# Performance Testing Framework Architecture

DSPEx.Performance.Framework
├── DSPEx.Performance.Baseline               # Foundation infrastructure baselines
├── DSPEx.Performance.Benchmarks             # Comprehensive benchmark suite
├── DSPEx.Performance.Scenarios              # Real-world testing scenarios
├── DSPEx.Performance.Monitoring             # Continuous performance monitoring
├── DSPEx.Performance.Analysis               # Performance data analysis
├── DSPEx.Performance.Validation             # Automated validation and reporting
└── DSPEx.Performance.Optimization          # Performance optimization recommendations

defmodule DSPEx.Performance.Benchmarks.AgentRegistration do
  @moduledoc """
  Benchmarks for agent registration and discovery performance
  """
  
  use DSPEx.Performance.BenchmarkSuite
  
  @benchmark_scenarios [
    {:single_agent_registration, %{agents: 1, iterations: 1000}},
    {:bulk_agent_registration, %{agents: 100, iterations: 10}},
    {:concurrent_registration, %{agents: 50, concurrency: 10}},
    {:discovery_queries, %{registered_agents: 1000, queries: 500}},
    {:capability_matching, %{agents: 500, capabilities: 20}},
    {:cluster_discovery, %{nodes: 10, agents_per_node: 100}}
  ]
  
  def benchmark_single_agent_registration(config) do
    agent_configs = generate_agent_configs(config.agents)
    
    measure_operation("single_agent_registration", config.iterations) do
      Enum.each(agent_configs, fn agent_config ->
        {:ok, agent_pid} = DSPEx.Agents.TestAgent.start_link(agent_config)
        
        # Measure registration time
        start_time = System.monotonic_time(:microsecond)
        {:ok, registration_info} = DSPEx.Foundation.Bridge.register_agent(agent_pid)
        end_time = System.monotonic_time(:microsecond)
        
        registration_time = end_time - start_time
        
        # Cleanup
        GenServer.stop(agent_pid)
        
        registration_time
      end)
    end
  end
  
  def benchmark_discovery_queries(config) do
    # Pre-register agents
    agents = setup_registered_agents(config.registered_agents)
    
    discovery_queries = [
      {:capability_query, :model_management},
      {:resource_query, %{min_memory: 0.5, min_cpu: 0.3}},
      {:health_query, :healthy},
      {:geographic_query, :us_east},
      {:composite_query, %{capability: :optimization, health: :healthy, resources: %{memory: 0.7}}}
    ]
    
    measure_operation("discovery_queries", config.queries) do
      query = Enum.random(discovery_queries)
      
      start_time = System.monotonic_time(:microsecond)
      {:ok, discovered_agents} = execute_discovery_query(query)
      end_time = System.monotonic_time(:microsecond)
      
      discovery_time = end_time - start_time
      
      %{
        query_time: discovery_time,
        results_count: length(discovered_agents),
        query_type: elem(query, 0)
      }
    end
  end
  
  def benchmark_cluster_discovery(config) do
    # Setup multi-node test environment
    nodes = setup_test_cluster(config.nodes, config.agents_per_node)
    
    measure_operation("cluster_discovery", 100) do
      start_time = System.monotonic_time(:microsecond)
      {:ok, cluster_topology} = DSPEx.Clustering.Agents.NodeDiscovery.discover_cluster()
      end_time = System.monotonic_time(:microsecond)
      
      discovery_time = end_time - start_time
      
      %{
        discovery_time: discovery_time,
        nodes_discovered: length(cluster_topology.nodes),
        agents_discovered: count_discovered_agents(cluster_topology),
        topology_accuracy: validate_topology_accuracy(cluster_topology, nodes)
      }
    end
  end
end

defmodule DSPEx.Performance.Benchmarks.CognitiveVariables do
  @moduledoc """
  Comprehensive benchmarks for Cognitive Variables performance
  """
  
  use DSPEx.Performance.BenchmarkSuite
  
  @variable_types [
    {:cognitive_float, DSPEx.Variables.CognitiveFloat},
    {:cognitive_choice, DSPEx.Variables.CognitiveChoice},
    {:cognitive_agent_team, DSPEx.Variables.CognitiveAgentTeam}
  ]
  
  def benchmark_variable_creation(config) do
    variable_configs = generate_variable_configs(config.variable_count)
    
