defmodule Examples.HierarchicalMLConsensus do
  @moduledoc """
  Demonstrates complex hierarchical coordination for ML model selection.
  
  This example validates:
  - Multi-level coordination protocols
  - Byzantine fault tolerance under load
  - Weighted voting with dynamic expertise
  - Iterative refinement convergence
  - Cross-level result integration
  """
  
  alias Foundation.MABEAM.{Coordination, AgentRegistry, Types}
  
  def run_hierarchical_consensus() do
    IO.puts("🚀 Starting Hierarchical ML Consensus Example")
    
    # Phase 1: Setup agent hierarchy
    {:ok, agent_hierarchy} = setup_agent_hierarchy()
    
    # Phase 2: Level 1 - Byzantine consensus among domain experts
    {:ok, domain_consensus} = run_domain_expert_consensus(agent_hierarchy.domain_experts)
    
    # Phase 3: Level 2 - Weighted voting among model evaluators
    {:ok, evaluation_consensus} = run_model_evaluator_voting(
      agent_hierarchy.evaluators,
      domain_consensus
    )
    
    # Phase 4: Level 3 - Iterative refinement of criteria
    {:ok, final_criteria} = run_criteria_refinement(
      agent_hierarchy.refinement_team,
      evaluation_consensus
    )
    
    # Phase 5: Validate results and performance metrics
    validate_hierarchical_results(domain_consensus, evaluation_consensus, final_criteria)
  end
  
  defp setup_agent_hierarchy() do
    # Create 42 specialized agents across three tiers
    domain_experts = create_domain_expert_agents(7)
    evaluators = create_model_evaluator_agents(15) 
    refinement_team = create_refinement_agents(20)
    
    {:ok, %{
      domain_experts: domain_experts,
      evaluators: evaluators,
      refinement_team: refinement_team,
      total_agents: 42
    }}
  end
  
  defp run_domain_expert_consensus(experts) do
    # Byzantine consensus with injected Byzantine agents
    byzantine_agents = inject_byzantine_behavior(experts, 2)  # f=2 Byzantine agents
    
    proposal = %{
      model_categories: ["transformer", "cnn", "rnn", "hybrid"],
      evaluation_domains: ["accuracy", "latency", "memory", "robustness"],
      selection_criteria: %{
        min_accuracy: 0.85,
        max_latency_ms: 100,
        max_memory_mb: 512
      }
    }
    
    # Start Byzantine consensus with fault injection
    {:ok, session_id} = Coordination.start_byzantine_consensus(
      proposal,
      byzantine_agents,
      [
        fault_tolerance: 2,
        timeout_ms: 30_000,
        fault_injection: true  # Enable Byzantine behavior simulation
      ]
    )
    
    # Monitor consensus progress with detailed metrics
    {:ok, result} = monitor_consensus_progress(session_id, :byzantine)
    
    validate_byzantine_properties(result, experts)
    result
  end
end

defmodule Examples.AdaptiveCoordination do
  @moduledoc """
  Demonstrates adaptive coordination strategy selection.
  
  This example validates:
  - Dynamic algorithm switching under load
  - Real-time performance monitoring
  - Graceful degradation patterns
  - Quality vs. speed trade-offs
  - Automatic fault detection and response
  """
  
  def run_adaptive_scenario() do
    IO.puts("⚡ Starting Adaptive Coordination Example")
    
    # Setup dynamic agent environment
    {:ok, agent_pool} = setup_dynamic_environment(50)
    
    # Scenario 1: High-stakes decision requiring Byzantine consensus
    {:ok, critical_result} = coordinate_critical_decision(agent_pool)
    
    # Scenario 2: Agents start failing, adapt to weighted voting
    {:ok, adapted_result} = simulate_agent_failures_and_adapt(agent_pool)
    
    # Scenario 3: Time pressure forces simple majority voting
    {:ok, urgent_result} = coordinate_under_time_pressure(agent_pool)
    
