class AdaptiveInteractionLayer {
    constructor() {
        this.userProfilingService = new UserProfileManager();
        this.interfaceAdaptationEngine = new InterfaceAdaptationEngine();
    }
    
    renderUserInterface(userContext) {
        const userProfile = this.userProfilingService.determineExpertiseLevel(userContext);
        const interactionPattern = this.interfaceAdaptationEngine.selectOptimalPattern(userProfile);
        
        // For novice users: simplified controls with extensive guidance
        if (userProfile.level === 'novice') {
            return this.renderNoviceInterface({
                simplifiedControls: true,
                contextualHelp: true,
                guidedWorkflows: true,
                explicitConfirmation: true
            });
        }
        
        // For expert users: direct manipulation with advanced options
        if (userProfile.level === 'expert') {
            return this.renderExpertInterface({
                directManipulation: true,
                keyboardShortcuts: true,
                customizationOptions: true,
                minimalIntervention: true
            });
        }
    }
}

class CognitiveLoadOptimizer:
    def optimize_information_presentation(self, complex_data, user_context):
        # Chunk information appropriately
        information_chunks = self.segment_into_manageable_pieces(complex_data)
        
        # Prioritize based on user goals
        prioritized_chunks = self.rank_by_relevance(information_chunks, user_context.goals)
        
        # Sequence for optimal cognitive flow
        sequenced_chunks = self.arrange_for_progressive_disclosure(prioritized_chunks)
        
        # Format for easy processing
        formatted_chunks = self.apply_visual_coding_conventions(sequenced_chunks)
        
        return formatted_chunks
        
    def segment_into_manageable_pieces(self, data, max_chunk_size=7):
        """Apply Miller's Rule (7 ± 2 items maximum) to information chunks"""
        # Implementation details...
        pass

interface AgentReliabilityIndicator {
    confidenceScore: number;  // 0.0 - 1.0 scale
    historicalAccuracy: number;
    recentPerformance: PerformanceMetrics;
    domainExpertise: ExpertiseRating;
    uncertaintyFlags: string[];
}

class TrustCalibrationDisplay {
    render(agentState: AgentReliabilityIndicator, context: InteractionContext) {
        return `
        <div class="trust-indicator">
            <div class="confidence-meter" 
                 data-confidence="${agentState.confidenceScore}"
                 title="Agent's confidence in this recommendation">
                <span class="confidence-label">Confidence: ${Math.round(agentState.confidenceScore * 100)}%</span>
                <div class="confidence-bar">
                    <div class="fill" style="width: ${agentState.confidenceScore * 100}%"></div>
                </div>
            </div>
            
            <details class="reasoning-details">
                <summary>Why this recommendation?</summary>
                <div class="reasoning-chain">${this.renderReasoningChain(agentState.reasoning)}</div>
                <div class="evidence-sources">${this.renderSources(agentState.sources)}</div>
            </details>
            
            ${agentState.uncertaintyFlags.length ? 
              `<div class="uncertainty-warning">
                   <strong>Note:</strong> ${this.formatUncertaintyWarnings(agentState.uncertaintyFlags)}
               </div>` : ''}
        </div>`;
    }
}

public class InteractionGracefulDegradation {
    public InteractionResponse handleDegradedMode(UserRequest request, DegradationContext context) {
        switch(context.degradationType) {
            case NETWORK_UNAVAILABLE:
                return provideOfflineCapabilities(request);
                
            case PROCESSING_OVERLOADED:
                return queueRequestWithExpectedCompletionTime(request);
                
            case KNOWLEDGE_GAP:
                return suggestHumanEscalationWithPathways(request);
                
            case INTERFACE_UNSUPPORTED:
                return fallbackToAlternativeInputMethods(request);
                
            default:
                return apologeticRecoveryFlow(request);
        }
    }
    
    private InteractionResponse provideOfflineCapabilities(UserRequest request) {
        List<OfflineCapability> availableOffline = this.offlineCapabilities.getAvailableFor(request.type);
        return InteractionResponse.builder()
            .message("Limited connectivity detected. Available offline options:")
            .options(availableOffline.stream().map(this::formatOfflineOption).collect(Collectors.toList()))
            .build();
    }
}