    measure_operation("variable_creation", config.iterations) do
      Enum.map(variable_configs, fn {type, config} ->
        start_time = System.monotonic_time(:microsecond)
        {:ok, variable_pid} = create_variable(type, config)
        end_time = System.monotonic_time(:microsecond)
        
        creation_time = end_time - start_time
        
        # Cleanup
        GenServer.stop(variable_pid)
        
        %{type: type, creation_time: creation_time}
      end)
    end
  end
  
  def benchmark_value_updates(config) do
    # Pre-create variables
    variables = setup_test_variables(config.variable_count)
    
    measure_operation("value_updates", config.updates_per_variable) do
      variable = Enum.random(variables)
      new_value = generate_random_value(variable.type)
      
      start_time = System.monotonic_time(:microsecond)
      :ok = Jido.Signal.send(variable.pid, {:change_value, new_value, %{requester: :benchmark}})
      
      # Wait for confirmation
      receive do
        {:variable_updated, ^variable, _old_value, ^new_value} ->
          end_time = System.monotonic_time(:microsecond)
          end_time - start_time
      after 5000 ->
        {:error, :timeout}
      end
    end
  end
  
  def benchmark_coordination_performance(config) do
    # Setup coordinated variables with affected agents
    coordination_setup = setup_coordination_scenario(config)
    
    measure_operation("coordination_performance", config.coordination_events) do
      scenario = Enum.random(coordination_setup.scenarios)
      
      start_time = System.monotonic_time(:microsecond)
      coordination_result = execute_coordination_scenario(scenario)
      end_time = System.monotonic_time(:microsecond)
      
      coordination_time = end_time - start_time
      
      %{
        coordination_time: coordination_time,
        scenario_type: scenario.type,
        affected_agents: length(scenario.affected_agents),
        coordination_success: coordination_result.success,
        consensus_time: coordination_result.consensus_time,
        notification_time: coordination_result.notification_time
      }
    end
  end
  
  def benchmark_adaptation_performance(config) do
    # Setup variables with performance feedback
    adaptive_variables = setup_adaptive_variables(config.variable_count)
    
    measure_operation("adaptation_performance", config.adaptation_cycles) do
      variable = Enum.random(adaptive_variables)
      feedback_data = generate_performance_feedback(variable)
      
      start_time = System.monotonic_time(:microsecond)
      :ok = Jido.Signal.send(variable.pid, {:performance_feedback, feedback_data})
      
      # Wait for adaptation completion
      receive do
        {:adaptation_completed, ^variable, adaptation_result} ->
          end_time = System.monotonic_time(:microsecond)
          
          %{
            adaptation_time: end_time - start_time,
            value_changed: adaptation_result.value_changed,
            adaptation_magnitude: adaptation_result.magnitude,
            coordination_triggered: adaptation_result.coordination_triggered
          }
      after 10000 ->
        {:error, :adaptation_timeout}
      end
    end
  end
end

defmodule DSPEx.Performance.Benchmarks.AgentClustering do
  @moduledoc """
  Benchmarks for agent-native clustering performance vs traditional clustering
  """
  
  use DSPEx.Performance.BenchmarkSuite
  
  def benchmark_node_discovery_performance(config) do
    # Setup test cluster with varying node counts
    cluster_sizes = [5, 10, 25, 50, 100]
    
    Enum.map(cluster_sizes, fn node_count ->
      test_cluster = setup_test_cluster(node_count)
      
      # Benchmark agent-native discovery
      agent_discovery_time = measure_operation("agent_node_discovery") do
        {:ok, agent_pid} = DSPEx.Clustering.Agents.NodeDiscovery.start_link([])
        
        start_time = System.monotonic_time(:microsecond)
        {:ok, topology} = DSPEx.Clustering.Agents.NodeDiscovery.discover_cluster_topology(agent_pid)
        end_time = System.monotonic_time(:microsecond)
        
        GenServer.stop(agent_pid)
        
        %{
          discovery_time: end_time - start_time,
          nodes_discovered: length(topology.nodes),
          accuracy: validate_discovery_accuracy(topology, test_cluster)
        }
      end
      
      # Benchmark traditional service-based discovery (if available)
      traditional_discovery_time = measure_operation("traditional_node_discovery") do
        start_time = System.monotonic_time(:microsecond)
        {:ok, nodes} = traditional_cluster_discovery(test_cluster)
        end_time = System.monotonic_time(:microsecond)
        
        %{
          discovery_time: end_time - start_time,
          nodes_discovered: length(nodes)
        }
      end
      
