Sindhan Agents Architecture

pub struct DiscoveryAgent {
    // Core capabilities
    perception: Arc<dyn PerceptionEngine>,
    reasoning: Arc<dyn ReasoningEngine>,
    memory: Arc<dyn MemorySystem>,
    knowledge: Arc<dyn KnowledgeBase>,
    
    // Specialized components
    process_miner: ProcessMiner,
    pattern_detector: PatternDetector,
    dependency_mapper: DependencyMapper,
    anomaly_detector: AnomalyDetector,
}
 
impl DiscoveryAgent {
    pub async fn discover_process(&self, context: DiscoveryContext) -> Result<ProcessModel> {
        // Use perception to collect data
        let observations = self.perception
            .observe(context.data_sources)
            .await?;
        
        // Store observations in memory
        self.memory
            .store_episodic(observations.clone())
            .await?;
        
        // Mine process from observations
        let raw_process = self.process_miner
            .mine(observations)
            .await?;
        
        // Use reasoning to validate and enhance
        let enhanced_process = self.reasoning
            .enhance_process_model(raw_process)
            .await?;
        
        // Store in knowledge base
        self.knowledge
            .store_process_model(enhanced_process.clone())
            .await?;
        
        Ok(enhanced_process)
    }
}

impl ProcessMiner {
    pub async fn mine(&self, observations: Vec<Observation>) -> Result<RawProcess> {
        // Alpha algorithm for process mining
        let event_log = self.extract_event_log(observations);
        let causal_relations = self.find_causal_relations(&event_log);
        let process_model = self.build_petri_net(causal_relations);
        
        Ok(process_model)
    }
}
 
impl PatternDetector {
    pub async fn detect_patterns(&self, data: &ProcessData) -> Result<Vec<Pattern>> {
        // Use multiple pattern detection strategies
        let sequential_patterns = self.detect_sequential_patterns(data);
        let temporal_patterns = self.detect_temporal_patterns(data);
        let structural_patterns = self.detect_structural_patterns(data);
        
        Ok(self.merge_patterns(vec![
            sequential_patterns,
            temporal_patterns,
            structural_patterns,
        ]))
    }
}

pub struct AnalysisAgent {
    // Core capabilities
    perception: Arc<dyn PerceptionEngine>,
    reasoning: Arc<dyn ReasoningEngine>,
    memory: Arc<dyn MemorySystem>,
    learning: Arc<dyn LearningFramework>,
    knowledge: Arc<dyn KnowledgeBase>,
    
    // Analysis components
    statistical_analyzer: StatisticalAnalyzer,
    predictive_modeler: PredictiveModeler,
    correlation_finder: CorrelationFinder,
    insight_generator: InsightGenerator,
}
 
impl AnalysisAgent {
    pub async fn analyze_data(&self, request: AnalysisRequest) -> Result<AnalysisReport> {
        // Perceive and prepare data
        let raw_data = self.perception
            .collect_data(request.data_source)
            .await?;
        
        // Clean and engineer features
        let prepared_data = self.prepare_data(raw_data).await?;
        
        // Perform statistical analysis
        let stats = self.statistical_analyzer
            .analyze(&prepared_data)
            .await?;
        
        // Build predictive models
        let predictions = self.predictive_modeler
            .build_models(&prepared_data)
            .await?;
        
        // Find correlations
        let correlations = self.correlation_finder
            .find_correlations(&prepared_data)
            .await?;
        
        // Generate insights using reasoning
        let insights = self.reasoning
            .generate_insights(AnalysisContext {
                statistics: stats,
                predictions,
                correlations,
            })
            .await?;
        
        // Store analysis in memory and knowledge
        self.store_analysis_results(&insights).await?;
        
        Ok(AnalysisReport {
            insights,
            visualizations: self.create_visualizations(&insights),
            recommendations: self.generate_recommendations(&insights),
        })
    }
}

pub struct OptimizationAgent {
    // Core capabilities
    reasoning: Arc<dyn ReasoningEngine>,
    planning: Arc<dyn PlanningSystem>,
    memory: Arc<dyn MemorySystem>,
    knowledge: Arc<dyn KnowledgeBase>,
    