Customer Service Dialog Flow Structure
┌─────────────────────────────────────────────────────────┐
│ Intent Identification                                   │
│   ├─ Problem Classification                            │
│   └─ Context Gathering                                 │
├─────────────────────────────────────────────────────────┤
│ Solution Exploration                                    │
│   ├─ Information Retrieval                             │
│   ├─ Recommendation Generation                         │
│   └─ Validation with User                              │
├─────────────────────────────────────────────────────────┤
│ Action Facilitation                                     │
│   ├─ Self-Service Capability                           │
│   ├─ Guided Execution                                  │
│   └─ Human Handoff Preparation                         │
├─────────────────────────────────────────────────────────┤
│ Outcome Confirmation & Feedback Collection              │
│   ├─ Satisfaction Assessment                           │
│   ├─ Learning Opportunity Capture                      │
│   └─ Follow-up Schedule Setting                        │
└─────────────────────────────────────────────────────────┘

class AdvancedLanguageProcessor:
    def parse_user_utterance(self, utterance, conversation_context):
        # Extract explicit semantic meaning
        explicit_intent = self.extract_intended_action(utterance)
        
        # Infer implicit intentions from conversational patterns
        implicit_intentions = self.infer_unstated_needs(conversation_context.history)
        
        # Resolve ambiguous references using context
        resolved_entities = self.resolve_coreferences(utterance, conversation_context.entities)
        
        # Detect emotional tone and adapt response accordingly
        emotional_tone = self.analyze_sentiment(utterance)
        
        # Combine for comprehensive understanding
        full_semantic_representation = SemanticRepresentation(
            explicit_intent=explicit_intent,
            implicit_intentions=implicit_intentions,
            resolved_entities=resolved_entities,
            emotional_context=emotional_tone,
            cultural_context=conversation_context.user_cultural_background
        )
        
        return full_semantic_representation

struct SpatialAgentInterface {
    var agentPresence: AgentEntity
    
    func renderInAugmentedReality(for userPerspective: ARCamera) -> ARVisualization {
        let agentPosition = calculateOptimalPresentationLocation(
            relativeTo: userPerspective.position,
            considering: interactionContext
        )
        
        let visualizationElements = [
            createStatusIndicator(at: agentPosition),
            generateActivityStreamOverlay(agentPosition),
            setupInteractionHotspots(around: agentPosition)
        ]
        
        return ARVisualization(elements: visualizationElements)
    }
    
    private func calculateOptimalPresentationLocation(relativeTo userPosition: Vector3, 
                                                     considering context: InteractionContext) -> Vector3 {
        // Position agent interface where it's visible but non-obtrusive
        // Account for user attention focus areas
        // Maintain appropriate social distance metaphors
    }
}

class AdaptiveModalitySelector {
    fun selectBestInteractionChannels(userContext: UserContext, 
                                    agentCapability: AgentCapability,
                                    environmentalConstraints: EnvironmentalConstraints): List<InteractionChannel> {
        
        val preferredChannels = mutableListOf<InteractionChannel>()
        
        // Audio-first when visually occupied
        if (userContext.visualAttentionConsumed) {
            preferredChannels.add(VoiceChannel(priority = HIGH))
            if (environmentAllowsAudio) {
                preferredChannels.add(SoundEffectChannel())
            }
        }
        
        // Visual-first when audio unavailable
        else if (!environmentAllowsAudio) {
            preferredChannels.add(GraphicalDisplayChannel(priority = HIGH))
            preferredChannels.add(HapticFeedbackChannel())
        }
        
        // Multi-channel when optimal conditions met
        else {
            preferredChannels.add(VoiceAndVisualIntegratedChannel(priority = HIGH))
            preferredChannels.add(GestureRecognitionChannel())
        }
        
        // Always include text backup for accessibility
        preferredChannels.add(TextualInterfaceChannel(priority = BACKUP))
        
        return preferredChannels.filter { channel -> 
            agentCapability.supports(channel) && 
            !environmentalConstraints.prevents(channel) 
        }
    }
}

<div class="agent-interface progressive-disclosure">
    <!-- Level 1: Overview -->
    <section class="overview-panel" data-disclosure-level="1">
        <h2>Agent Status</h2>
        <div class="status-indicators">
            <span class="health-status good">Operational</span>
            <span class="activity-level moderate">Active Tasks: 3</span>
        </div>
        <button onclick="showNextLevel(2)">Show Details</button>
    </section>
    
    <!-- Level 2: Detail View -->
    <section class="detail-panel hidden" data-disclosure-level="2">
        <h3>Current Activities</h3>
        <ul class="task-list">
            <li>Processing customer inquiry #12345</li>
            <li>Analyzing market trends</li>
            <li>Updating knowledge base</li>
        </ul>
        <div class="controls">
            <button onclick="showPreviousLevel(1)">Back to Overview</button>
            <button onclick="showNextLevel(3)">Advanced Settings</button>
        </div>
    </section>
    