      %{
        cluster_size: node_count,
        agent_native: agent_discovery_time,
        traditional: traditional_discovery_time,
        performance_ratio: calculate_performance_ratio(agent_discovery_time, traditional_discovery_time)
      }
    end)
  end
  
  def benchmark_load_balancing_performance(config) do
    # Setup load balancing scenarios
    load_scenarios = [
      {:low_load, %{requests_per_second: 100, nodes: 5}},
      {:medium_load, %{requests_per_second: 1000, nodes: 10}},
      {:high_load, %{requests_per_second: 10000, nodes: 20}},
      {:spike_load, %{requests_per_second: 50000, nodes: 50}}
    ]
    
    Enum.map(load_scenarios, fn {scenario_name, scenario_config} ->
      # Setup load balancing environment
      load_balancer_setup = setup_load_balancer_environment(scenario_config)
      
      # Benchmark agent-native load balancing
      agent_lb_results = benchmark_agent_load_balancing(load_balancer_setup, scenario_config)
      
      # Benchmark traditional load balancing
      traditional_lb_results = benchmark_traditional_load_balancing(load_balancer_setup, scenario_config)
      
      %{
        scenario: scenario_name,
        agent_native: agent_lb_results,
        traditional: traditional_lb_results,
        performance_comparison: compare_load_balancing_performance(agent_lb_results, traditional_lb_results)
      }
    end)
  end
  
  def benchmark_health_monitoring_performance(config) do
    # Setup health monitoring scenarios
    monitoring_scenarios = [
      {:basic_health_checks, %{nodes: 10, check_interval: 30_000}},
      {:comprehensive_monitoring, %{nodes: 25, check_interval: 10_000}},
      {:predictive_monitoring, %{nodes: 50, check_interval: 5_000}},
      {:large_scale_monitoring, %{nodes: 100, check_interval: 15_000}}
    ]
    
    Enum.map(monitoring_scenarios, fn {scenario_name, scenario_config} ->
      # Setup monitoring environment
      monitoring_setup = setup_health_monitoring_environment(scenario_config)
      
      # Benchmark agent-native health monitoring
      agent_monitoring_results = measure_operation("agent_health_monitoring", 100) do
        {:ok, health_agent} = DSPEx.Clustering.Agents.HealthMonitor.start_link(scenario_config)
        
        start_time = System.monotonic_time(:microsecond)
        {:ok, health_results} = DSPEx.Clustering.Agents.HealthMonitor.perform_cluster_health_check(health_agent)
        end_time = System.monotonic_time(:microsecond)
        
        GenServer.stop(health_agent)
        
        %{
          monitoring_time: end_time - start_time,
          nodes_checked: length(health_results),
          health_accuracy: validate_health_accuracy(health_results, monitoring_setup),
          anomalies_detected: count_anomalies(health_results),
          predictive_insights: count_predictive_insights(health_results)
        }
      end
      
      # Benchmark traditional health monitoring
      traditional_monitoring_results = measure_operation("traditional_health_monitoring", 100) do
        start_time = System.monotonic_time(:microsecond)
        {:ok, health_status} = traditional_health_monitoring(monitoring_setup)
        end_time = System.monotonic_time(:microsecond)
        
        %{
          monitoring_time: end_time - start_time,
          nodes_checked: length(health_status)
        }
      end
      
      %{
        scenario: scenario_name,
        agent_native: agent_monitoring_results,
        traditional: traditional_monitoring_results,
        performance_improvement: calculate_monitoring_improvement(agent_monitoring_results, traditional_monitoring_results)
      }
    end)
  end
end

defmodule DSPEx.Performance.Benchmarks.MLWorkflows do
  @moduledoc """
  End-to-end benchmarks for ML workflows using Cognitive Variables
  """
  
  use DSPEx.Performance.BenchmarkSuite
  
  def benchmark_program_creation_and_optimization(config) do
    # Test various program complexities
    program_complexities = [
      {:simple, %{variables: 3, agents: 2, constraints: 1}},
      {:moderate, %{variables: 8, agents: 5, constraints: 3}},
      {:complex, %{variables: 15, agents: 10, constraints: 7}},
      {:enterprise, %{variables: 30, agents: 20, constraints: 15}}
    ]
    
