AI Agents Architecture

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from aimatrix.agents import PlannerAgent, ObjectiveDecomposer, ResourcePlanner

class BusinessPlannerAgent(PlannerAgent):
    def __init__(self, domain="business_operations"):
        super().__init__(domain)
        self.objective_decomposer = ObjectiveDecomposer()
        self.resource_planner = ResourcePlanner()
        self.plan_history = []
    
    async def create_execution_plan(self, objective, constraints):
        """Create comprehensive execution plan for business objective"""
        
        # Decompose high-level objective
        sub_objectives = self.objective_decomposer.decompose(
            objective=objective,
            complexity_threshold=0.7,
            max_depth=4
        )
        
        # Analyze resource requirements
        resource_requirements = await self.analyze_resource_needs(sub_objectives)
        
        # Generate execution strategy
        execution_plan = ExecutionPlan(
            id=f"plan_{uuid.uuid4()}",
            main_objective=objective,
            sub_objectives=sub_objectives,
            resource_allocation=resource_requirements,
            timeline=self.generate_timeline(sub_objectives),
            risk_factors=await self.assess_risks(sub_objectives),
            success_criteria=self.define_success_metrics(objective)
        )
        
        # Optimize plan using constraints
        optimized_plan = self.resource_planner.optimize(
            plan=execution_plan,
            constraints=constraints,
            optimization_objectives=["time", "cost", "quality"]
        )
        
        self.plan_history.append(optimized_plan)
        return optimized_plan
    
    async def adapt_plan(self, plan_id, execution_feedback):
        """Adapt existing plan based on execution feedback"""
        
        current_plan = self.get_plan(plan_id)
        performance_analysis = self.analyze_execution_performance(
            plan=current_plan,
            feedback=execution_feedback
        )
        
        if performance_analysis.requires_adaptation:
            adapted_plan = self.modify_plan(
                original_plan=current_plan,
                performance_issues=performance_analysis.issues,
                available_resources=self.get_available_resources()
            )
            
            # Validate adapted plan
            validation_result = await self.validate_plan_feasibility(adapted_plan)
            if validation_result.is_feasible:
                return adapted_plan
            else:
                return await self.create_contingency_plan(current_plan, validation_result)
        
        return current_plan

# Usage example
planner = BusinessPlannerAgent()
plan = await planner.create_execution_plan(
    objective="Launch new product line within 6 months",
    constraints={
        "budget": 2000000,
        "team_size": 25,
        "regulatory_compliance": "FDA_approval_required",
        "market_window": "Q4_holiday_season"
    }
)

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from aimatrix.agents import ExecutorAgent, TaskQueue, ProgressTracker

class ProcessExecutorAgent(ExecutorAgent):
    def __init__(self, process_domain):
        super().__init__(process_domain)
        self.task_queue = TaskQueue(priority_enabled=True)
        self.progress_tracker = ProgressTracker()
        self.execution_context = {}
    
    async def execute_process(self, process_definition, input_data):
        """Execute business process with adaptive error handling"""
        
        execution_id = f"exec_{uuid.uuid4()}"
        self.progress_tracker.start_execution(execution_id)
        
        try:
            # Initialize execution context
            context = ProcessContext(
                execution_id=execution_id,
                process_def=process_definition,
                input_data=input_data,
                start_time=datetime.now()
            )
            
            # Execute process steps
            for step in process_definition.steps:
                step_result = await self.execute_step(step, context)
                
                if step_result.requires_human_intervention:
                    # Escalate to human operator
                    escalation_result = await self.escalate_to_human(
                        step=step,
                        context=context,
                        reason=step_result.intervention_reason
                    )
                    step_result = escalation_result
                
                elif step_result.has_errors:
                    # Attempt automatic recovery
                    recovery_result = await self.attempt_recovery(
                        step=step,
                        error=step_result.error,
                        context=context
                    )
                    
                    if not recovery_result.successful:
                        # Escalate unrecoverable error
                        await self.escalate_error(step, step_result.error, context)
                        break
                