    <!-- Level 3: Expert Configuration -->
    <section class="expert-panel hidden" data-disclosure-level="3">
        <h3>Configuration Parameters</h3>
        <form class="advanced-settings">
            <!-- ... detailed configuration options ... -->
        </form>
        <div class="controls">
            <button onclick="showPreviousLevel(2)">Back to Details</button>
        </div>
    </section>
</div>

/* Consistent error state styling */
.error-state {
    border-color: #ff4444;
    background-color: #ffeeee;
    padding: 1rem;
    border-radius: 4px;
    margin: 1rem 0;
}

.error-state .title {
    font-weight: bold;
    color: #cc0000;
    margin-bottom: 0.5rem;
}

.error-state .action-button {
    background-color: #ff4444;
    color: white;
    border: none;
    padding: 0.5rem 1rem;
    border-radius: 3px;
    cursor: pointer;
}

class FeedbackCollectionSystem {
    collectInteractionFeedback(interactionEvent) {
        const feedbackTypes = [
            new RatingScaleFeedback('ease_of_use', 'How easy was this interaction?'),
            new OpenTextFeedback('suggestions', 'What could be improved?'),
            new BehavioralFeedback('repeat_action', 'Would you do this again the same way?')
        ];
        
        return feedbackTypes.map(type => type.present(interactionEvent.context));
    }
    
    processCollectedFeedback(feedbackData) {
        // Aggregate across users and sessions
        const aggregatedInsights = this.analyzeFeedbackPatterns(feedbackData);
        
        // Trigger design improvements when thresholds met
        if (aggregatedInsights.indicatesSignificantIssue()) {
            this.submitImprovementRequest(aggregatedInsights);
        }
        
        // Update personalization models
        this.updateUserPreferenceModels(feedbackData.userId, feedbackData.responses);
    }
}

const AccessibleAgentInterface = ({ agentState, userPreferences }) => {
    const visualSettings = useVisualAccessibilitySettings(userPreferences);
    
    return (
        <div 
            className={`agent-interface ${visualSettings.theme}`}
            style={{
                fontSize: `${visualSettings.fontSize}em`,
                lineHeight: visualSettings.lineHeight
            }}
        >
            <AgentStatus 
                agent={agentState}
                accessibility={visualSettings.accessibilityFeatures}
                aria-label={`Agent status: ${agentState.statusText}`}
            />
            
            {visualSettings.screenReaderOptimized && (
                <ScreenReaderOnlyContent>
                    <p>Detailed agent status for screen readers: {agentState.detailedDescription}</p>
                </ScreenReaderOnlyContent>
            )}
            
            <AgentInteractionControls 
                agent={agentState}
                keyboardNavigationEnabled={userPreferences.keyboardNavigation}
            />
        </div>
    );
};

class ConsentManagementSystem:
    def request_significant_action_permission(self, action, user_context):
        risk_assessment = self.assess_action_risks(action, user_context)
        
        if risk_assessment.level == RiskLevel.HIGH:
            return self.present_detailed_consent_dialog(action, risk_assessment.details)
        elif risk_assessment.level == RiskLevel.MEDIUM:
            return self.present_simplified_consent_prompt(action)
        else:
            return self.proceed_with_implied_consent(action)
    
    def present_detailed_consent_dialog(self, action, risk_details):
        dialog_content = """
        <dialog id="action-consent-dialog">
            <h2>Important Action Request</h2>
            <p>This action will affect: {affected_parties}</p>
            <p>Potential impacts include: {potential_impacts}</p>
            <p>Reversible: {reversible}</p>
            
            <div class="consent-options">
                <button id="proceed-button" onclick="approveAction()">Proceed</button>
                <button id="modify-button" onclick="adjustParameters()">Modify First</button>
                <button id="cancel-button" onclick="cancelAction()">Cancel</button>
            </div>
        </dialog>
        """.format(**risk_details.__dict__)
        
        return dialog_content

class ExplanationGenerator
  def generate_human_readable_explanation(internal_reasoning)
    explanation = Explanation.new
    