    Enum.map(program_complexities, fn {complexity, config} ->
      # Benchmark program creation
      creation_time = measure_operation("program_creation") do
        signature = generate_ml_signature(config)
        
        start_time = System.monotonic_time(:microsecond)
        {:ok, program} = DSPEx.Program.new(signature, [
          cognitive_variables_enabled: true,
          agent_coordination: true,
          foundation_integration: true
        ])
        end_time = System.monotonic_time(:microsecond)
        
        creation_time = end_time - start_time
        
        # Cleanup
        DSPEx.Program.cleanup(program)
        
        creation_time
      end
      
      # Benchmark program optimization
      optimization_time = measure_operation("program_optimization") do
        signature = generate_ml_signature(config)
        {:ok, program} = DSPEx.Program.new(signature, [cognitive_variables_enabled: true])
        training_data = generate_training_data(config.complexity)
        
        start_time = System.monotonic_time(:microsecond)
        {:ok, optimization_result} = DSPEx.Program.optimize(program, training_data, [
          max_iterations: 10,
          optimization_strategy: :cognitive_variables
        ])
        end_time = System.monotonic_time(:microsecond)
        
        optimization_time = end_time - start_time
        
        # Cleanup
        DSPEx.Program.cleanup(program)
        
        %{
          optimization_time: optimization_time,
          iterations_completed: optimization_result.iterations,
          final_performance: optimization_result.performance,
          variables_adapted: optimization_result.variables_adapted
        }
      end
      
      %{
        complexity: complexity,
        creation_time: creation_time,
        optimization_results: optimization_time
      }
    end)
  end
  
  def benchmark_cognitive_vs_traditional_optimization(config) do
    # Compare cognitive variables vs traditional parameter optimization
    optimization_scenarios = [
      {:hyperparameter_tuning, generate_hyperparameter_scenario()},
      {:model_selection, generate_model_selection_scenario()},
      {:architecture_optimization, generate_architecture_scenario()},
      {:resource_optimization, generate_resource_scenario()}
    ]
    
    Enum.map(optimization_scenarios, fn {scenario_name, scenario_config} ->
      # Benchmark cognitive variables approach
      cognitive_results = measure_operation("cognitive_optimization", 5) do
        {:ok, program} = create_cognitive_program(scenario_config)
        training_data = scenario_config.training_data
        
        start_time = System.monotonic_time(:microsecond)
        {:ok, result} = DSPEx.Program.optimize(program, training_data, [
          strategy: :cognitive_variables,
          max_iterations: 20
        ])
        end_time = System.monotonic_time(:microsecond)
        
        DSPEx.Program.cleanup(program)
        
        %{
          optimization_time: end_time - start_time,
          final_performance: result.performance,
          iterations: result.iterations,
          convergence_speed: result.convergence_metrics
        }
      end
      
      # Benchmark traditional optimization approach
      traditional_results = measure_operation("traditional_optimization", 5) do
        traditional_optimizer = create_traditional_optimizer(scenario_config)
        training_data = scenario_config.training_data
        
        start_time = System.monotonic_time(:microsecond)
        {:ok, result} = TraditionalOptimizer.optimize(traditional_optimizer, training_data, [
          strategy: :grid_search,  # or :random_search, :bayesian
          max_iterations: 20
        ])
        end_time = System.monotonic_time(:microsecond)
        
        %{
          optimization_time: end_time - start_time,
          final_performance: result.performance,
          iterations: result.iterations
        }
      end
      
      %{
        scenario: scenario_name,
        cognitive_variables: cognitive_results,
        traditional: traditional_results,
        performance_comparison: %{
          speed_improvement: cognitive_results.optimization_time / traditional_results.optimization_time,
          quality_improvement: cognitive_results.final_performance - traditional_results.final_performance,
          convergence_improvement: analyze_convergence_improvement(cognitive_results, traditional_results)
        }
      }
    end)
  end
end

defmodule DSPEx.Performance.Benchmarks.Scalability do
  @moduledoc """
  Large-scale system benchmarks for validating scalability
  """
  
  use DSPEx.Performance.BenchmarkSuite
  
  def benchmark_large_scale_agent_coordination(config) do
    # Test coordination with varying numbers of agents
    agent_scales = [100, 500, 1000, 5000, 10000]
    
    Enum.map(agent_scales, fn agent_count ->
      # Setup large-scale agent environment
      agent_environment = setup_large_scale_environment(agent_count)
      
      coordination_results = measure_operation("large_scale_coordination", 10) do
        coordination_scenarios = [
          {:consensus_coordination, %{participants: agent_count / 10}},
          {:broadcast_coordination, %{targets: agent_count / 5}},
          {:hierarchical_coordination, %{levels: 3, agents_per_level: agent_count / 3}}
        ]
        