    # Validate adaptation behavior
    validate_adaptive_behavior(critical_result, adapted_result, urgent_result)
  end
  
  defp coordinate_critical_decision(agent_pool) do
    # High-stakes scenario: Financial transaction approval
    proposal = %{
      transaction_amount: 1_000_000,
      risk_level: :high,
      approval_threshold: 0.95,
      required_consensus: :byzantine  # Must use Byzantine for security
    }
    
    coordination_spec = %{
      strategy: :adaptive,
      initial_algorithm: :byzantine_consensus,
      fallback_chain: [:weighted_consensus, :simple_majority],
      quality_requirements: %{
        min_consensus: 0.95,
        max_time_ms: 10_000,
        fault_tolerance: 3
      },
      adaptation_triggers: %{
        agent_failure_threshold: 0.1,  # 10% agent failure triggers adaptation
        time_pressure_threshold: 8_000, # 8 seconds triggers faster algorithm
        quality_drop_threshold: 0.9    # Quality below 90% triggers more robust algorithm
      }
    }
    
    {:ok, session_id} = Coordination.start_adaptive_coordination(
      proposal,
      agent_pool,
      coordination_spec
    )
    
    # Inject failures during execution to test adaptation
    spawn(fn -> inject_gradual_failures(agent_pool, session_id) end)
    
    {:ok, result} = Coordination.wait_for_adaptive_completion(session_id)
    
    # Validate that system adapted appropriately
    validate_adaptation_decisions(result, coordination_spec)
    result
  end
end

defmodule Examples.MassiveScaleTest do
  @moduledoc """
  Tests Foundation MABEAM coordination at extreme scale.
  
  This example validates:
  - Coordination with 500+ agents
  - Memory efficiency under load
  - Network partition handling
  - Performance degradation patterns
  - Resource cleanup effectiveness
  """
  
  def run_scale_test() do
    IO.puts("🌊 Starting Massive Scale Test")
    
    # Progressive scaling test
    scale_targets = [10, 25, 50, 100, 200, 500]
    
    results = for target <- scale_targets do
      IO.puts("📈 Testing coordination with #{target} agents...")
      
      start_time = System.monotonic_time(:millisecond)
      {:ok, result} = run_coordination_at_scale(target)
      end_time = System.monotonic_time(:millisecond)
      
      %{
        agent_count: target,
        duration_ms: end_time - start_time,
        memory_usage: get_memory_usage(),
        consensus_quality: result.consensus_quality,
        message_count: result.message_count,
        success: result.status == :completed
      }
    end
    
    analyze_scaling_patterns(results)
  end
  
  defp run_coordination_at_scale(agent_count) do
    # Create heterogeneous agent pool
    {:ok, agent_pool} = create_heterogeneous_agents(agent_count)
    
    # Complex proposal requiring significant coordination
    proposal = %{
      task: :distributed_ml_training,
      model_type: :large_transformer,
      training_params: %{
        batch_size: 64,
        learning_rate: 0.001,
        epochs: 100
      },
      resource_allocation: %{
        total_gpus: agent_count * 2,
        memory_per_agent: "8GB",
        network_bandwidth: "10Gbps"
      },
      constraints: %{
        max_training_time: 3600_000,  # 1 hour
        min_accuracy: 0.95,
        cost_budget: agent_count * 10
      }
    }
    
    # Use weighted consensus for scale efficiency
    {:ok, session_id} = Coordination.start_weighted_consensus(
      proposal,
      agent_pool,
      [
        timeout_ms: 60_000,
        consensus_threshold: 0.8,
        enable_early_termination: true,
        memory_optimization: true,
        batch_processing: true  # Process votes in batches for efficiency
      ]
    )
    
    # Monitor performance metrics during execution
    {:ok, performance_monitor} = start_performance_monitoring(session_id)
    
    {:ok, result} = Coordination.get_coordination_result(session_id)
    
    # Collect detailed performance data
    performance_data = collect_performance_data(performance_monitor)
    
    {:ok, Map.merge(result, performance_data)}
  end
  
  defp create_heterogeneous_agents(count) do
    # Create diverse agent types with realistic characteristics
    agent_types = [
      {:gpu_powerhouse, 0.2},      # 20% high-performance GPU agents
      {:cpu_optimizer, 0.3},       # 30% CPU-optimized agents  
      {:memory_specialist, 0.2},   # 20% high-memory agents
      {:network_hub, 0.1},         # 10% network-optimized agents
      {:general_purpose, 0.2}      # 20% balanced agents
    ]
    
    agents = for i <- 1..count do
      agent_type = select_weighted_random(agent_types)
      create_specialized_agent(:"agent_#{i}", agent_type)
    end
    
    {:ok, agents}
  end
end

defmodule Examples.PerformanceBenchmarks do
  @moduledoc """
  Comprehensive performance benchmarking for all coordination algorithms.
  