    # Map technical concepts to everyday language
    explanation.primary_reason = translate_technical_concept(
      internal_reasoning.decision_factors.first.factor_type
    )
    
    explanation.supporting_evidence = internal_reasoning.evidence.map do |piece|
      EvidenceSummary.new(
        source: piece.origin.description,
        relevance: piece.weight,
        confidence: piece.certainty_level
      )
    end
    
    explanation.uncertainty_elements = identify_ambiguous_factors(
      internal_reasoning.uncertain_inputs
    ).map(&:natural_language_summary)
    
    explanation
  end
  
  private
  
  def translate_technical_concept(concept_symbol)
    translation_map = {
      :statistical_correlation => "Based on patterns I've seen before",
      :domain_expertise_match => "Drawing from specialized knowledge",
      :safety_protocol_match => "Following established safety guidelines",
      :user_preference_alignment => "Matching your stated preferences"
    }
    
    translation_map.fetch(concept_symbol, "Using my learned understanding")
  end
end

<div class="perspective-diversity-indicator">
    <h4>Sources Consulted</h4>
    <div class="source-types">
        <span class="academic-sources" title="Academic papers and research">{academic_count}</span>
        <span class="industry-sources" title="Industry reports and practices">{industry_count}</span>
        <span class="user-sources" title="Your preferences and history">{personal_count}</span>
        <span class="realtime-sources" title="Current events and data">{current_count}</span>
    </div>
    
    <div class="diversity-meter">
        <label>Diversity Score: {diversity_rating}/100</label>
        <progress value="{diversity_rating}" max="100"></progress>
        <small>{diversity_interpretation}</small>
    </div>
    
    {diversity_gaps_warning if diversity_rating < 70}
</div>

const AgentStatusComponent = ({ agent, onInteractionRequested }) => {
    const [expanded, setExpanded] = useState(false);
    const statusColor = getAgentStatusColor(agent.status);
    
    return (
        <div className="agent-status-card" style={{ borderColor: statusColor }}>
            <div className="status-header">
                <h3>{agent.name}</h3>
                <span className="status-badge" style={{ backgroundColor: statusColor }}>
                    {agent.statusText}
                </span>
            </div>
            
            <div className="status-body">
                {agent.currentTask && (
                    <div className="current-task">
                        <label>Currently Working On:</label>
                        <p>{agent.currentTask.description}</p>
                        <ProgressBar 
                            progress={agent.currentTask.progress} 
                            estimatedCompletion={agent.currentTask.estimatedCompletion}
                        />
                    </div>
                )}
                
                <div className="quick-actions">
                    <button onClick={() => onInteractionRequested('chat')}>
                        Chat with Agent
                    </button>
                    <button onClick={() => onInteractionRequested('status')}>
                        View Detailed Status
                    </button>
                    <button onClick={() => setExpanded(!expanded)}>
                        {expanded ? 'Show Less' : 'Show More'}
                    </button>
                </div>
                
                {expanded && (
                    <div className="expanded-details">
                        <h4>Technical Details</h4>
                        <ul>
                            <li>Model Version: {agent.modelVersion}</li>
                            <li>Last Updated: {agent.lastUpdated.toLocaleString()}</li>
                            <li>Uptime: {agent.uptime}</li>
                            <li>Tasks Completed: {agent.tasksCompleted}</li>
                        </ul>
                    </div>
                )}
            </div>
        </div>
    );
};

// store/modules/agentChat.js
export default {
    namespaced: true,
    
    state: {
        activeConversations: {},
        conversationHistory: {},
        typingIndicators: {},
        unreadMessageCounts: {}
    },
    
    mutations: {
        START_CONVERSATION(state, { agentId, initialContext }) {
            Vue.set(state.activeConversations, agentId, {
                id: agentId,
                participants: ['user', agentId],
                createdAt: Date.now(),
                context: initialContext,
                status: 'active'
            });
        },
        
        ADD_MESSAGE(state, { agentId, message }) {
            if (!state.conversationHistory[agentId]) {
                Vue.set(state.conversationHistory, agentId, []);
            }
            
            state.conversationHistory[agentId].push({
                ...message,
                timestamp: Date.now()
            });
        },
        
        SET_TYPING_INDICATOR(state, { agentId, isTyping }) {
            Vue.set(state.typingIndicators, agentId, isTyping);
        },
        