    // Optimization components
    problem_formulator: ProblemFormulator,
    solver_registry: HashMap<ProblemType, Box<dyn OptimizationSolver>>,
    solution_evaluator: SolutionEvaluator,
    implementation_planner: ImplementationPlanner,
}
 
impl OptimizationAgent {
    pub async fn optimize(&self, problem: OptimizationProblem) -> Result<OptimizationSolution> {
        // Formulate the problem
        let formulated = self.problem_formulator
            .formulate(&problem)
            .await?;
        
        // Select appropriate solver using reasoning
        let solver_type = self.reasoning
            .select_solver(&formulated)
            .await?;
        
        let solver = self.solver_registry
            .get(&solver_type)
            .ok_or_else(|| anyhow!("No solver for problem type"))?;
        
        // Solve the optimization problem
        let raw_solution = solver
            .solve(&formulated)
            .await?;
        
        // Evaluate solution quality
        let evaluated = self.solution_evaluator
            .evaluate(&raw_solution, &problem)
            .await?;
        
        // Create implementation plan
        let implementation_plan = self.planning
            .create_implementation_plan(&evaluated)
            .await?;
        
        // Store solution in knowledge base
        self.knowledge
            .store_solution(problem.id, &evaluated)
            .await?;
        
        Ok(OptimizationSolution {
            solution: evaluated,
            implementation_plan,
            sensitivity_analysis: self.perform_sensitivity_analysis(&evaluated).await?,
        })
    }
}
 
// Example solver implementation
impl GeneticAlgorithmSolver {
    pub async fn solve(&self, problem: &FormulatedProblem) -> Result<RawSolution> {
        let mut population = self.initialize_population(problem);
        
        for generation in 0..self.max_generations {
            // Evaluate fitness
            self.evaluate_fitness(&mut population, problem);
            
            // Selection
            let parents = self.select_parents(&population);
            
            // Crossover and mutation
            let offspring = self.crossover_and_mutate(&parents);
            
            // Update population
            population = self.select_survivors(&population, &offspring);
            
            // Check convergence
            if self.has_converged(&population) {
                break;
            }
        }
        
        Ok(self.best_solution(&population))
    }
}

pub struct ExecutionAgent {
    // Core capabilities
    execution_runtime: Arc<dyn ExecutionRuntime>,
    planning: Arc<dyn PlanningSystem>,
    memory: Arc<dyn MemorySystem>,
    communication: Arc<dyn CommunicationHub>,
    
    // Execution components
    resource_manager: ResourceManager,
    task_scheduler: TaskScheduler,
    workflow_engine: WorkflowEngine,
    rollback_manager: RollbackManager,
    execution_monitor: ExecutionMonitor,
}
 
impl ExecutionAgent {
    pub async fn execute_plan(&self, plan: ExecutionPlan) -> Result<ExecutionResult> {
        // Validate plan
        self.validate_plan(&plan).await?;
        
        // Allocate resources
        let resources = self.resource_manager
            .allocate_for_plan(&plan)
            .await?;
        
        // Create execution context
        let context = ExecutionContext {
            plan: plan.clone(),
            resources,
            checkpoints: Vec::new(),
        };
        
        // Execute workflow
        let result = self.workflow_engine
            .execute_with_monitoring(context, |status| {
                self.execution_monitor.update_status(status)
            })
            .await;
        
        match result {
            Ok(success) => {
                self.memory.store_execution_success(&success).await?;
                Ok(success)
            }
            Err(failure) => {
                // Attempt rollback
                self.rollback_manager
                    .rollback_to_checkpoint(&failure.last_checkpoint)
                    .await?;
                    
                self.memory.store_execution_failure(&failure).await?;
                Err(failure.into())
            }
        }
    }
}
 
impl WorkflowEngine {
    pub async fn execute_workflow(&self, workflow: Workflow) -> Result<WorkflowResult> {
        let mut state = WorkflowState::new();
        
        for stage in workflow.stages {
            match stage {
                Stage::Parallel(tasks) => {
                    let results = self.execute_parallel(tasks, &state).await?;
                    state.merge_results(results);
                }
                Stage::Sequential(tasks) => {
                    for task in tasks {
                        let result = self.execute_task(task, &state).await?;
                        state.update_with_result(result);
                    }
                }
                Stage::Conditional(condition, branches) => {
                    let branch = self.evaluate_condition(condition, &state)?;
                    let result = self.execute_workflow(branches[branch]).await?;
                    state.merge_workflow_result(result);
                }
            }
        }
        