  This example validates:
  - Latency characteristics under load
  - Throughput limits for each algorithm  
  - Resource utilization patterns
  - Performance regression detection
  - Optimization effectiveness
  """
  
  @benchmark_scenarios [
    %{name: "Byzantine Consensus", algorithm: :byzantine, agents: [4, 7, 10, 13], iterations: 100},
    %{name: "Weighted Voting", algorithm: :weighted, agents: [5, 15, 25, 50], iterations: 200},
    %{name: "Iterative Refinement", algorithm: :iterative, agents: [3, 8, 12, 20], iterations: 50}
  ]
  
  def run_benchmark_suite() do
    IO.puts("🏃 Starting Performance Benchmark Suite")
    
    results = for scenario <- @benchmark_scenarios do
      IO.puts("📊 Benchmarking #{scenario.name}...")
      run_algorithm_benchmark(scenario)
    end
    
    generate_performance_report(results)
  end
  
  defp run_algorithm_benchmark(scenario) do
    benchmark_results = for agent_count <- scenario.agents do
      IO.puts("  Testing with #{agent_count} agents (#{scenario.iterations} iterations)...")
      
      # Run multiple iterations for statistical significance
      iteration_results = for _i <- 1..scenario.iterations do
        run_single_benchmark_iteration(scenario.algorithm, agent_count)
      end
      
      %{
        agent_count: agent_count,
        latency_p50: calculate_percentile(iteration_results, :latency, 50),
        latency_p95: calculate_percentile(iteration_results, :latency, 95),
        latency_p99: calculate_percentile(iteration_results, :latency, 99),
        memory_peak: Enum.max(Enum.map(iteration_results, & &1.memory_peak)),
        memory_avg: Enum.sum(Enum.map(iteration_results, & &1.memory_avg)) / scenario.iterations,
        cpu_avg: Enum.sum(Enum.map(iteration_results, & &1.cpu_usage)) / scenario.iterations,
        message_count: Enum.sum(Enum.map(iteration_results, & &1.message_count)) / scenario.iterations,
        success_rate: Enum.count(iteration_results, & &1.success) / scenario.iterations
      }
    end
    
    %{
      algorithm: scenario.algorithm,
      name: scenario.name,
      results: benchmark_results,
      performance_characteristics: analyze_performance_characteristics(benchmark_results)
    }
  end
  
  defp run_single_benchmark_iteration(algorithm, agent_count) do
    # Setup monitoring
    :observer_cli.start()
    memory_before = :erlang.memory(:total)
    start_time = System.monotonic_time(:microsecond)
    
    # Run coordination
    result = case algorithm do
      :byzantine -> 
        run_byzantine_benchmark(agent_count)
      :weighted -> 
        run_weighted_benchmark(agent_count)
      :iterative -> 
        run_iterative_benchmark(agent_count)
    end
    
    end_time = System.monotonic_time(:microsecond)
    memory_after = :erlang.memory(:total)
    
    %{
      latency: end_time - start_time,  # microseconds
      memory_peak: memory_after,
      memory_avg: (memory_before + memory_after) / 2,
      cpu_usage: get_cpu_usage(),
      message_count: result.message_count,
      success: result.status == :completed
    }
  end
end

defmodule Examples.ByzantineBehaviorSimulation do
  @moduledoc """
  Simulates realistic Byzantine agent behaviors to validate fault tolerance.
  