        INCREMENT_UNREAD_COUNT(state, { agentId }) {
            const currentCount = state.unreadMessageCounts[agentId] || 0;
            Vue.set(state.unreadMessageCounts, agentId, currentCount + 1);
        }
    },
    
    actions: {
        async sendMessage({ commit, state }, { agentId, content }) {
            commit('ADD_MESSAGE', {
                agentId,
                message: {
                    sender: 'user',
                    content,
                    type: 'text'
                }
            });
            
            commit('SET_TYPING_INDICATOR', { agentId, isTyping: true });
            
            try {
                const response = await agentApi.sendMessage(agentId, content);
                
                commit('ADD_MESSAGE', {
                    agentId,
                    message: {
                        sender: agentId,
                        content: response.content,
                        type: response.type,
                        metadata: response.metadata
                    }
                });
                
                commit('INCREMENT_UNREAD_COUNT', { agentId });
            } catch (error) {
                commit('ADD_MESSAGE', {
                    agentId,
                    message: {
                        sender: 'system',
                        content: 'Failed to send message. Please try again.',
                        type: 'error'
                    }
                });
            } finally {
                commit('SET_TYPING_INDICATOR', { agentId, isTyping: false });
            }
        }
    }
};

class RealTimeAgentInterface(ServerProtocol):
    def __init__(self, agent_manager):
        self.agent_manager = agent_manager
        self.active_connections = {}
        
    async def handle_new_connection(self, websocket, path):
        connection_id = self.generate_unique_id()
        self.active_connections[connection_id] = websocket
        
        try:
            async for message in websocket:
                await self.process_client_message(connection_id, message)
        except WebSocketException as e:
            logger.warning(f"Connection {connection_id} closed unexpectedly: {e}")
        finally:
            del self.active_connections[connection_id]
            
    async def process_client_message(self, connection_id, message):
        parsed_message = json.loads(message)
        message_type = parsed_message.get('type')
        
        if message_type == 'agent_request':
            await self.handle_agent_request(connection_id, parsed_message['request'])
        elif message_type == 'interaction_update':
            await self.handle_interaction_update(parsed_message['update'])
        # Additional message types...
        
    async def handle_agent_request(self, connection_id, request):
        agent_id = request['target_agent']
        agent = self.agent_manager.get_agent(agent_id)
        
        if not agent:
            await self.send_error(connection_id, f"Agent {agent_id} not found")
            return
            
        # Process through agent with streaming response support
        async for response_chunk in agent.process_request_streaming(request):
            await self.push_to_client(connection_id, {
                'type': 'response_chunk',
                'chunk': response_chunk,
                'final': False
            })
            
        # Send completion signal
        await self.push_to_client(connection_id, {
            'type': 'response_complete',
            'final': True
        })

openapi: 3.0.0
info:
  title: Agent Interaction API
  version: 1.0.0

paths:
  /agents/{agentId}/interactions:
    post:
      summary: Initiate a new interaction session
      parameters:
        - name: agentId
          in: path
          required: true
          schema:
            type: string
      requestBody:
        required: true
        content:
          application/json:
            schema:
              type: object
              properties:
                interactionType:
                  type: string
                  enum: [chat, task_completion, decision_support]
                context:
                  type: object
                  description: Initial context for the interaction
              required:
                - interactionType
                
  /agents/{agentId}/sessions/{sessionId}/messages:
    post:
      summary: Send a message within an interaction session
      parameters:
        - name: agentId
          in: path
          required: true
          schema:
            type: string
        - name: sessionId
          in: path
          required: true
          schema:
            type: string
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/Message'
              
  /agents/{agentId}/sessions/{sessionId}/status:
    get:
      summary: Get current session status and agent state
      parameters:
        - name: agentId
          in: path
          required: true
          schema:
            type: string
        - name: sessionId
          in: path
          required: true
          schema:
            type: string
      responses:
        '200':
          description: Session status information
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/SessionStatus'

components:
  schemas:
    Message:
      type: object
      properties:
        sender:
          type: string
        content:
          type: string
        messageType:
          type: string
          enum: [text, command, file, multimedia]
        attachments:
          type: array
          items:
            $ref: '#/components/schemas/Attachment'
            
    SessionStatus:
      type: object
      properties:
        state:
          type: string
          enum: [active, paused, completed, errored]
        currentTask:
          $ref: '#/components/schemas/Task'
        interactionMetrics:
          $ref: '#/components/schemas/InteractionMetrics'
          
    InteractionMetrics:
      type: object
      properties:
        messageCount:
          type: integer
        responseTimeAverage:
          type: number
        userSatisfactionScore:
          type: number

class InteractionAnalyticsCollector:
    def collect_session_metrics(self, interaction_session):
        metrics = InteractionMetrics()
        
        # Efficiency measures
        metrics.task_completion_time = self.measure_task_duration(interaction_session)
        metrics.clicks_per_task = self.count_user_actions(interaction_session)
        metrics.navigation_depth = self.calculate_navigation_complexity(interaction_session)
        
