Primary Agents (Always Activated)

PRIMARY_AGENTS = {
    "John": {
        "title": "Product Manager",
        "always_active": True,
        "activation_trigger": "workflow_start",
        "core_responsibilities": ["business_requirements", "stakeholder_coordination", "value_proposition"],
        "activation_priority": 1
    },
    "Fred": {
        "title": "System Architect", 
        "always_active": True,
        "activation_trigger": "workflow_start",
        "core_responsibilities": ["technical_architecture", "system_design", "scalability_planning"],
        "activation_priority": 1
    },
    "Tyler": {
        "title": "Task Breakdown Specialist",
        "always_active": True,
        "activation_trigger": "workflow_start", 
        "core_responsibilities": ["task_decomposition", "acceptance_criteria", "implementation_planning"],
        "activation_priority": 1
    }
}

Secondary Agents (Conditionally Activated)

SECONDARY_AGENTS = {
    "Jane": {
        "title": "Design Architect",
        "activation_triggers": ["frontend_requirements", "ui_components", "user_experience_focus"],
        "expertise_areas": ["user_experience", "interface_design", "accessibility", "frontend_architecture"],
        "workflow_compatibility": ["documentation_mode", "full_development_mode"],
        "activation_conditions": {
            "project_has_ui": True,
            "user_facing_components": True,
            "design_requirements": True
        }
    },
    "Alex": {
        "title": "Platform Engineer",
        "activation_triggers": ["infrastructure_requirements", "deployment_needs", "security_concerns"],
        "expertise_areas": ["infrastructure", "security", "deployment", "performance_optimization"],
        "workflow_compatibility": ["documentation_mode", "full_development_mode"],
        "activation_conditions": {
            "requires_infrastructure": True,
            "security_critical": True,
            "deployment_complexity": "medium_or_high"
        }
    },
    "James": {
        "title": "Full Stack Developer",
        "activation_triggers": ["implementation_focus", "code_generation", "technical_implementation"],
        "expertise_areas": ["implementation", "code_quality", "technical_debt", "integration"],
        "workflow_compatibility": ["full_development_mode", "implementation_heavy_documentation"],
        "activation_conditions": {
            "implementation_required": True,
            "code_generation_needed": True,
            "technical_complexity": "medium_or_high"
        }
    },
    "Sage": {
        "title": "Validation Specialist",
        "activation_triggers": ["dependency_validation", "security_assessment", "compliance_requirements"],
        "expertise_areas": ["dependency_validation", "security_review", "compliance", "risk_assessment"],
        "workflow_compatibility": ["documentation_mode", "full_development_mode"],
        "activation_conditions": {
            "external_dependencies": True,
            "security_requirements": True,
            "compliance_needed": True
        }
    },
    "Dakota": {
        "title": "Data Engineer",
        "activation_triggers": ["data_requirements", "database_needs", "data_processing"],
        "expertise_areas": ["data_architecture", "database_design", "data_processing", "privacy"],
        "workflow_compatibility": ["documentation_mode", "full_development_mode"],
        "activation_conditions": {
            "data_storage_required": True,
            "data_processing_needed": True,
            "database_complexity": "medium_or_high"
        }
    },
    "Sentinel": {
        "title": "QA Specialist",
        "activation_triggers": ["quality_requirements", "testing_strategy", "quality_assurance"],
        "expertise_areas": ["testing_strategy", "quality_standards", "risk_assessment", "validation"],
        "workflow_compatibility": ["documentation_mode", "full_development_mode"],
        "activation_conditions": {
            "quality_critical": True,
            "testing_required": True,
            "risk_assessment_needed": True
        }
    },
    "DocQA": {
        "title": "Technical Writer",
        "activation_triggers": ["documentation_requirements", "user_guides", "content_creation"],
        "expertise_areas": ["technical_documentation", "user_guides", "content_standards", "accessibility"],
        "workflow_compatibility": ["documentation_mode", "full_development_mode"],
        "activation_conditions": {
            "documentation_heavy": True,
            "user_facing_documentation": True,
            "content_standards_required": True
        }
    }
}

2. Intelligent Activation Engine

Project Analysis for Agent Selection

class ProjectAnalysisEngine:
    """Analyze project requirements to determine optimal agent activation"""
    
    def __init__(self):
        self.requirement_analyzer = RequirementAnalyzer()
        self.complexity_assessor = ComplexityAssessor()
        self.domain_detector = DomainDetector()
    
    def analyze_project_for_agent_activation(self, project_requirements):
        """Analyze project to determine which agents should be activated"""
        
        # Extract project characteristics
        project_characteristics = self.extract_project_characteristics(project_requirements)
        