  This example validates:
  - Detection of various Byzantine attack patterns
  - System resilience to coordinated malicious behavior
  - Recovery mechanisms after Byzantine agent isolation
  - Safety guarantees under maximum fault conditions
  - Performance impact of Byzantine detection
  """
  
  @byzantine_attack_patterns [
    :silent_failure,           # Agent stops responding
    :conflicting_messages,     # Sends different messages to different agents
    :timing_attack,            # Delays messages strategically
    :invalid_signatures,       # Sends messages with invalid authentication
    :replay_attack,            # Replays old messages
    :coalition_attack,         # Coordinated attack with other Byzantine agents
    :adaptive_behavior         # Changes attack strategy based on detection
  ]
  
  def run_byzantine_simulation() do
    IO.puts("🎭 Starting Byzantine Behavior Simulation")
    
    # Test each attack pattern individually
    individual_results = for pattern <- @byzantine_attack_patterns do
      IO.puts("🔍 Testing #{pattern} attack pattern...")
      test_byzantine_pattern(pattern)
    end
    
    # Test coordinated attacks
    coordinated_results = test_coordinated_byzantine_attacks()
    
    # Test maximum fault tolerance (f = (n-1)/3)
    maximum_fault_results = test_maximum_fault_tolerance()
    
    compile_security_report(individual_results, coordinated_results, maximum_fault_results)
  end
  
  defp test_byzantine_pattern(pattern) do
    # Setup 10 agents with 3 Byzantine (maximum fault tolerance)
    honest_agents = create_honest_agents(7)
    byzantine_agents = create_byzantine_agents(3, pattern)
    all_agents = honest_agents ++ byzantine_agents
    
    proposal = %{
      decision: :critical_system_update,
      parameters: %{version: "2.1.0", rollback_plan: "automatic"},
      security_level: :maximum
    }
    
    start_time = System.monotonic_time(:millisecond)
    
    {:ok, session_id} = Coordination.start_byzantine_consensus(
      proposal,
      all_agents,
      [
        fault_tolerance: 3,
        timeout_ms: 30_000,
        byzantine_detection: true,
        security_monitoring: true
      ]
    )
    
    # Monitor Byzantine behavior detection
    {:ok, security_monitor} = start_security_monitoring(session_id)
    
    # Let Byzantine agents execute their attack strategy
    activate_byzantine_behavior(byzantine_agents, pattern, session_id)
    
    {:ok, result} = Coordination.get_coordination_result(session_id)
    end_time = System.monotonic_time(:millisecond)
    
    # Analyze security response
    security_report = analyze_security_response(security_monitor)
    
    %{
      attack_pattern: pattern,
      consensus_reached: result.status == :completed,
      detection_time: security_report.detection_time,
      false_positives: security_report.false_positives,
      false_negatives: security_report.false_negatives,
      system_recovery_time: security_report.recovery_time,
      total_duration: end_time - start_time,
      safety_maintained: validate_safety_properties(result),
      liveness_maintained: validate_liveness_properties(result, end_time - start_time)
    }
  end
  
  defp create_byzantine_agents(count, attack_pattern) do
    for i <- 1..count do
      agent_id = :"byzantine_#{i}"
      {:ok, _pid} = start_byzantine_agent(agent_id, attack_pattern)
      agent_id
    end
  end
  
  defp start_byzantine_agent(agent_id, attack_pattern) do
    GenServer.start_link(
      Examples.ByzantineAgent,
      %{agent_id: agent_id, attack_pattern: attack_pattern},
      name: agent_id
    )
  end
  
  defp activate_byzantine_behavior(byzantine_agents, pattern, session_id) do
    for agent_id <- byzantine_agents do
      GenServer.cast(agent_id, {:activate_attack, pattern, session_id})
    end
  end
end

defmodule Examples.ByzantineAgent do
  use GenServer
  require Logger
  
  def init(state) do
    {:ok, state}
  end
  
  def handle_cast({:activate_attack, pattern, session_id}, state) do
    spawn(fn -> execute_attack_pattern(pattern, session_id, state.agent_id) end)
    {:noreply, state}
  end
  
  defp execute_attack_pattern(:silent_failure, session_id, agent_id) do
    # Stop responding to coordination messages after initial participation
    Process.sleep(2000)  # Participate normally for 2 seconds
    Logger.info("Byzantine agent #{agent_id} going silent")
    # Agent becomes unresponsive
  end
  