                # Update context and progress
                context.add_step_result(step, step_result)
                self.progress_tracker.update_progress(
                    execution_id, 
                    step.id, 
                    step_result.status
                )
            
            return ProcessExecutionResult(
                execution_id=execution_id,
                status="completed",
                results=context.get_results(),
                metrics=self.progress_tracker.get_metrics(execution_id)
            )
            
        except Exception as e:
            await self.handle_execution_exception(execution_id, e)
            raise ProcessExecutionError(f"Execution {execution_id} failed: {str(e)}")
    
    async def execute_step(self, step, context):
        """Execute individual process step with intelligent adaptation"""
        
        # Select optimal execution strategy
        strategy = self.select_execution_strategy(step, context)
        
        if strategy == "parallel":
            return await self.execute_parallel_step(step, context)
        elif strategy == "distributed":
            return await self.execute_distributed_step(step, context)
        else:
            return await self.execute_sequential_step(step, context)

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from aimatrix.agents import CoworkerAgent, HumanInterface, CollaborationProtocol

class CustomerServiceCoworker(CoworkerAgent):
    def __init__(self):
        super().__init__(role="customer_service_assistant")
        self.human_interface = HumanInterface()
        self.collaboration_protocol = CollaborationProtocol()
        self.knowledge_base = CustomerServiceKB()
    
    async def assist_customer_interaction(self, customer_inquiry):
        """Collaborate with human agent to resolve customer issues"""
        
        # Analyze inquiry complexity
        complexity_assessment = await self.assess_inquiry_complexity(customer_inquiry)
        
        if complexity_assessment.can_handle_autonomously:
            # Handle simple inquiries independently
            response = await self.generate_response(customer_inquiry)
            return await self.send_response(response)
        
        else:
            # Collaborate with human agent
            collaboration_session = await self.start_collaboration_session(
                inquiry=customer_inquiry,
                required_expertise=complexity_assessment.expertise_needed
            )
            
            # Provide context and suggestions to human
            context_brief = self.prepare_context_brief(customer_inquiry)
            suggestions = await self.generate_response_suggestions(customer_inquiry)
            
            await self.human_interface.present_collaboration_context(
                session_id=collaboration_session.id,
                context=context_brief,
                suggestions=suggestions
            )
            
            # Monitor collaboration and provide real-time assistance
            while collaboration_session.is_active:
                human_input = await self.human_interface.get_input(collaboration_session.id)
                
                if human_input.requests_assistance:
                    assistance = await self.provide_contextual_assistance(
                        request=human_input.assistance_request,
                        session_context=collaboration_session.context
                    )
                    await self.human_interface.provide_assistance(assistance)
                
                elif human_input.finalizes_response:
                    # Learn from human decision
                    await self.learn_from_interaction(
                        inquiry=customer_inquiry,
                        human_response=human_input.response,
                        outcome=human_input.outcome
                    )
                    break
            
            return collaboration_session.final_result

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from aimatrix.agents import SpecialistAgent

class FinancialAnalystAgent(SpecialistAgent):
    """Specialized agent for financial analysis and forecasting"""
    
    def __init__(self):
        super().__init__(specialty="financial_analysis")
        self.models = {
            "risk_assessment": RiskAssessmentModel(),
            "cash_flow_forecast": CashFlowModel(), 
            "investment_analysis": InvestmentAnalysisModel(),
            "fraud_detection": FraudDetectionModel()
        }
    
    async def analyze_investment_opportunity(self, investment_data):
        """Comprehensive investment analysis with risk assessment"""
        
        # Multi-model analysis
        risk_profile = await self.models["risk_assessment"].analyze(investment_data)
        cash_flow_projection = await self.models["cash_flow_forecast"].project(
            investment_data, 
            time_horizon="5_years"
        )
        