        # Assess complexity levels
        complexity_assessment = self.complexity_assessor.assess_complexity(project_requirements)
        
        # Detect domain requirements
        domain_requirements = self.domain_detector.detect_domains(project_requirements)
        
        # Determine agent activation recommendations
        activation_recommendations = self.generate_activation_recommendations(
            project_characteristics,
            complexity_assessment,
            domain_requirements
        )
        
        return ProjectAnalysisResult(
            project_characteristics=project_characteristics,
            complexity_assessment=complexity_assessment,
            domain_requirements=domain_requirements,
            activation_recommendations=activation_recommendations
        )
    
    def extract_project_characteristics(self, project_requirements):
        """Extract key characteristics that influence agent selection"""
        
        characteristics = ProjectCharacteristics()
        
        # UI/UX Requirements
        characteristics.has_user_interface = self.detect_ui_requirements(project_requirements)
        characteristics.user_experience_focus = self.detect_ux_focus(project_requirements)
        
        # Infrastructure Requirements  
        characteristics.requires_infrastructure = self.detect_infrastructure_needs(project_requirements)
        characteristics.deployment_complexity = self.assess_deployment_complexity(project_requirements)
        
        # Data Requirements
        characteristics.data_storage_required = self.detect_data_storage_needs(project_requirements)
        characteristics.data_processing_needed = self.detect_data_processing_needs(project_requirements)
        
        # Security Requirements
        characteristics.security_critical = self.assess_security_criticality(project_requirements)
        characteristics.compliance_requirements = self.detect_compliance_needs(project_requirements)
        
        # Quality Requirements
        characteristics.quality_critical = self.assess_quality_criticality(project_requirements)
        characteristics.testing_complexity = self.assess_testing_complexity(project_requirements)
        
        # Documentation Requirements
        characteristics.documentation_heavy = self.assess_documentation_needs(project_requirements)
        characteristics.user_facing_documentation = self.detect_user_documentation_needs(project_requirements)
        
        return characteristics
    
    def generate_activation_recommendations(self, characteristics, complexity, domains):
        """Generate agent activation recommendations based on analysis"""
        
        recommendations = AgentActivationRecommendations()
        
        # Always recommend primary agents
        recommendations.primary_agents = ["John", "Fred", "Tyler"]
        
        # Conditionally recommend secondary agents
        secondary_recommendations = []
        
        # UI/UX Agent (Jane)
        if characteristics.has_user_interface or characteristics.user_experience_focus:
            secondary_recommendations.append({
                "agent": "Jane",
                "reason": "Project requires UI/UX design expertise",
                "confidence": 0.9
            })
        
        # Infrastructure Agent (Alex)
        if characteristics.requires_infrastructure or characteristics.deployment_complexity == "high":
            secondary_recommendations.append({
                "agent": "Alex", 
                "reason": "Project requires infrastructure and deployment expertise",
                "confidence": 0.85
            })
        
        # Development Agent (James)
        if complexity.technical_complexity == "high" or "implementation" in domains:
            secondary_recommendations.append({
                "agent": "James",
                "reason": "Project requires significant implementation expertise", 
                "confidence": 0.8
            })
        
        # Validation Agent (Sage)
        if characteristics.security_critical or characteristics.compliance_requirements:
            secondary_recommendations.append({
                "agent": "Sage",
                "reason": "Project requires security and compliance validation",
                "confidence": 0.9
            })
        
        # Data Agent (Dakota)
        if characteristics.data_storage_required or characteristics.data_processing_needed:
            secondary_recommendations.append({
                "agent": "Dakota",
                "reason": "Project requires data architecture expertise",
                "confidence": 0.85
            })
        
        # QA Agent (Sentinel)
        if characteristics.quality_critical or characteristics.testing_complexity == "high":
            secondary_recommendations.append({
                "agent": "Sentinel",
                "reason": "Project requires comprehensive quality assurance",
                "confidence": 0.8
            })
        
        # Documentation Agent (DocQA)
        if characteristics.documentation_heavy or characteristics.user_facing_documentation:
            secondary_recommendations.append({
                "agent": "DocQA",
                "reason": "Project requires comprehensive documentation",
                "confidence": 0.75
            })
        
        recommendations.secondary_agents = secondary_recommendations
        
        return recommendations

3. Activation Decision Engine

Smart Activation Logic

class AgentActivationDecisionEngine:
    """Make intelligent decisions about agent activation"""
    
    def __init__(self):
        self.project_analyzer = ProjectAnalysisEngine()
        self.activation_optimizer = ActivationOptimizer()
        self.performance_predictor = PerformancePredictor()
    
    def determine_agent_activation(self, workflow_session):
        """Determine which agents should be activated for the workflow"""
        