        Enum.map(coordination_scenarios, fn {scenario_type, scenario_config} ->
          start_time = System.monotonic_time(:microsecond)
          coordination_result = execute_large_scale_coordination(scenario_type, scenario_config, agent_environment)
          end_time = System.monotonic_time(:microsecond)
          
          %{
            scenario: scenario_type,
            coordination_time: end_time - start_time,
            success_rate: coordination_result.success_rate,
            agent_participation: coordination_result.participation_rate,
            network_overhead: coordination_result.network_metrics
          }
        end)
      end
      
      %{
        agent_count: agent_count,
        coordination_results: coordination_results,
        system_resource_usage: measure_system_resources(),
        scalability_metrics: calculate_scalability_metrics(coordination_results, agent_count)
      }
    end)
  end
  
  def benchmark_distributed_cluster_performance(config) do
    # Test performance across multiple nodes
    cluster_configurations = [
      {:small_cluster, %{nodes: 3, agents_per_node: 100}},
      {:medium_cluster, %{nodes: 10, agents_per_node: 200}},
      {:large_cluster, %{nodes: 25, agents_per_node: 300}},
      {:enterprise_cluster, %{nodes: 50, agents_per_node: 500}}
    ]
    
    Enum.map(cluster_configurations, fn {cluster_type, cluster_config} ->
      # Setup distributed cluster
      cluster = setup_distributed_cluster(cluster_config)
      
      cluster_performance = measure_operation("distributed_performance", 20) do
        # Test various distributed operations
        distributed_operations = [
          {:cross_node_discovery, %{}},
          {:cluster_wide_consensus, %{proposal: generate_test_proposal()}},
          {:distributed_load_balancing, %{requests: 1000}},
          {:cross_cluster_coordination, %{coordination_type: :resource_allocation}}
        ]
        
        Enum.map(distributed_operations, fn {operation_type, operation_config} ->
          start_time = System.monotonic_time(:microsecond)
          operation_result = execute_distributed_operation(operation_type, operation_config, cluster)
          end_time = System.monotonic_time(:microsecond)
          
          %{
            operation: operation_type,
            execution_time: end_time - start_time,
            success_rate: operation_result.success_rate,
            network_latency: operation_result.network_metrics.average_latency,
            resource_efficiency: operation_result.resource_metrics
          }
        end)
      end
      
      %{
        cluster_type: cluster_type,
        cluster_config: cluster_config,
        performance_results: cluster_performance,
        resource_utilization: measure_cluster_resources(cluster),
        scalability_analysis: analyze_cluster_scalability(cluster_performance, cluster_config)
      }
    end)
  end
end

defmodule DSPEx.Performance.Benchmarks.ResourceEfficiency do
  @moduledoc """
  Comprehensive resource efficiency benchmarks
  """
  
  use DSPEx.Performance.BenchmarkSuite
  
  def benchmark_memory_efficiency(config) do
    # Test memory usage across different scenarios
    memory_scenarios = [
      {:baseline, %{agents: 0, variables: 0}},
      {:small_system, %{agents: 10, variables: 20}},
      {:medium_system, %{agents: 50, variables: 100}},
      {:large_system, %{agents: 200, variables: 500}},
      {:enterprise_system, %{agents: 1000, variables: 2000}}
    ]
    
    Enum.map(memory_scenarios, fn {scenario_name, scenario_config} ->
      # Measure baseline memory
      baseline_memory = measure_system_memory()
      
      # Setup scenario
      system_components = setup_memory_test_scenario(scenario_config)
      
      # Measure memory after setup
      setup_memory = measure_system_memory()
      
      # Run memory stress operations
      stress_results = run_memory_stress_operations(system_components, 1000)
      
      # Measure peak memory
      peak_memory = measure_system_memory()
      
      # Cleanup and measure final memory
      cleanup_system_components(system_components)
      final_memory = measure_system_memory()
      