        # Generate investment recommendation
        recommendation = InvestmentRecommendation(
            opportunity_id=investment_data.id,
            risk_score=risk_profile.composite_score,
            expected_roi=cash_flow_projection.roi,
            confidence_interval=cash_flow_projection.confidence_bounds,
            key_risks=risk_profile.primary_risks,
            mitigation_strategies=await self.suggest_risk_mitigations(risk_profile)
        )
        
        return recommendation

class ComplianceAgent(SpecialistAgent):
    """Regulatory compliance monitoring and enforcement"""
    
    def __init__(self, jurisdiction="US"):
        super().__init__(specialty="regulatory_compliance")
        self.jurisdiction = jurisdiction
        self.regulation_engine = RegulationEngine(jurisdiction)
        self.compliance_monitor = ComplianceMonitor()
    
    async def monitor_compliance_continuous(self):
        """Continuous monitoring of business operations for compliance"""
        
        while True:
            # Scan current business activities
            activities = await self.get_current_business_activities()
            
            for activity in activities:
                compliance_check = await self.regulation_engine.check_compliance(
                    activity=activity,
                    applicable_regulations=self.get_applicable_regulations(activity)
                )
                
                if compliance_check.has_violations:
                    violation_alert = ComplianceAlert(
                        severity=compliance_check.violation_severity,
                        regulation=compliance_check.violated_regulation,
                        activity=activity,
                        required_actions=compliance_check.remediation_steps
                    )
                    
                    await self.escalate_compliance_violation(violation_alert)
            
            await asyncio.sleep(300)  # Check every 5 minutes

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from aimatrix.agents import SwarmController, SwarmBehaviors

class BusinessSwarmController(SwarmController):
    def __init__(self):
        super().__init__()
        self.swarm_behaviors = SwarmBehaviors()
        self.collective_memory = CollectiveMemory()
        self.emergence_detector = EmergenceDetector()
    
    async def orchestrate_complex_objective(self, objective, agent_pool):
        """Orchestrate multiple agents to achieve complex business objective"""
        
        # Initialize swarm for objective
        swarm = self.create_swarm(
            objective=objective,
            agents=agent_pool,
            coordination_pattern="hierarchical_mesh"
        )
        
        # Define swarm behaviors
        behaviors = [
            self.swarm_behaviors.task_distribution(),
            self.swarm_behaviors.information_sharing(),
            self.swarm_behaviors.consensus_building(),
            self.swarm_behaviors.adaptive_reorganization()
        ]
        
        swarm.apply_behaviors(behaviors)
        
        # Execute swarm coordination
        execution_result = await swarm.execute_collaborative_objective()
        
        # Detect emergent behaviors
        emergent_patterns = self.emergence_detector.analyze_execution(
            swarm_execution=execution_result
        )
        
        # Learn from emergent behaviors
        if emergent_patterns.beneficial_patterns:
            await self.incorporate_emergent_patterns(
                patterns=emergent_patterns.beneficial_patterns,
                swarm_template=swarm.template
            )
        
        return execution_result

class SwarmBehaviors:
    """Define collective behaviors for agent swarms"""
    
    def task_distribution(self):
        """Intelligent task distribution based on agent capabilities"""
        return TaskDistributionBehavior(
            strategy="capability_based",
            load_balancing=True,
            dynamic_reassignment=True
        )
    
    def information_sharing(self):
        """Efficient information propagation across swarm"""
        return InformationSharingBehavior(
            protocol="gossip_with_priorities",
            knowledge_aggregation=True,
            conflict_resolution="weighted_consensus"
        )
    
    def consensus_building(self):
        """Democratic decision-making processes"""
        return ConsensusBehavior(
            mechanism="delegated_proof_of_stake",
            voting_weights="expertise_based",
            quorum_requirements=0.67
        )
    
    def adaptive_reorganization(self):
        """Dynamic swarm restructuring based on performance"""
        return AdaptiveReorganizationBehavior(
            trigger_conditions=["performance_degradation", "objective_change"],
            reorganization_strategies=["role_reassignment", "topology_modification"],
            stability_cooldown="15_minutes"
        )