        # Analyze project requirements
        project_analysis = self.project_analyzer.analyze_project_for_agent_activation(
            workflow_session.project_requirements
        )
        
        # Check full team participation setting
        if workflow_session.full_team_participation_enabled:
            return self.activate_full_team(project_analysis)
        else:
            return self.activate_selective_team(project_analysis)
    
    def activate_full_team(self, project_analysis):
        """Activate all agents for full team participation"""
        
        activation_plan = ActivationPlan()
        
        # Always activate primary agents
        activation_plan.primary_agents = PRIMARY_AGENTS.keys()
        
        # Activate all secondary agents for full team participation
        activation_plan.secondary_agents = SECONDARY_AGENTS.keys()
        
        # Create integration schedule for all agents
        activation_plan.integration_schedule = self.create_full_team_integration_schedule(
            activation_plan.primary_agents + activation_plan.secondary_agents,
            project_analysis
        )
        
        # Predict performance impact
        activation_plan.performance_prediction = self.performance_predictor.predict_full_team_performance(
            activation_plan
        )
        
        return activation_plan
    
    def activate_selective_team(self, project_analysis):
        """Activate selective team based on project requirements"""
        
        activation_plan = ActivationPlan()
        
        # Always activate primary agents
        activation_plan.primary_agents = PRIMARY_AGENTS.keys()
        
        # Selectively activate secondary agents based on recommendations
        selected_secondary_agents = []
        for recommendation in project_analysis.activation_recommendations.secondary_agents:
            if recommendation["confidence"] >= 0.7:  # Confidence threshold
                selected_secondary_agents.append(recommendation["agent"])
        
        activation_plan.secondary_agents = selected_secondary_agents
        
        # Create optimized integration schedule
        activation_plan.integration_schedule = self.create_selective_integration_schedule(
            activation_plan.primary_agents + activation_plan.secondary_agents,
            project_analysis
        )
        
        # Predict performance impact
        activation_plan.performance_prediction = self.performance_predictor.predict_selective_performance(
            activation_plan
        )
        
        return activation_plan
    
    def create_full_team_integration_schedule(self, all_agents, project_analysis):
        """Create integration schedule for full team participation"""
        
        integration_schedule = IntegrationSchedule()
        
        # Define workflow phases
        workflow_phases = [
            "project_analysis",
            "requirements_refinement", 
            "collaborative_planning",
            "document_generation",
            "quality_validation"
        ]
        
        # Schedule agent integration for each phase
        for phase in workflow_phases:
            phase_schedule = self.create_phase_integration_schedule(
                phase,
                all_agents,
                project_analysis
            )
            integration_schedule.add_phase_schedule(phase_schedule)
        
        return integration_schedule
    
    def create_phase_integration_schedule(self, phase, agents, project_analysis):
        """Create integration schedule for specific workflow phase"""
        
        phase_schedule = PhaseIntegrationSchedule(phase_name=phase)
        
        # Define phase-specific agent roles
        phase_agent_roles = {
            "project_analysis": {
                "John": "business_requirements_analysis",
                "Fred": "technical_feasibility_assessment", 
                "Tyler": "scope_and_planning_analysis",
                "Jane": "user_experience_considerations",
                "Alex": "infrastructure_requirements_analysis",
                "James": "implementation_complexity_assessment",
                "Sage": "security_and_compliance_review",
                "Dakota": "data_requirements_analysis",
                "Sentinel": "quality_requirements_assessment",
                "DocQA": "documentation_requirements_analysis"
            },
            "requirements_refinement": {
                "John": "business_requirements_refinement",
                "Fred": "technical_requirements_validation",
                "Tyler": "acceptance_criteria_definition",
                "Jane": "user_experience_requirements_refinement",
                "Alex": "infrastructure_requirements_validation",
                "James": "implementation_requirements_review",
                "Sage": "security_requirements_validation",
                "Dakota": "data_requirements_refinement",
                "Sentinel": "quality_standards_definition",
                "DocQA": "documentation_standards_definition"
            },
            "collaborative_planning": {
                "John": "business_planning_coordination",
                "Fred": "technical_architecture_planning",
                "Tyler": "implementation_planning_coordination",
                "Jane": "user_experience_planning",
                "Alex": "infrastructure_planning",
                "James": "development_methodology_planning",
                "Sage": "security_planning",
                "Dakota": "data_architecture_planning",
                "Sentinel": "quality_assurance_planning",
                "DocQA": "documentation_planning"
            },
            "document_generation": {
                "John": "business_requirements_documentation",
                "Fred": "technical_architecture_documentation",
                "Tyler": "implementation_checklist_creation",
                "Jane": "user_experience_specifications",
                "Alex": "infrastructure_specifications",
                "James": "implementation_guidelines",
                "Sage": "security_and_compliance_documentation",
                "Dakota": "data_architecture_specifications",
                "Sentinel": "quality_assurance_procedures",
                "DocQA": "documentation_standards_and_templates"
            },
            "quality_validation": {
                "John": "business_value_validation",
                "Fred": "technical_architecture_validation",
                "Tyler": "implementation_plan_validation",
                "Jane": "user_experience_validation",
                "Alex": "infrastructure_validation",
                "James": "implementation_feasibility_validation",
                "Sage": "security_and_compliance_validation",
                "Dakota": "data_architecture_validation",
                "Sentinel": "quality_standards_validation",
                "DocQA": "documentation_quality_validation"
            }
        }
        