        # Satisfaction measures
        metrics.user_rating = self.aggregate_user_feedback(interaction_session)
        metrics.task_success_rate = self.calculate_successful_completions(interaction_session)
        metrics.error_frequency = self.count_and_categorize_errors(interaction_session)
        
        # Engagement measures
        metrics_interaction_duration = self.measure_total_engagement_time(interaction_session)
        metrics_return_visits = self.track_session_continuation(interaction_session)
        metrics_feature_utilization = self.analyze_feature_adoption(interaction_session)
        
        return metrics
        
    def detect_usability_issues(self, metrics_collection):
        issues = []
        
        if metrics_collection.average_task_completion_time > threshold.slow_task_time:
            issues.append(Issue(
                type='efficiency',
                severity='high',
                description='Tasks taking longer than expected',
                affected_users=metrics_collection.get_users_with_slow_times()
            ))
            
        if metrics_collection.error_rate > threshold.acceptable_error_rate:
            issues.append(Issue(
                type='accessibility',
                severity='critical',
                description='Error rate exceeding usability standards',
                details=metrics_collection.get_common_error_patterns()
            ))
            
        return issues

-- Database schema for interaction experiment tracking
CREATE TABLE interaction_experiments (
    experiment_id UUID PRIMARY KEY,
    experiment_name VARCHAR(255),
    hypothesis TEXT,
    start_date TIMESTAMP,
    end_date TIMESTAMP,
    target_user_segments JSONB,
    status VARCHAR(50)
);

CREATE TABLE experiment_variants (
    variant_id UUID PRIMARY KEY,
    experiment_id UUID REFERENCES interaction_experiments(experiment_id),
    variant_name VARCHAR(100),
    configuration JSONB,
    allocation_percentage DECIMAL(5,2)
);

CREATE TABLE user_experiment_assignments (
    assignment_id UUID PRIMARY KEY,
    user_id UUID,
    experiment_id UUID REFERENCES interaction_experiments(experiment_id),
    variant_id UUID REFERENCES experiment_variants(variant_id),
    assignment_timestamp TIMESTAMP
);

CREATE TABLE interaction_outcomes (
    outcome_id UUID PRIMARY KEY,
    assignment_id UUID REFERENCES user_experiment_assignments(assignment_id),
    metric_name VARCHAR(100),
    metric_value NUMERIC,
    recorded_at TIMESTAMP
);

-- Query to analyze experimental results
SELECT 
    v.variant_name,
    COUNT(o.outcome_id) as sample_size,
    AVG(CASE WHEN o.metric_name = 'task_completion_success' THEN o.metric_value END) as success_rate,
    AVG(CASE WHEN o.metric_name = 'time_to_completion' THEN o.metric_value END) as avg_completion_time,
    AVG(CASE WHEN o.metric_name = 'user_satisfaction' THEN o.metric_value END) as satisfaction_score
FROM interaction_experiments e
JOIN experiment_variants v ON e.experiment_id = v.experiment_id
JOIN user_experiment_assignments ua ON v.variant_id = ua.variant_id
JOIN interaction_outcomes o ON ua.assignment_id = o.assignment_id
WHERE e.experiment_name = 'chat_interface_layout_comparison'
GROUP BY v.variant_name;

Customer Support Interaction Flow
┌─────────────────────────────────────────────────────┐
│ Initial Contact                                     │
├─────────────────────────────────────────────────────┤
│ Email │ Phone │ Chat │ Social Media │ Mobile App   │
├─────────────────────────────────────────────────────┤
│         ↓ Single Intelligent Support Agent ←        │
├─────────────────────────────────────────────────────┤
│  Problem Understanding and Context Reconstruction   │
├─────────────────────────────────────────────────────┤
│         Solution Generation and Validation          │
├─────────────────────────────────────────────────────┤
│   Multi-Modal Response Delivery (Channel-Aware)     │
├─────────────────────────────────────────────────────┤
│          Feedback Collection and Analysis           │
├─────────────────────────────────────────────────────┤
│        Knowledge Base Update and Improvement        │
└─────────────────────────────────────────────────────┘

public class UnifiedCustomerContext 
{
    public CustomerProfile Profile { get; set; }
    public InteractionHistory History { get; set; }
    public CurrentIssue Issue { get; set; }
    public PreferredChannels Channels { get; set; }
    public EmotionalState Emotion { get; set; }
    public ServiceLevelAgreement SLA { get; set; }
    