      %{
        scenario: scenario_name,
        baseline_memory: baseline_memory,
        setup_memory: setup_memory,
        peak_memory: peak_memory,
        final_memory: final_memory,
        memory_efficiency: %{
          memory_per_agent: calculate_memory_per_agent(setup_memory, baseline_memory, scenario_config.agents),
          memory_per_variable: calculate_memory_per_variable(setup_memory, baseline_memory, scenario_config.variables),
          memory_leak_rate: calculate_memory_leak_rate(setup_memory, final_memory),
          peak_memory_multiplier: peak_memory / setup_memory
        },
        stress_results: stress_results
      }
    end)
  end
  
  def benchmark_cpu_efficiency(config) do
    # Test CPU usage patterns
    cpu_scenarios = [
      {:idle_system, %{load_level: 0.0}},
      {:light_load, %{load_level: 0.2}},
      {:moderate_load, %{load_level: 0.5}},
      {:heavy_load, %{load_level: 0.8}},
      {:maximum_load, %{load_level: 1.0}}
    ]
    
    Enum.map(cpu_scenarios, fn {scenario_name, scenario_config} ->
      # Setup CPU monitoring
      cpu_monitor = start_cpu_monitoring()
      
      # Setup system under test
      system_setup = setup_cpu_test_scenario(scenario_config)
      
      # Run CPU load scenario
      load_results = run_cpu_load_scenario(system_setup, scenario_config.load_level, 60_000)  # 60 seconds
      
      # Stop monitoring and get results
      cpu_metrics = stop_cpu_monitoring(cpu_monitor)
      
      # Cleanup
      cleanup_cpu_test_scenario(system_setup)
      
      %{
        scenario: scenario_name,
        load_level: scenario_config.load_level,
        cpu_metrics: %{
          average_cpu_usage: cpu_metrics.average_usage,
          peak_cpu_usage: cpu_metrics.peak_usage,
          cpu_efficiency: cpu_metrics.efficiency_score,
          context_switches: cpu_metrics.context_switches,
          scheduler_utilization: cpu_metrics.scheduler_utilization
        },
        performance_under_load: load_results,
        resource_optimization: analyze_cpu_optimization_opportunities(cpu_metrics, load_results)
      }
    end)
  end
end

defmodule DSPEx.Performance.Monitoring do
  @moduledoc """
  Continuous performance monitoring and alerting system
  """
  
  use GenServer
  
  def start_link(opts) do
    GenServer.start_link(__MODULE__, opts, name: __MODULE__)
  end
  
  def init(opts) do
    monitoring_config = %{
      monitoring_interval: Keyword.get(opts, :monitoring_interval, 30_000),
      performance_thresholds: initialize_performance_thresholds(opts),
      baseline_metrics: load_baseline_metrics(),
      alert_handlers: initialize_alert_handlers(opts),
      metric_history: [],
      regression_detection: initialize_regression_detection(opts)
    }
    
    # Start periodic monitoring
    schedule_performance_monitoring(monitoring_config.monitoring_interval)
    
    {:ok, monitoring_config}
  end
  
  def handle_info(:perform_monitoring, state) do
    # Collect current performance metrics
    current_metrics = collect_performance_metrics()
    
    # Compare with baselines and thresholds
    performance_analysis = analyze_performance_metrics(current_metrics, state)
    
    # Check for regressions
    regression_analysis = detect_performance_regressions(current_metrics, state.metric_history)
    
    # Trigger alerts if necessary
    alert_results = process_performance_alerts(performance_analysis, regression_analysis, state)
    
    # Update metric history
    updated_history = update_metric_history(current_metrics, state.metric_history)
    
    # Generate performance reports
    generate_performance_report(current_metrics, performance_analysis, regression_analysis)
    
    # Schedule next monitoring cycle
    schedule_performance_monitoring(state.monitoring_interval)
    
    updated_state = %{state | 
      metric_history: updated_history,
      last_monitoring: DateTime.utc_now()
    }
    
    {:noreply, updated_state}
  end
  
  defp collect_performance_metrics do
    %{
      timestamp: DateTime.utc_now(),
      
      # System metrics
      system: %{
        memory_usage: get_system_memory_usage(),
        cpu_usage: get_system_cpu_usage(),
        disk_usage: get_system_disk_usage(),
        network_usage: get_system_network_usage()
      },
      
      # Agent metrics
      agents: %{
        total_agents: count_active_agents(),
        agent_performance: get_agent_performance_metrics(),
        coordination_metrics: get_coordination_metrics(),
        failure_rates: get_agent_failure_rates()
      },
      
      # Variable metrics
      variables: %{
        total_variables: count_active_variables(),
        adaptation_rates: get_variable_adaptation_rates(),
        coordination_success_rates: get_variable_coordination_success_rates(),
        performance_improvements: get_variable_performance_improvements()
      },
      