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from aimatrix.agents import EmergenceBehaviorAnalyzer

class BusinessEmergenceAnalyzer(EmergenceBehaviorAnalyzer):
    def __init__(self):
        super().__init__()
        self.pattern_recognition = PatternRecognitionEngine()
        self.behavior_classifier = BehaviorClassifier()
        self.performance_correlator = PerformanceCorrelator()
    
    async def detect_emergent_behaviors(self, swarm_execution_data):
        """Identify emergent behaviors from swarm execution"""
        
        # Extract interaction patterns
        interaction_patterns = self.pattern_recognition.extract_patterns(
            data=swarm_execution_data.agent_interactions,
            pattern_types=["communication", "collaboration", "decision_making"]
        )
        
        # Classify behavior types
        behavior_types = []
        for pattern in interaction_patterns:
            classification = self.behavior_classifier.classify(
                pattern=pattern,
                context=swarm_execution_data.execution_context
            )
            
            if classification.emergence_score > 0.7:  # High emergence threshold
                behavior_types.append(EmergentBehavior(
                    pattern=pattern,
                    type=classification.behavior_type,
                    emergence_score=classification.emergence_score,
                    novelty_score=classification.novelty_score
                ))
        
        # Analyze performance correlation
        performance_impact = await self.analyze_performance_impact(
            emergent_behaviors=behavior_types,
            execution_results=swarm_execution_data.results
        )
        
        return EmergenceAnalysisResult(
            detected_behaviors=behavior_types,
            performance_correlations=performance_impact,
            recommendations=self.generate_emergence_recommendations(
                behavior_types, performance_impact
            )
        )

# Example emergent behaviors observed in business swarms
emergent_behavior_examples = {
    "spontaneous_load_balancing": {
        "description": "Agents automatically redistribute tasks without central coordination",
        "conditions": "High workload variation, diverse agent capabilities",
        "benefits": "30% improvement in throughput, reduced bottlenecks"
    },
    
    "collaborative_learning": {
        "description": "Agents share learned patterns to improve collective performance", 
        "conditions": "Repeated similar tasks, knowledge-sharing mechanisms enabled",
        "benefits": "45% reduction in error rates, faster problem resolution"
    },
    
    "adaptive_hierarchy": {
        "description": "Dynamic leadership roles based on situational expertise",
        "conditions": "Complex multi-domain problems, diverse specialist agents",
        "benefits": "Better decision quality, improved agility"
    },
    
    "predictive_coordination": {
        "description": "Agents anticipate needs and coordinate proactively",
        "conditions": "Predictable workflow patterns, sufficient historical data", 
        "benefits": "25% reduction in coordination overhead, smoother operations"
    }
}

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from aimatrix.agents import CommunicationProtocol, MessageRouter

class BusinessAgentCommunicationProtocol(CommunicationProtocol):
    def __init__(self):
        super().__init__()
        self.message_router = MessageRouter()
        self.protocol_stack = self.build_protocol_stack()
    
    def build_protocol_stack(self):
        """Build layered communication protocol stack"""
        return ProtocolStack([
            # Physical layer - transport mechanisms
            TransportLayer(protocols=["http", "websocket", "message_queue"]),
            
            # Network layer - routing and discovery
            NetworkLayer(
                routing_algorithm="adaptive_shortest_path",
                discovery_mechanism="distributed_hash_table"
            ),
            
            # Session layer - connection management
            SessionLayer(
                connection_pooling=True,
                heartbeat_interval=30,
                reconnection_strategy="exponential_backoff"
            ),
            