  defp execute_attack_pattern(:conflicting_messages, session_id, agent_id) do
    # Send different prepare messages to different agents
    participants = get_session_participants(session_id)
    
    for {i, target_agent} <- Enum.with_index(participants) do
      conflicting_message = %{
        type: :prepare,
        proposal: "conflicting_proposal_#{i}",  # Different proposal to each agent
        from: agent_id,
        session_id: session_id
      }
      
      send_to_agent(target_agent, conflicting_message)
    end
  end
  
  defp execute_attack_pattern(:timing_attack, session_id, agent_id) do
    # Strategically delay messages to disrupt consensus timing
    participants = get_session_participants(session_id)
    
    # Send early messages to some agents, delayed to others
    {early_targets, late_targets} = Enum.split(participants, div(length(participants), 2))
    
    message = create_prepare_message(session_id, agent_id)
    
    # Send immediately to early targets
    for target <- early_targets do
      send_to_agent(target, message)
    end
    
    # Delay messages to late targets to create timing confusion
    spawn(fn ->
      Process.sleep(5000)  # 5 second delay
      for target <- late_targets do
        send_to_agent(target, message)
      end
    end)
  end
  
  defp execute_attack_pattern(:coalition_attack, session_id, agent_id) do
    # Coordinate with other Byzantine agents for sophisticated attack
    byzantine_coalition = get_byzantine_coalition(session_id)
    
    if agent_id == hd(byzantine_coalition) do
      # Leader Byzantine agent coordinates the attack
      coordinate_coalition_attack(session_id, byzantine_coalition)
    else
      # Follower Byzantine agent waits for coordination
      wait_for_coalition_commands(session_id, agent_id)
    end
  end
end

defmodule Examples.NetworkPartitionTesting do
  @moduledoc """
  Tests coordination behavior under various network partition scenarios.
  
  This example validates:
  - Behavior during network partitions
  - Recovery mechanisms after partition healing
  - Data consistency across partitions
  - Performance impact of partition detection
  - Graceful degradation strategies
  """
  
  def run_partition_testing() do
    IO.puts("🌐 Starting Network Partition Testing")
    
    # Test various partition scenarios
    partition_scenarios = [
      %{name: "Clean Split", type: :clean_split, agents: 12, partition_sizes: [6, 6]},
      %{name: "Minority Isolation", type: :minority_isolation, agents: 15, partition_sizes: [12, 3]},
      %{name: "Multiple Fragments", type: :fragmentation, agents: 18, partition_sizes: [8, 6, 4]},
      %{name: "Intermittent Connectivity", type: :intermittent, agents: 10, partition_sizes: [5, 5]}
    ]
    
    results = for scenario <- partition_scenarios do
      IO.puts("🔌 Testing #{scenario.name} scenario...")
      test_partition_scenario(scenario)
    end
    
    generate_partition_resilience_report(results)
  end
  
  defp test_partition_scenario(scenario) do
    # Setup agents in single network initially
    {:ok, all_agents} = setup_networked_agents(scenario.agents)
    
    # Start consensus process
    proposal = %{
      task: :distributed_computation,
      deadline: DateTime.add(DateTime.utc_now(), 60, :second),
      fault_tolerance: :network_partition_resilient
    }
    
    {:ok, session_id} = Coordination.start_byzantine_consensus(
      proposal,
      all_agents,
      [
        fault_tolerance: 2,
        partition_detection: true,
        partition_healing: true,
        timeout_ms: 45_000
      ]
    )
    
    # Allow normal operation for initial period
    Process.sleep(5000)
    
    # Create network partition
    partition_groups = create_network_partition(all_agents, scenario)
    partition_start_time = System.monotonic_time(:millisecond)
    
    # Monitor behavior during partition
    {:ok, partition_monitor} = monitor_partition_behavior(session_id, partition_groups)
    
    # Simulate partition duration
    partition_duration = simulate_partition_duration(scenario.type)
    Process.sleep(partition_duration)
    
    # Heal partition
    heal_network_partition(partition_groups)
    healing_start_time = System.monotonic_time(:millisecond)
    