      # Clustering metrics
      clustering: %{
        discovery_performance: get_discovery_performance_metrics(),
        load_balancing_efficiency: get_load_balancing_metrics(),
        health_monitoring_accuracy: get_health_monitoring_metrics(),
        cluster_stability: get_cluster_stability_metrics()
      },
      
      # Bridge metrics
      bridge: %{
        translation_overhead: get_bridge_translation_overhead(),
        foundation_integration_health: get_foundation_integration_health(),
        coordination_bridge_performance: get_coordination_bridge_performance()
      },
      
      # End-to-end metrics
      workflows: %{
        ml_workflow_performance: get_ml_workflow_metrics(),
        optimization_effectiveness: get_optimization_effectiveness_metrics(),
        user_experience_metrics: get_user_experience_metrics()
      }
    }
  end
  
  defp analyze_performance_metrics(current_metrics, state) do
    baseline = state.baseline_metrics
    thresholds = state.performance_thresholds
    
    %{
      performance_comparison: compare_with_baseline(current_metrics, baseline),
      threshold_violations: check_threshold_violations(current_metrics, thresholds),
      trend_analysis: analyze_performance_trends(current_metrics, state.metric_history),
      efficiency_analysis: analyze_resource_efficiency(current_metrics),
      optimization_opportunities: identify_optimization_opportunities(current_metrics, baseline)
    }
  end
  
  defp detect_performance_regressions(current_metrics, metric_history) do
    if length(metric_history) >= 10 do
      # Statistical regression detection
      recent_history = Enum.take(metric_history, 10)
      
      %{
        statistical_regressions: detect_statistical_regressions(current_metrics, recent_history),
        trend_regressions: detect_trend_regressions(current_metrics, recent_history),
        threshold_regressions: detect_threshold_regressions(current_metrics, recent_history),
        severity_assessment: assess_regression_severity(current_metrics, recent_history)
      }
    else
      %{insufficient_history: true}
    end
  end
end

defmodule DSPEx.Performance.Alerting do
  @moduledoc """
  Performance alerting and automated response system
  """
  
  def process_performance_alerts(performance_analysis, regression_analysis, config) do
    alerts = []
    
    # Check for critical performance issues
    critical_alerts = check_critical_performance_alerts(performance_analysis)
    alerts = alerts ++ critical_alerts
    
    # Check for performance regressions
    regression_alerts = check_regression_alerts(regression_analysis)
    alerts = alerts ++ regression_alerts
    
    # Check for resource efficiency issues
    efficiency_alerts = check_efficiency_alerts(performance_analysis)
    alerts = alerts ++ efficiency_alerts
    
    # Process alerts based on severity
    Enum.each(alerts, fn alert ->
      case alert.severity do
        :critical ->
          handle_critical_alert(alert, config)
          
        :warning ->
          handle_warning_alert(alert, config)
          
        :info ->
          handle_info_alert(alert, config)
      end
    end)
    
    %{
      total_alerts: length(alerts),
      critical_alerts: count_alerts_by_severity(alerts, :critical),
      warning_alerts: count_alerts_by_severity(alerts, :warning),
      info_alerts: count_alerts_by_severity(alerts, :info),
      alert_details: alerts
    }
  end
  
  defp handle_critical_alert(alert, config) do
    # Immediate notification
    notify_alert_handlers(alert, config.alert_handlers)
    
    # Automated response if configured
    if alert.auto_response_enabled do
      execute_automated_response(alert)
    end
    
    # Escalation if needed
    if alert.escalation_required do
      escalate_alert(alert, config)
    end
    
    Logger.error("CRITICAL PERFORMANCE ALERT: #{alert.title} - #{alert.description}")
  end
  
  def generate_performance_report(metrics, analysis, regression_analysis) do
    report = %{
      timestamp: DateTime.utc_now(),
      summary: generate_performance_summary(metrics, analysis),
      detailed_metrics: metrics,
      performance_analysis: analysis,
      regression_analysis: regression_analysis,
      recommendations: generate_optimization_recommendations(analysis),
      trend_projections: generate_trend_projections(metrics, analysis)
    }
    