            # Presentation layer - message serialization
            PresentationLayer(
                serialization_format="protocol_buffers",
                compression=True,
                encryption="aes_256"
            ),
            
            # Application layer - business logic
            ApplicationLayer(
                message_types=self.define_business_message_types(),
                workflow_protocols=self.define_workflow_protocols()
            )
        ])
    
    def define_business_message_types(self):
        """Define message types for business agent communication"""
        return {
            "task_assignment": TaskAssignmentMessage,
            "progress_update": ProgressUpdateMessage,
            "resource_request": ResourceRequestMessage,
            "collaboration_invite": CollaborationInviteMessage,
            "escalation_alert": EscalationAlertMessage,
            "knowledge_share": KnowledgeShareMessage,
            "consensus_vote": ConsensusVoteMessage,
            "emergency_broadcast": EmergencyBroadcastMessage
        }

class MessageRouter:
    """Intelligent message routing for agent networks"""
    
    def __init__(self):
        self.routing_table = RoutingTable()
        self.load_balancer = LoadBalancer()
        self.priority_queue = PriorityQueue()
    
    async def route_message(self, message, routing_options=None):
        """Route message using intelligent routing algorithms"""
        
        # Determine routing strategy based on message type
        routing_strategy = self.select_routing_strategy(
            message_type=message.type,
            priority=message.priority,
            destination=message.destination,
            options=routing_options
        )
        
        if routing_strategy == "direct":
            return await self.route_direct(message)
        elif routing_strategy == "broadcast":
            return await self.route_broadcast(message)
        elif routing_strategy == "multicast":
            return await self.route_multicast(message)
        elif routing_strategy == "consensus":
            return await self.route_for_consensus(message)
        else:
            return await self.route_adaptive(message)

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from aimatrix.agents import ConsensusManager, ConsensusAlgorithm

class BusinessConsensusManager(ConsensusManager):
    """Manage consensus-building for business decisions"""
    
    def __init__(self):
        super().__init__()
        self.algorithms = {
            "simple_majority": SimpleMajorityConsensus(),
            "weighted_voting": WeightedVotingConsensus(), 
            "delegated_authority": DelegatedAuthorityConsensus(),
            "expertise_based": ExpertiseBasedConsensus(),
            "stakeholder_consensus": StakeholderConsensus()
        }
    
    async def build_consensus(self, decision_context, participating_agents):
        """Build consensus for business decisions"""
        
        # Select appropriate consensus algorithm
        algorithm = self.select_consensus_algorithm(decision_context)
        
        # Initialize consensus round
        consensus_round = ConsensusRound(
            id=f"consensus_{uuid.uuid4()}",
            decision_context=decision_context,
            participants=participating_agents,
            algorithm=algorithm,
            timeout=decision_context.urgency_timeout
        )
        
        # Collect initial positions
        initial_positions = await self.collect_initial_positions(
            participants=participating_agents,
            context=decision_context
        )
        
        # Execute consensus building process
        consensus_result = await algorithm.execute_consensus(
            round=consensus_round,
            initial_positions=initial_positions
        )
        
        # Validate consensus quality
        quality_assessment = await self.assess_consensus_quality(
            result=consensus_result,
            context=decision_context
        )
        
        if quality_assessment.is_acceptable:
            return consensus_result
        else:
            # Attempt consensus improvement
            return await self.improve_consensus(
                initial_result=consensus_result,
                quality_issues=quality_assessment.issues
            )

class ExpertiseBasedConsensus(ConsensusAlgorithm):
    """Weight votes based on agent expertise in relevant domains"""
    
    async def calculate_voting_weights(self, participants, decision_context):
        """Calculate voting weights based on expertise relevance"""
        
        weights = {}
        for agent in participants:
            # Assess agent expertise relevance
            expertise_relevance = await self.assess_expertise_relevance(
                agent_expertise=agent.expertise_profile,
                decision_domain=decision_context.domain,
                decision_complexity=decision_context.complexity
            )
            