    # Monitor recovery behavior
    {:ok, recovery_monitor} = monitor_recovery_behavior(session_id)
    
    # Wait for final consensus
    {:ok, result} = Coordination.get_coordination_result(session_id)
    completion_time = System.monotonic_time(:millisecond)
    
    analyze_partition_resilience(
      scenario,
      result,
      partition_monitor,
      recovery_monitor,
      %{
        partition_start: partition_start_time,
        healing_start: healing_start_time,
        completion: completion_time
      }
    )
  end
end

defmodule Examples.DistributedMLTraining do
  @moduledoc """
  Demonstrates coordination for distributed ML training scenarios.
  
  This example validates:
  - Resource allocation for heterogeneous compute agents
  - Hyperparameter optimization across multiple agents
  - Fault tolerance during long-running training
  - Dynamic load balancing and agent scaling
  - Cost optimization and budget management
  """
  
  def run_distributed_training_example() do
    IO.puts("🤖 Starting Distributed ML Training Coordination")
    
    # Phase 1: Resource Discovery and Agent Characterization
    {:ok, compute_agents} = discover_and_characterize_compute_agents()
    
    # Phase 2: Training Strategy Consensus
    {:ok, training_strategy} = coordinate_training_strategy(compute_agents)
    
    # Phase 3: Resource Allocation Optimization
    {:ok, resource_allocation} = optimize_resource_allocation(compute_agents, training_strategy)
    
    # Phase 4: Distributed Training Execution with Coordination
    {:ok, training_results} = execute_coordinated_training(compute_agents, resource_allocation)
    
    # Phase 5: Results Aggregation and Model Selection
    {:ok, final_model} = coordinate_model_selection(training_results)
    
    validate_distributed_training_results(final_model, training_results)
  end
  
  defp discover_and_characterize_compute_agents() do
    # Create realistic heterogeneous compute environment
    agent_specifications = [
      # High-end GPU servers
      %{type: :gpu_powerhouse, count: 4, spec: %{gpus: 8, memory: "80GB", cpu_cores: 64}},
      # Mid-range GPU workstations  
      %{type: :gpu_workstation, count: 8, spec: %{gpus: 2, memory: "16GB", cpu_cores: 16}},
      # CPU-only servers for preprocessing
      %{type: :cpu_server, count: 12, spec: %{gpus: 0, memory: "32GB", cpu_cores: 32}},
      # Edge devices for inference testing
      %{type: :edge_device, count: 20, spec: %{gpus: 0, memory: "4GB", cpu_cores: 4}}
    ]
    
    agents = for spec <- agent_specifications, i <- 1..spec.count do
      agent_id = :"#{spec.type}_#{i}"
      create_compute_agent(agent_id, spec.type, spec.spec)
    end
    
    # Characterize agents through benchmarking coordination
    {:ok, characterization_session} = Coordination.start_weighted_consensus(
      %{task: :agent_characterization, benchmark_suite: :ml_training},
      agents,
      [timeout_ms: 30_000, consensus_threshold: 0.9]
    )
    
    {:ok, characterization_results} = Coordination.get_coordination_result(characterization_session)
    
    # Enhance agents with characterization data
    characterized_agents = enhance_agents_with_benchmarks(agents, characterization_results)
    
    {:ok, characterized_agents}
  end
  
  defp coordinate_training_strategy(compute_agents) do
    # Use iterative refinement to develop optimal training strategy
    initial_strategy = %{
      model_architecture: :transformer,
      dataset: :large_language_corpus,
      training_approach: :data_parallel,
      optimization_algorithm: :adamw,
      learning_rate_schedule: :cosine_annealing
    }
    
    {:ok, strategy_session} = Coordination.start_iterative_consensus(
      initial_strategy,
      compute_agents,
      [
        max_rounds: 5,
        convergence_threshold: 0.85,
        timeout_ms: 120_000
      ]
    )
    
    # Simulate expert feedback during iterative refinement
    spawn(fn -> provide_expert_feedback(strategy_session, compute_agents) end)
    
    {:ok, refined_strategy} = Coordination.get_coordination_result(strategy_session)
    