    public async Task<CustomerContext> BuildFromMultipleSources(CustomerId customerId)
    {
        var profile = await customerRepository.GetProfile(customerId);
        var history = await interactionRepository.GetRecentHistory(customerId, days: 30);
        var preferences = await preferenceRepository.GetChannelPreferences(customerId);
        var sentiment = await sentimentAnalyzer.ProcessHistory(history);
        
        return new CustomerContext
        {
            Profile = profile,
            History = history.OrderByDescending(i => i.Timestamp).ToList(),
            Channels = preferences,
            Emotion = sentiment.CurrentEmotionalState,
            SLA = DetermineSLABasedOnCustomerTier(profile.Tier)
        };
    }
}

struct CreativeCollaborationAgent {
    brainstorming_engine: BrainstormingEngine,
    inspiration_database: InspirationDatabase,
    constraint_analyzer: ConstraintAnalyzer,
    quality_evaluator: CreativityEvaluator,
}

impl CreativeCollaborationAgent {
    fn facilitate_brainstorming_session(&mut self, session_params: BrainstormingParams) -> BrainstormingSession {
        let mut session = BrainstormingSession::new(session_params.topic);
        
        // Establish creative constraints and freedoms
        let constraints = self.constraint_analyzer.identify_bounding_parameters(&session_params);
        session.with_constraints(constraints);
        
        // Generate diverse initial ideas
        let seed_ideas = self.brainstorming_engine.generate_initial_concepts(
            &session_params.topic,
            &constraints,
            session_params.diversity_level
        );
        session.add_ideas(seed_ideas);
        
        // Stimulate creative evolution through prompts
        for round in 0..session_params.iteration_count {
            let evolutionary_prompts = self.inspiration_database.retrieve_stimuli_for_round(round);
            let evolved_ideas = self.brainstorming_engine.evolve_concepts(
                session.current_ideas(),
                evolutionary_prompts,
                session_params.exploration_intensity
            );
            session.replace_ideas(evolved_ideas);
        }
        
        // Evaluate and rank resulting concepts
        let evaluations = self.quality_evaluator.assess_concepts(
            session.final_ideas(),
            session_params.evaluation_criteria
        );
        session.with_evaluations(evaluations);
        
        session
    }
}

type DecisionSupportOrchestrator struct {
    dataProviders map[string]DataProvider
    analysisEngines map[string]AnalysisEngine
    synthesisModules []SynthesisModule
}

func (dso *DecisionSupportOrchestrator) GenerateDecisionInsights(request DecisionRequest) (*DecisionInsights, error) {
    // Gather data from multiple heterogeneous sources
    rawData := make(map[string]interface{})
    for _, source := range request.RequiredDataSources {
        provider, exists := dso.dataProviders[source.Type]
        if !exists {
            return nil, fmt.Errorf("unsupported data source type: %s", source.Type)
        }
        
        data, err := provider.Fetch(source.Parameters)
        if err != nil {
            log.Printf("Warning: Failed to fetch data from %s: %v", source.Name, err)
            continue
        }
        
        rawData[source.Name] = data
    }
    
    // Perform parallel analysis on different dataset subsets
    analysisResults := make(chan AnalysisResult, len(dso.analysisEngines))
    var wg sync.WaitGroup
    
    for engineName, engine := range dso.analysisEngines {
        wg.Add(1)
        go func(name string, eng AnalysisEngine) {
            defer wg.Done()
            result, err := eng.Analyze(rawData, request.AnalysisParameters)
            if err != nil {
                log.Printf("Analysis engine %s failed: %v", name, err)
                return
            }
            analysisResults <- AnalysisResult{Name: name, Result: result}
        }(engineName, engine)
    }
    
    wg.Wait()
    close(analysisResults)
    
    // Synthesize findings into cohesive insights
    var results []AnalysisResult
    for result := range analysisResults {
        results = append(results, result)
    }
    
    synthesizedInsights := dso.synthesizeFindings(results, request.SynthesisParameters)
    
    return &DecisionInsights{
        RawData: rawData,
        AnalysisResults: results,
        SynthesizedInsights: synthesizedInsights,
        ConfidenceLevels: dso.assessConfidence(synthesizedInsights, rawData),
        KeyRecommendations: dso.extractRecommendations(synthesizedInsights),
    }, nil
}