            # Calculate base weight from expertise
            base_weight = expertise_relevance.relevance_score
            
            # Apply modifiers
            experience_modifier = self.calculate_experience_modifier(
                agent_experience=agent.experience_history,
                similar_decisions=decision_context.similar_decisions
            )
            
            track_record_modifier = self.calculate_track_record_modifier(
                agent_track_record=agent.decision_track_record,
                decision_type=decision_context.type
            )
            
            final_weight = base_weight * experience_modifier * track_record_modifier
            weights[agent.id] = min(final_weight, 1.0)  # Cap at 1.0
        
        # Normalize weights
        total_weight = sum(weights.values())
        normalized_weights = {
            agent_id: weight / total_weight 
            for agent_id, weight in weights.items()
        }
        
        return normalized_weights

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from aimatrix.agents import RemoteDesktopAgent, ScreenInterpreter, ApplicationController

class BusinessDesktopAgent(RemoteDesktopAgent):
    """Agent capable of interacting with desktop applications"""
    
    def __init__(self):
        super().__init__()
        self.screen_interpreter = ScreenInterpreter()
        self.app_controllers = {
            "excel": ExcelController(),
            "outlook": OutlookController(),
            "erp_system": ERPController(),
            "crm_system": CRMController()
        }
        self.workflow_executor = WorkflowExecutor()
    
    async def execute_desktop_workflow(self, workflow_definition):
        """Execute complex workflows across multiple desktop applications"""
        
        workflow_session = WorkflowSession(
            id=f"desktop_workflow_{uuid.uuid4()}",
            definition=workflow_definition
        )
        
        try:
            for step in workflow_definition.steps:
                if step.type == "application_interaction":
                    result = await self.interact_with_application(
                        app_name=step.application,
                        actions=step.actions,
                        context=workflow_session.context
                    )
                
                elif step.type == "data_extraction":
                    result = await self.extract_data_from_screen(
                        extraction_rules=step.extraction_rules,
                        target_region=step.target_region
                    )
                
                elif step.type == "decision_point":
                    result = await self.make_workflow_decision(
                        decision_criteria=step.criteria,
                        available_options=step.options,
                        context=workflow_session.context
                    )
                
                # Update workflow context
                workflow_session.context.add_step_result(step.id, result)
                
                # Check for workflow completion or branching
                next_step = self.determine_next_step(
                    current_step=step,
                    step_result=result,
                    workflow_context=workflow_session.context
                )
                
                if next_step is None:
                    break  # Workflow complete
            
            return WorkflowExecutionResult(
                session_id=workflow_session.id,
                status="completed",
                results=workflow_session.context.get_final_results()
            )
            
        except Exception as e:
            return await self.handle_workflow_error(workflow_session, e)

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from aimatrix.agents import ServerDaemonAgent, SystemMonitor, ServiceManager

class InfrastructureManagementDaemon(ServerDaemonAgent):
    """Autonomous server and infrastructure management"""
    
    def __init__(self):
        super().__init__(daemon_name="infrastructure_manager")
        self.system_monitor = SystemMonitor()
        self.service_manager = ServiceManager()
        self.alert_manager = AlertManager()
        self.auto_scaler = AutoScaler()
    
    async def run_continuous_monitoring(self):
        """Continuous infrastructure monitoring and management"""
        
        while self.is_running:
            try:
                # Collect system metrics
                metrics = await self.system_monitor.collect_metrics([
                    "cpu_usage", "memory_usage", "disk_usage",
                    "network_io", "application_response_times",
                    "error_rates", "throughput"
                ])
                
                # Analyze system health
                health_analysis = await self.analyze_system_health(metrics)
                
                # Take autonomous actions based on analysis
                if health_analysis.requires_scaling:
                    await self.handle_scaling_requirement(health_analysis.scaling_recommendation)
                
                if health_analysis.has_performance_issues:
                    await self.optimize_performance(health_analysis.performance_issues)
                
                if health_analysis.has_security_concerns:
                    await self.address_security_concerns(health_analysis.security_issues)
                