    # Store report for historical analysis
    store_performance_report(report)
    
    # Generate visualizations if configured
    if performance_visualization_enabled?() do
      generate_performance_visualizations(report)
    end
    
    report
  end
end

defmodule DSPEx.Performance.ValidationPipeline do
  @moduledoc """
  Automated performance validation pipeline for CI/CD integration
  """
  
  def run_performance_validation(validation_config) do
    validation_start_time = DateTime.utc_now()
    
    validation_results = %{
      infrastructure_benchmarks: run_infrastructure_benchmarks(validation_config),
      cognitive_variables_benchmarks: run_cognitive_variables_benchmarks(validation_config),
      clustering_benchmarks: run_clustering_benchmarks(validation_config),
      ml_workflow_benchmarks: run_ml_workflow_benchmarks(validation_config),
      scalability_tests: run_scalability_tests(validation_config),
      resource_efficiency_tests: run_resource_efficiency_tests(validation_config),
      regression_tests: run_regression_tests(validation_config)
    }
    
    validation_end_time = DateTime.utc_now()
    validation_duration = DateTime.diff(validation_end_time, validation_start_time, :millisecond)
    
    # Analyze validation results
    validation_analysis = analyze_validation_results(validation_results)
    
    # Generate validation report
    validation_report = generate_validation_report(validation_results, validation_analysis, validation_duration)
    
    # Determine overall validation status
    validation_status = determine_validation_status(validation_analysis)
    
    %{
      status: validation_status,
      duration: validation_duration,
      results: validation_results,
      analysis: validation_analysis,
      report: validation_report,
      recommendations: generate_validation_recommendations(validation_analysis)
    }
  end
  
  defp determine_validation_status(analysis) do
    critical_failures = count_critical_failures(analysis)
    warning_failures = count_warning_failures(analysis)
    
    cond do
      critical_failures > 0 ->
        :failed
        
      warning_failures > 5 ->
        :warning
        
      true ->
        :passed
    end
  end
  
  def generate_validation_recommendations(analysis) do
    recommendations = []
    
    # Performance improvement recommendations
    if analysis.performance_regressions > 0 do
      recommendations = recommendations ++ generate_performance_recommendations(analysis)
    end
    
    # Resource optimization recommendations
    if analysis.resource_efficiency < 0.8 do
      recommendations = recommendations ++ generate_resource_recommendations(analysis)
    end
    
    # Scalability recommendations
    if analysis.scalability_issues > 0 do
      recommendations = recommendations ++ generate_scalability_recommendations(analysis)
    end
    
    recommendations
  end
end

# .github/workflows/performance-testing.yml
name: Performance Testing and Validation

on:
  pull_request:
    branches: [ main, develop ]
  push:
    branches: [ main ]
  schedule:
    # Daily performance testing
    - cron: '0 2 * * *'

jobs:
  performance-testing:
    runs-on: ubuntu-latest
    
    services:
      postgres:
        image: postgres:13
        env:
          POSTGRES_PASSWORD: postgres
        options: >-
          --health-cmd pg_isready
          --health-interval 10s
          --health-timeout 5s
          --health-retries 5
          
    steps:
    - uses: actions/checkout@v3
    
    - name: Set up Elixir
      uses: erlef/setup-beam@v1
      with:
        elixir-version: '1.15.x'
        otp-version: '26.x'
        
    - name: Cache dependencies
      uses: actions/cache@v3
      with:
        path: |
          deps
          _build
        key: ${{ runner.os }}-mix-${{ hashFiles('**/mix.lock') }}
        restore-keys: |
          ${{ runner.os }}-mix-
          
    - name: Install dependencies
      run: mix deps.get
      
    - name: Compile project
      run: mix compile --warnings-as-errors
      
    - name: Run unit tests
      run: mix test
      
    - name: Run performance benchmarks
      run: |
        mix run -e "DSPEx.Performance.ValidationPipeline.run_ci_validation()"
        
    - name: Generate performance report
      run: |
        mix run -e "DSPEx.Performance.Reporting.generate_ci_report()"
        
    - name: Upload performance artifacts
      uses: actions/upload-artifact@v3
      with:
        name: performance-reports
        path: |
          performance_reports/
          benchmarks/
          
    - name: Performance regression check
      run: |
        mix run -e "DSPEx.Performance.RegressionCheck.validate_against_baseline()"
        
    - name: Comment performance results
      if: github.event_name == 'pull_request'
      uses: actions/github-script@v6
      with:
        script: |
          const fs = require('fs');
          const performanceReport = fs.readFileSync('performance_summary.md', 'utf8');
          github.rest.issues.createComment({
            issue_number: context.issue.number,
            owner: context.repo.owner,
            repo: context.repo.repo,
            body: performanceReport
          });