Spatial Interaction Architecture
┌─────────────────────────────────────────────────────┐
│ Physical Environment                                │
│  ├─ User Position & Orientation                    │
│  ├─ Gestural Input Recognition                     │
│  └─ Environmental Context Awareness                │
├─────────────────────────────────────────────────────┤
│ Digital Overlay Layer                               │
│  ├─ Agent Presence Manifestation                   │
│  ├─ Information Visualization Projections          │
│  └─ Interactive Control Surfaces                   │
├─────────────────────────────────────────────────────┤
│ Cognitive Mediation Layer                           │
│  ├─ Attention Management                           │
│  ├─ Contextual Information Filtering               │
│  └─ Natural Interaction Pattern Recognition        │
├─────────────────────────────────────────────────────┤
│ Communication Protocol                              │
│  ├─ Multi-Modal Input Processing                   │
│  ├─ Real-Time Rendering Coordination               │
│  └─ Persistent State Management                    │
└─────────────────────────────────────────────────────┘

class SpatialInteractionController: ObservableObject {
    @Published var agentRepresentations: [AgentSpatialEntity] = []
    private let arSession: ARKitSession
    private let gestureRecognizer: SpatialGestureRecognizer
    
    func handleSpatialGesture(_ gesture: SpatialGesture) {
        guard let targetedAgent = identifyTargetedAgent(gesture.location) else {
            // Handle environmental interaction or create new agent placeholder
            handleEnvironmentalInteraction(gesture)
            return
        }
        
        switch gesture.type {
        case .tap:
            initiateAgentDialogue(targetedAgent)
            
        case .swipe(let direction):
            modifyAgentBehavior(targetedAgent, in: direction)
            
        case .pinch(let scale):
            adjustAgentInterfaceProminence(targetedAgent, by: scale)
            
        case .hold:
            showDetailedAgentInformation(targetedAgent)
        }
    }
    
    private func initiateAgentDialogue(_ agent: AgentSpatialEntity) {
        // Transition from spatial representation to conversational interface
        let dialogueManager = DialogueSessionManager(agentId: agent.id)
        dialogueManager.startSpatialConversation(at: agent.position)
    }
}

function cognitiveAdaptiveInterface(userDataStream, agentCapabilities)
    persistent cognitiveStateEstimator;
    persistent interfaceAdaptor;
    
    if isempty(cognitiveStateEstimator)
        cognitiveStateEstimator = initializeNeuralDecoder();
        interfaceAdaptor = createInterfaceMapper(agentCapabilities);
    end
    
    % Continuously decode cognitive states from neural signals
    cognitiveStates = cognitiveStateEstimator.decode(userDataStream);
    
    % Adapt interface behavior based on cognitive predictions
    if cognitiveStates.attentionLevel < 0.3  % Low attention
        interfaceAdaptor.reduceComplexity();
        interfaceAdaptor.activateGuidedMode();
    elseif cognitiveStates.stressLevel > 0.7  % High stress
        interfaceAdaptor.activateSoothingTheme();
        interfaceAdaptor.prioritizeSimpleTasks();
    elseif cognitiveStates.flowDetected  % Optimal engagement
        interfaceAdaptor.expandFunctionality();
        interfaceAdaptor.minimizeInterruptions();
    end
    
    % Update agent interaction strategy
    agentInteractionStrategy = interfaceAdaptor.getCurrentStrategy();
    updateAgentParameters(agentInteractionStrategy);
end

defmodule CollectiveDecisionPlatform do
  use GenServer
  
  defstruct participants: [],
            contributions: %{},
            consensus_metrics: %{},
            agent_facilitators: []
  
  def join_session(pid, participant_info) do
    GenServer.call(pid, {:join, participant_info})
  end
  
  def submit_contribution(pid, participant_id, contribution) do
    GenServer.cast(pid, {:contribute, participant_id, contribution})
  end
  
  def handle_call({:join, participant_info}, _from, state) do
    new_participant = Participant.new(participant_info)
    new_state = %{state | participants: [new_participant | state.participants]}
    
    # Notify agent facilitators of new participant
    Enum.each(state.agent_facilitators, fn facilitator ->
      AgentFacilitator.notify_participant_joined(facilitator, new_participant)
    end)
    
    {:reply, {:ok, new_participant.id}, new_state}
  end
  
  def handle_cast({:contribute, participant_id, contribution}, state) do
    updated_contributions = Map.put(
      state.contributions, 
      participant_id, 
      Contribution.anonymize(contribution)
    )
    
    # Trigger agent analysis of emerging patterns
    if ready_for_analysis?(updated_contributions, state.participants) do
      spawn(fn -> 
        analysis_result = ConsensusAnalyzer.analyze(updated_contributions)
        broadcast_analysis(analysis_result, state.participants)
      end)
    end
    
    {:noreply, %{state | contributions: updated_contributions}}
  end
end