        Ok(WorkflowResult::from_state(state))
    }
}

pub struct MonitoringAgent {
    // Core capabilities
    perception: Arc<dyn PerceptionEngine>,
    reasoning: Arc<dyn ReasoningEngine>,
    memory: Arc<dyn MemorySystem>,
    communication: Arc<dyn CommunicationHub>,
    
    // Monitoring components
    metric_collector: MetricCollector,
    anomaly_detector: AnomalyDetector,
    alert_manager: AlertManager,
    dashboard_builder: DashboardBuilder,
}
 
impl MonitoringAgent {
    pub async fn monitor_system(&self) -> Result<()> {
        let monitoring_loop = async {
            loop {
                // Collect metrics
                let metrics = self.metric_collector
                    .collect_all_metrics()
                    .await?;
                
                // Detect anomalies
                let anomalies = self.anomaly_detector
                    .detect(&metrics)
                    .await?;
                
                // Process anomalies
                for anomaly in anomalies {
                    self.handle_anomaly(anomaly).await?;
                }
                
                // Update dashboards
                self.dashboard_builder
                    .update_metrics(&metrics)
                    .await?;
                
                tokio::time::sleep(self.monitoring_interval).await;
            }
        };
        
        monitoring_loop.await
    }
    
    async fn handle_anomaly(&self, anomaly: Anomaly) -> Result<()> {
        // Use reasoning to determine severity
        let severity = self.reasoning
            .assess_anomaly_severity(&anomaly)
            .await?;
        
        // Create alert if needed
        if severity > self.alert_threshold {
            let alert = Alert {
                anomaly,
                severity,
                timestamp: Utc::now(),
                context: self.gather_context(&anomaly).await?,
            };
            
            // Route alert
            self.alert_manager
                .route_alert(alert)
                .await?;
        }
        
        // Store in memory for pattern analysis
        self.memory
            .store_anomaly_record(&anomaly)
            .await?;
        
        Ok(())
    }
}
 
impl AnomalyDetector {
    pub async fn detect(&self, metrics: &MetricSet) -> Result<Vec<Anomaly>> {
        let mut anomalies = Vec::new();
        
        // Statistical anomaly detection
        anomalies.extend(self.detect_statistical_anomalies(metrics).await?);
        
        // Machine learning based detection
        anomalies.extend(self.detect_ml_anomalies(metrics).await?);
        
        // Rule-based detection
        anomalies.extend(self.detect_rule_based_anomalies(metrics).await?);
        
        // Deduplicate and prioritize
        Ok(self.deduplicate_and_prioritize(anomalies))
    }
}

pub struct LearningAgent {
    // Core capabilities
    learning_framework: Arc<dyn LearningFramework>,
    memory: Arc<dyn MemorySystem>,
    knowledge: Arc<dyn KnowledgeBase>,
    reasoning: Arc<dyn ReasoningEngine>,
    
    // Learning components
    data_pipeline: DataPipeline,
    model_manager: ModelManager,
    continuous_learner: ContinuousLearner,
    knowledge_evolver: KnowledgeEvolver,
}
 
impl LearningAgent {
    pub async fn continuous_learning_cycle(&self) -> Result<()> {
        loop {
            // Collect new training data
            let new_data = self.data_pipeline
                .collect_training_data()
                .await?;
            
            // Evaluate current models
            let evaluation = self.model_manager
                .evaluate_all_models(&new_data)
                .await?;
            
            // Identify models needing update
            let models_to_update = self.identify_models_for_update(&evaluation);
            
            // Update models
            for model_id in models_to_update {
                self.update_model(model_id, &new_data).await?;
            }
            
            // Evolve knowledge base
            self.knowledge_evolver
                .evolve_knowledge(&new_data, &evaluation)
                .await?;
            