                # Predictive maintenance
                maintenance_predictions = await self.predict_maintenance_needs(metrics)
                if maintenance_predictions.immediate_action_required:
                    await self.schedule_maintenance(maintenance_predictions.actions)
                
                await asyncio.sleep(self.monitoring_interval)
                
            except Exception as e:
                await self.handle_monitoring_error(e)

class ServiceManager:
    """Manage application services and dependencies"""
    
    async def ensure_service_health(self, service_name):
        """Ensure service is healthy and performing optimally"""
        
        service_status = await self.check_service_status(service_name)
        
        if not service_status.is_healthy:
            # Attempt automatic remediation
            remediation_actions = [
                "restart_service",
                "clear_cache",
                "reload_configuration",
                "scale_instances"
            ]
            
            for action in remediation_actions:
                try:
                    await self.execute_remediation_action(service_name, action)
                    
                    # Verify remediation effectiveness
                    post_action_status = await self.check_service_status(service_name)
                    if post_action_status.is_healthy:
                        await self.log_successful_remediation(service_name, action)
                        break
                        
                except Exception as e:
                    await self.log_remediation_failure(service_name, action, e)
            
            # If all remediation attempts failed, escalate
            if not post_action_status.is_healthy:
                await self.escalate_service_issue(service_name, service_status)

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from aimatrix.agents import IoTEdgeAgent, SensorDataProcessor, EdgeAnalytics

class SmartFacilityEdgeAgent(IoTEdgeAgent):
    """Edge intelligence for smart facility management"""
    
    def __init__(self, facility_id):
        super().__init__(agent_id=f"facility_edge_{facility_id}")
        self.facility_id = facility_id
        self.sensor_processor = SensorDataProcessor()
        self.edge_analytics = EdgeAnalytics()
        self.device_registry = IoTDeviceRegistry()
    
    async def process_facility_data_streams(self):
        """Process multiple IoT data streams for facility optimization"""
        
        # Register and manage IoT devices
        connected_devices = await self.device_registry.discover_devices()
        
        # Set up data processing pipelines
        data_pipelines = {}
        for device in connected_devices:
            pipeline = self.create_processing_pipeline(device)
            data_pipelines[device.id] = pipeline
        
        # Process real-time data streams
        async for sensor_batch in self.sensor_processor.stream_sensor_data():
            for device_id, data in sensor_batch.items():
                if device_id in data_pipelines:
                    processed_data = await data_pipelines[device_id].process(data)
                    
                    # Run edge analytics
                    insights = await self.edge_analytics.analyze(
                        device_id=device_id,
                        processed_data=processed_data,
                        historical_context=self.get_device_history(device_id)
                    )
                    
                    # Take immediate actions if needed
                    if insights.requires_immediate_action:
                        await self.execute_edge_action(insights.recommended_actions)
                    
                    # Update facility optimization models
                    await self.update_optimization_models(device_id, insights)
    
    async def optimize_facility_operations(self):
        """Continuous facility optimization using IoT data"""
        
        while self.is_active:
            # Collect current facility state
            facility_state = await self.get_current_facility_state()
            
            # Run optimization algorithms
            optimization_result = await self.edge_analytics.optimize(
                current_state=facility_state,
                optimization_objectives=[
                    "energy_efficiency",
                    "occupant_comfort", 
                    "equipment_longevity",
                    "operational_cost"
                ]
            )
            
            # Implement optimization recommendations
            for recommendation in optimization_result.recommendations:
                if recommendation.confidence > 0.8:  # High confidence threshold
                    await self.implement_optimization(recommendation)
                else:
                    # Queue for human review
                    await self.queue_for_human_review(recommendation)
            
            await asyncio.sleep(300)  # Optimize every 5 minutes

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# Agent intelligence assessment framework
intelligence_metrics = {
    "planning_capability": {
        "simple_tasks": 0.95,      # 95% success rate
        "complex_objectives": 0.87, # 87% success rate
        "multi_domain": 0.73       # 73% success rate
    },
    