        # Create integration entries for each agent in the phase
        phase_roles = phase_agent_roles.get(phase, {})
        for agent in agents:
            if agent in phase_roles:
                integration_entry = IntegrationEntry(
                    agent_name=agent,
                    phase_name=phase,
                    role=phase_roles[agent],
                    integration_trigger=self.determine_integration_trigger(agent, phase),
                    expected_contribution=self.define_expected_contribution(agent, phase),
                    quality_criteria=self.define_quality_criteria(agent, phase)
                )
                phase_schedule.add_integration_entry(integration_entry)
        
        return phase_schedule

4. Dynamic Activation Adjustment

Runtime Activation Optimization

class DynamicActivationAdjuster:
    """Dynamically adjust agent activation during workflow execution"""
    
    def __init__(self):
        self.performance_monitor = PerformanceMonitor()
        self.contribution_analyzer = ContributionAnalyzer()
        self.optimization_engine = OptimizationEngine()
    
    def monitor_and_adjust_activation(self, workflow_session):
        """Monitor workflow performance and adjust agent activation as needed"""
        
        # Monitor current performance
        performance_metrics = self.performance_monitor.get_current_metrics(workflow_session)
        
        # Analyze agent contributions
        contribution_analysis = self.contribution_analyzer.analyze_current_contributions(workflow_session)
        
        # Determine if adjustments are needed
        adjustment_recommendations = self.generate_adjustment_recommendations(
            performance_metrics,
            contribution_analysis
        )
        
        # Apply adjustments if beneficial
        if adjustment_recommendations.should_adjust:
            adjustment_result = self.apply_activation_adjustments(
                workflow_session,
                adjustment_recommendations
            )
            return adjustment_result
        
        return NoAdjustmentNeeded()
    
    def generate_adjustment_recommendations(self, performance_metrics, contribution_analysis):
        """Generate recommendations for activation adjustments"""
        
        recommendations = AdjustmentRecommendations()
        
        # Check for performance issues
        if performance_metrics.execution_time > performance_metrics.target_time * 1.2:
            recommendations.add_recommendation(
                "reduce_agent_count",
                "Workflow execution time exceeding target by 20%",
                priority="high"
            )
        
        # Check for insufficient contributions
        low_contribution_agents = [
            agent for agent, contribution in contribution_analysis.agent_contributions.items()
            if contribution.quality_score < 7.0
        ]
        
        if low_contribution_agents:
            recommendations.add_recommendation(
                "provide_guidance",
                f"Agents with low contribution quality: {low_contribution_agents}",
                priority="medium"
            )
        
        # Check for redundant contributions
        redundant_agents = contribution_analysis.identify_redundant_contributions()
        if redundant_agents:
            recommendations.add_recommendation(
                "optimize_agent_roles",
                f"Potential redundancy detected: {redundant_agents}",
                priority="low"
            )
        
        return recommendations

5. Success Metrics and Validation

Activation Logic Success Criteria

• Activation Accuracy: 95% of activated agents provide meaningful contributions

• Performance Optimization: Optimal agent selection reduces workflow time by 15%

• Quality Enhancement: Agent selection improves output quality by 20%

• Resource Efficiency: Intelligent activation reduces unnecessary agent overhead by 30%

• User Satisfaction: 90% user satisfaction with agent team composition

Validation Framework

• A/B Testing: Compare selective vs. full team activation outcomes

• Performance Benchmarking: Measure activation impact on workflow performance

• Quality Assessment: Evaluate contribution quality across different activation strategies

• User Feedback: Collect user feedback on agent team effectiveness

Implementation Status

✅ Agent Classification: Primary and secondary agents clearly defined ✅ Activation Logic: Intelligent activation based on project analysis ✅ Integration Scheduling: Phase-based agent integration framework ✅ Dynamic Adjustment: Runtime optimization and adjustment capabilities ✅ Performance Prediction: Impact assessment for activation decisions

Next Steps: Implement participation tracking system, create command system, integrate with workflows, and validate complete activation logic functionality.