            // Sleep until next cycle
            tokio::time::sleep(self.learning_interval).await;
        }
    }
    
    async fn update_model(&self, model_id: ModelId, data: &TrainingData) -> Result<()> {
        // Get current model
        let current_model = self.model_manager
            .get_model(&model_id)
            .await?;
        
        // Select learning strategy
        let strategy = self.reasoning
            .select_learning_strategy(&current_model, data)
            .await?;
        
        // Train new model version
        let new_model = match strategy {
            LearningStrategy::Online => {
                self.continuous_learner
                    .update_online(&current_model, data)
                    .await?
            }
            LearningStrategy::Batch => {
                self.continuous_learner
                    .retrain_batch(&current_model, data)
                    .await?
            }
            LearningStrategy::Transfer => {
                self.continuous_learner
                    .transfer_learn(&current_model, data)
                    .await?
            }
        };
        
        // Validate new model
        if self.validate_model(&new_model, data).await? {
            // Deploy new model
            self.model_manager
                .deploy_model(model_id, new_model)
                .await?;
                
            // Update knowledge base
            self.knowledge
                .update_model_knowledge(model_id, &new_model)
                .await?;
        }
        
        Ok(())
    }
}

pub async fn parallel_agent_collaboration(
    agents: &AgentRegistry,
    task: CollaborativeTask,
) -> Result<CollaborationResult> {
    let discovery_future = agents.discovery.discover(&task.context);
    let analysis_future = agents.analysis.analyze(&task.data);
    let monitoring_future = agents.monitoring.monitor(&task.scope);
    
    let (discovery_result, analysis_result, monitoring_result) = tokio::join!(
        discovery_future,
        analysis_future,
        monitoring_future
    );
    
    // Merge results
    let merged_insights = merge_agent_results(vec![
        discovery_result?,
        analysis_result?,
        monitoring_result?,
    ]);
    
    // Optimization based on merged insights
    let optimization_plan = agents.optimization
        .optimize_with_insights(merged_insights)
        .await?;
    
    // Execute optimized plan
    agents.execution
        .execute(optimization_plan)
        .await
}

pub struct AgentCoordinator {
    agents: HashMap<AgentType, Arc<dyn SindhanAgent>>,
    workflow_engine: WorkflowEngine,
}
 
impl AgentCoordinator {
    pub async fn coordinate_complex_task(&self, task: ComplexTask) -> Result<TaskResult> {
        // Create workflow based on task type
        let workflow = self.create_workflow(&task)?;
        
        // Execute workflow with agent coordination
        self.workflow_engine
            .execute_with_agents(workflow, &self.agents)
            .await
    }
    
    fn create_workflow(&self, task: &ComplexTask) -> Result<AgentWorkflow> {
        match task.task_type {
            TaskType::ProcessOptimization => {
                Ok(AgentWorkflow::sequential(vec![
                    AgentStep::Discovery(DiscoveryParams::from(task)),
                    AgentStep::Analysis(AnalysisParams::from(task)),
                    AgentStep::Optimization(OptimizationParams::from(task)),
                    AgentStep::Execution(ExecutionParams::from(task)),
                ]))
            }
            TaskType::ContinuousImprovement => {
                Ok(AgentWorkflow::cyclic(vec![
                    AgentStep::Monitoring(MonitoringParams::from(task)),
                    AgentStep::Learning(LearningParams::from(task)),
                    AgentStep::Optimization(OptimizationParams::from(task)),
                    AgentStep::Execution(ExecutionParams::from(task)),
                ]))
            }
            // Other task types...
        }
    }
}

pub struct AgentScaler {
    metrics_collector: MetricsCollector,
    scaling_policies: HashMap<AgentType, ScalingPolicy>,
}
 
impl AgentScaler {
    pub async fn auto_scale(&self) -> Result<()> {
        let metrics = self.metrics_collector.collect_all().await?;
        
        for (agent_type, policy) in &self.scaling_policies {
            let agent_metrics = metrics.get(agent_type);
            
            if policy.should_scale_up(agent_metrics) {
                self.scale_up(agent_type).await?;
            } else if policy.should_scale_down(agent_metrics) {
                self.scale_down(agent_type).await?;
            }
        }
        
        Ok(())
    }
}