    "adaptation_speed": {
        "environmental_changes": "< 30 seconds",
        "objective_modifications": "< 10 seconds", 
        "failure_recovery": "< 60 seconds"
    },
    
    "collaboration_effectiveness": {
        "human_agent_pairs": 0.89,    # 89% improvement over human-only
        "agent_agent_pairs": 0.94,    # 94% task completion rate
        "swarm_emergence": 0.78       # 78% beneficial emergence rate
    },
    
    "learning_performance": {
        "pattern_recognition": "95% accuracy after 100 examples",
        "process_optimization": "25% improvement after 1 week",
        "domain_adaptation": "80% knowledge transfer rate"
    }
}

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# Agent deployment configuration
agent_cluster:
  name: "business_operations_cluster"
  
  agents:
    planners:
      count: 5
      resources:
        cpu: "2 cores"
        memory: "4GB"
        specializations: ["strategic", "operational", "tactical"]
    
    executors:
      count: 20
      resources:
        cpu: "1 core" 
        memory: "2GB"
        domains: ["finance", "hr", "sales", "operations"]
    
    specialists:
      count: 10
      resources:
        cpu: "4 cores"
        memory: "8GB"
        expertise: ["legal", "compliance", "analytics", "security"]
  
  communication:
    protocol: "aimatrix_agent_protocol_v2"
    encryption: "tls_1.3"
    message_broker: "distributed_kafka"
    
  coordination:
    swarm_controller: "hierarchical_mesh"
    consensus_mechanism: "expertise_weighted_voting"
    emergence_detection: "enabled"
    
  monitoring:
    metrics_collection: "prometheus"
    performance_dashboard: "grafana" 
    alerting: "aimatrix_alertmanager"

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from aimatrix.integration import SystemIntegrator, AgentConnector

class EnterpriseAgentIntegrator(SystemIntegrator):
    def __init__(self):
        super().__init__()
        self.connectors = {
            "erp": ERPAgentConnector(),
            "crm": CRMAgentConnector(),
            "hr": HRSystemConnector(),
            "finance": FinanceSystemConnector()
        }
    
    async def integrate_business_systems(self, agent_cluster):
        """Integrate agent cluster with enterprise systems"""
        
        integration_tasks = []
        
        for system_name, connector in self.connectors.items():
            integration_task = asyncio.create_task(
                self.integrate_single_system(
                    system_name=system_name,
                    connector=connector,
                    agent_cluster=agent_cluster
                )
            )
            integration_tasks.append(integration_task)
        
        integration_results = await asyncio.gather(*integration_tasks)
        
        # Validate integration health
        integration_health = await self.validate_integration_health(
            integration_results
        )
        
        return integration_health

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from aimatrix.agents import AgentCluster, ClusterConfiguration

# 1. Define cluster configuration
config = ClusterConfiguration(
    cluster_name="my_business_agents",
    agent_types=[
        {"type": "planner", "count": 2},
        {"type": "executor", "count": 10}, 
        {"type": "coworker", "count": 5}
    ],
    communication_protocol="standard",
    coordination_pattern="hierarchical"
)

# 2. Deploy agent cluster
cluster = AgentCluster.create(config)
await cluster.deploy()

# 3. Assign business objectives
objectives = [
    "optimize customer onboarding process",
    "improve supply chain efficiency",
    "enhance customer service response times"
]

for objective in objectives:
    await cluster.assign_objective(objective)

# 4. Monitor agent performance
performance_metrics = await cluster.get_performance_metrics()
print(f"Cluster efficiency: {performance_metrics.overall_efficiency:.2%}")