    {:ok, refined_strategy}
  end
  
  defp optimize_resource_allocation(compute_agents, training_strategy) do
    # Use weighted voting with resource-based expertise scoring
    allocation_proposal = %{
      strategy: training_strategy,
      available_resources: summarize_available_resources(compute_agents),
      optimization_objectives: %{
        minimize_training_time: 0.4,
        minimize_cost: 0.3,
        maximize_fault_tolerance: 0.3
      }
    }
    
    # Weight agents based on their resource capacity and reliability
    expertise_weights = calculate_resource_expertise_weights(compute_agents)
    
    {:ok, allocation_session} = Coordination.start_weighted_consensus(
      allocation_proposal,
      compute_agents,
      [
        timeout_ms: 60_000,
        consensus_threshold: 0.8,
        initial_weights: expertise_weights,
        dynamic_weight_updates: true
      ]
    )
    
    {:ok, allocation_result} = Coordination.get_coordination_result(allocation_session)
    
    {:ok, allocation_result}
  end
end

defmodule Examples.LLMEnsembleReasoning do
  @moduledoc """
  Demonstrates coordination of multiple LLM agents for complex reasoning.
  
  This example validates:
  - Chain-of-thought coordination across specialized LLMs
  - Reasoning consensus and quality assessment
  - Multi-modal reasoning integration
  - Adaptive reasoning strategy selection
  - Real-time reasoning quality monitoring
  """
  
  def run_ensemble_reasoning_example() do
    IO.puts("🧠 Starting LLM Ensemble Reasoning Coordination")
    
    # Phase 1: Setup Specialized LLM Agents
    {:ok, llm_ensemble} = setup_specialized_llm_agents()
    
    # Phase 2: Complex Reasoning Task Coordination
    {:ok, reasoning_results} = coordinate_complex_reasoning_task(llm_ensemble)
    
    # Phase 3: Reasoning Quality Assessment and Selection
    {:ok, best_reasoning} = assess_and_select_reasoning(reasoning_results)
    
    # Phase 4: Multi-Modal Integration
    {:ok, enhanced_reasoning} = integrate_multimodal_insights(best_reasoning, llm_ensemble)
    
    validate_reasoning_quality(enhanced_reasoning)
  end
  
  defp setup_specialized_llm_agents() do
    # Create diverse LLM agents with different specializations
    llm_specifications = [
      # Mathematical reasoning specialists
      %{type: :math_specialist, count: 3, model: "gpt-4-math", specialization: :mathematical_reasoning},
      # Logical reasoning specialists  
      %{type: :logic_specialist, count: 3, model: "claude-logic", specialization: :logical_reasoning},
      # Creative reasoning specialists
      %{type: :creative_specialist, count: 2, model: "gpt-4-creative", specialization: :creative_reasoning},
      # Scientific reasoning specialists
      %{type: :science_specialist, count: 3, model: "llama-science", specialization: :scientific_reasoning},
      # Common sense reasoning specialists
      %{type: :commonsense_specialist, count: 2, model: "claude-commonsense", specialization: :commonsense_reasoning}
    ]
    
    agents = for spec <- llm_specifications, i <- 1..spec.count do
      agent_id = :"#{spec.type}_#{i}"
      create_llm_agent(agent_id, spec)
    end
    
    # Test and characterize each agent's reasoning capabilities
    {:ok, characterization_results} = characterize_llm_reasoning_abilities(agents)
    
    # Create specialized agent groups
    ensemble = %{
      math_agents: filter_agents_by_type(agents, :math_specialist),
      logic_agents: filter_agents_by_type(agents, :logic_specialist),
      creative_agents: filter_agents_by_type(agents, :creative_specialist),
      science_agents: filter_agents_by_type(agents, :science_specialist),
      commonsense_agents: filter_agents_by_type(agents, :commonsense_specialist),
      all_agents: agents,
      characterization: characterization_results
    }
    
    {:ok, ensemble}
  end
  
  defp coordinate_complex_reasoning_task(llm_ensemble) do
    # Complex multi-step reasoning problem requiring diverse expertise
    reasoning_task = %{
      problem: """
      A research laboratory is designing a sustainable energy system for a remote island community.
      The system must:
      1. Provide reliable 24/7 power for 500 residents
      2. Use only renewable energy sources
      3. Operate within a $2M budget
      4. Be maintainable by local technicians
      5. Survive extreme weather events (hurricanes, typhoons)
      
      Design an optimal solution considering technical feasibility, environmental impact,
      economic constraints, and social acceptance.
      """,
      reasoning_requirements: %{
        mathematical_analysis: :required,    # Power calculations, cost analysis
        logical_reasoning: :required,        # System design logic
        creative_problem_solving: :helpful,  # Innovative solutions
        scientific_analysis: :required,      # Technical feasibility
        practical_considerations: :required  # Real-world constraints
      },
      output_format: :structured_solution,
      quality_threshold: 0.9
    }
    
    # Phase 1: Individual Reasoning by Specialized Agents
    individual_results = coordinate_individual_reasoning(llm_ensemble, reasoning_task)
    
    # Phase 2: Cross-Pollination of Ideas
    cross_pollinated_results = coordinate_idea_cross_pollination(llm_ensemble, individual_results)
    
    # Phase 3: Consensus Building
    consensus_results = coordinate_reasoning_consensus(llm_ensemble, cross_pollinated_results)
    
    {:ok, %{
      individual: individual_results,
      cross_pollinated: cross_pollinated_results,
      consensus: consensus_results
    }}
  end
  
  defp coordinate_individual_reasoning(llm_ensemble, reasoning_task) do
    # Each agent group works on their specialized aspect
    reasoning_assignments = %{
      math_agents: extract_mathematical_aspects(reasoning_task),
      logic_agents: extract_logical_aspects(reasoning_task),
      creative_agents: extract_creative_aspects(reasoning_task),
      science_agents: extract_scientific_aspects(reasoning_task),
      commonsense_agents: extract_practical_aspects(reasoning_task)
    }
    
    # Coordinate parallel reasoning sessions
    results = for {agent_group, task_aspect} <- reasoning_assignments do
      agents = Map.get(llm_ensemble, agent_group)
      
      {:ok, session_id} = Coordination.start_weighted_consensus(
        task_aspect,
        agents,
        [
          timeout_ms: 180_000,  # 3 minutes for deep reasoning
          consensus_threshold: 0.7,
          reasoning_mode: :chain_of_thought,
          quality_assessment: true
        ]
      )
      
      {:ok, result} = Coordination.get_coordination_result(session_id)
      {agent_group, result}
    end
    
    Map.new(results)
  end
end

defmodule Examples.ValidationFramework do
  @moduledoc """
  Automated validation framework for all coordination examples.
  
  This framework provides:
  - Automated example execution and validation
  - Performance regression detection
  - Quality metrics collection and analysis
  - Continuous integration test suite
  - Production readiness assessment
  """
  
  def run_full_validation_suite() do
    IO.puts("🔬 Starting Full Validation Suite")
    
    # Execute all example categories
    results = %{
      multi_algorithm: run_multi_algorithm_examples(),
      scale_performance: run_scale_performance_examples(),
      fault_tolerance: run_fault_tolerance_examples(),
      ml_scenarios: run_ml_scenario_examples(),
      integration: run_integration_examples()
    }
    
    # Generate comprehensive validation report
    generate_validation_report(results)
    
    # Check production readiness criteria
    assess_production_readiness(results)
  end
  
  defp assess_production_readiness(results) do
    criteria = %{
      byzantine_fault_tolerance: check_byzantine_guarantees(results),
      performance_requirements: check_performance_requirements(results),
      scalability_limits: check_scalability_limits(results),
      resource_efficiency: check_resource_efficiency(results),
      integration_stability: check_integration_stability(results),
      code_quality: check_code_quality_metrics(),
      documentation_completeness: check_documentation_completeness()
    }
    
    overall_readiness = calculate_overall_readiness(criteria)
    
    %{
      production_ready: overall_readiness >= 0.95,
      readiness_score: overall_readiness,
      criteria: criteria,
      recommendations: generate_improvement_recommendations(criteria)
    }
  end
end