Agent Interface Architecture

use std::collections::HashMap;
use tokio::time::{timeout, Duration};
use anyhow::{Result, anyhow};
 
pub struct AgentCommunicationProtocol {
    protocol_version: String,
    message_types: HashMap<String, Box<dyn MessageHandler>>,
    routing_table: RoutingTable,
}
 
impl AgentCommunicationProtocol {
    pub fn new() -> Self {
        Self {
            protocol_version: "2.0".to_string(),
            message_types: Self::setup_message_types(),
            routing_table: RoutingTable::new(),
        }
    }
    
    fn setup_message_types() -> HashMap<String, Box<dyn MessageHandler>> {
        let mut handlers = HashMap::new();
        handlers.insert("task_delegation".to_string(), Box::new(TaskDelegationHandler::new()));
        handlers.insert("status_update".to_string(), Box::new(StatusUpdateHandler::new()));
        handlers.insert("knowledge_sharing".to_string(), Box::new(KnowledgeShareHandler::new()));
        handlers.insert("coordination".to_string(), Box::new(CoordinationHandler::new()));
        handlers.insert("collaboration_request".to_string(), Box::new(CollaborationHandler::new()));
        handlers.insert("capability_inquiry".to_string(), Box::new(CapabilityInquiryHandler::new()));
        handlers.insert("resource_request".to_string(), Box::new(ResourceRequestHandler::new()));
        handlers.insert("emergency_signal".to_string(), Box::new(EmergencySignalHandler::new()));
        handlers
    }
    
    pub async fn send_message(&self, message: AgentMessage, target_agent: &str) -> Result<MessageResponse> {
        // Validate message
        let validation_result = self.validate_message(&message).await?;
        if !validation_result.is_valid {
            return Err(anyhow!("Invalid message: {:?}", validation_result.errors));
        }
        
        // Route message
        let route = self.routing_table.find_route(target_agent).await
            .ok_or_else(|| anyhow!("No route to agent {}", target_agent))?;
        
        // Encrypt message
        let encrypted_message = self.encrypt_message(&message, &route.encryption_key).await?;
        
        // Send with retry logic
        let response = self.send_with_retry(encrypted_message, route).await?;
        
        Ok(response)
    }
}

agent_message_schema:
  header:
    message_id: "uuid"
    message_type: "task_delegation|status_update|knowledge_sharing|..."
    sender_id: "agent_identifier"
    recipient_id: "agent_identifier"
    timestamp: "iso8601_datetime"
    priority: "low|normal|high|critical"
    encryption_method: "aes256|rsa2048|..."
    
  body:
    content: "message_content"
    attachments: ["optional_file_references"]
    context: "execution_context"
    metadata: "additional_metadata"
    
  routing:
    ttl: "time_to_live_seconds"
    max_hops: "maximum_routing_hops"
    delivery_receipt: "boolean"
    response_required: "boolean"

use std::collections::HashMap;
use anyhow::{Result, anyhow};
 
pub struct CollaborationManager {
    collaboration_patterns: HashMap<String, Box<dyn CollaborationPattern>>,
}
 
impl CollaborationManager {
    pub fn new() -> Self {
        let mut patterns = HashMap::new();
        patterns.insert("master_worker".to_string(), Box::new(MasterWorkerPattern::new()));
        patterns.insert("peer_to_peer".to_string(), Box::new(PeerToPeerPattern::new()));
        patterns.insert("pipeline".to_string(), Box::new(PipelinePattern::new()));
        patterns.insert("consensus".to_string(), Box::new(ConsensusPattern::new()));
        patterns.insert("auction".to_string(), Box::new(AuctionPattern::new()));
        
        Self {
            collaboration_patterns: patterns,
        }
    }
    
    pub async fn initiate_collaboration(
        &self, 
        pattern: &str, 
        participants: Vec<String>, 
        task: Task
    ) -> Result<Collaboration> {
        let pattern_impl = self.collaboration_patterns.get(pattern)
            .ok_or_else(|| anyhow!("Unsupported pattern: {}", pattern))?;
        
        // Setup collaboration
        let mut collaboration = pattern_impl.setup(participants.clone(), task).await?;
        
        // Establish communication channels
        let channels = self.setup_channels(participants, &collaboration.protocol).await?;
        collaboration.channels = channels;
        
        // Start coordination
        pattern_impl.start_coordination(&collaboration).await?;
        
        Ok(collaboration)
    }
}

use anyhow::Result;
use serde::{Serialize, Deserialize};
 
pub struct ConversationManager {
    nlp_engine: NLPEngine,
    context_tracker: ConversationContextTracker,
    personality_engine: PersonalityEngine,
}
 
impl ConversationManager {
    pub fn new() -> Self {
        Self {
            nlp_engine: NLPEngine::new(),
            context_tracker: ConversationContextTracker::new(),
            personality_engine: PersonalityEngine::new(),
        }
    }
    
    pub async fn process_human_message(
        &self, 
        message: HumanMessage, 
        conversation_id: &str
    ) -> Result<AgentResponse> {
        // Update conversation context
        let mut conversation = self.context_tracker.get_conversation(conversation_id).await?;
        conversation = self.context_tracker.update_context(conversation, &message).await?;
        
        // Process natural language
        let nlp_result = self.nlp_engine.process(&message.text, &conversation.context).await?;
        
        // Extract intent and entities
        let intent = nlp_result.intent.clone();
        let entities = nlp_result.entities.clone();
        let sentiment = nlp_result.sentiment.clone();
        
        // Generate contextual response
        let response_content = self.generate_response(&intent, &entities, &conversation).await?;
        
        // Apply personality
        let personalized_response = self.personality_engine.apply_personality(
            &response_content,
            &conversation.user_profile,
            &conversation.interaction_style
        ).await?;
        
        Ok(AgentResponse {
            conversation_id: conversation_id.to_string(),
            response_text: personalized_response.text,
            response_type: personalized_response.response_type,
            confidence: nlp_result.confidence,
            suggested_actions: personalized_response.actions,
            context_update: conversation.context,
        })
    }
}

class NLPEngine:
    def __init__(self):
        self.intent_classifier = IntentClassifier()
        self.entity_extractor = EntityExtractor()
        self.sentiment_analyzer = SentimentAnalyzer()
        self.context_resolver = ContextResolver()
        
    async def process(self, text: str, context: ConversationContext) -> NLPResult:
        # Preprocess text
        preprocessed = await self.preprocess_text(text)
        
        # Intent classification
        intent = await self.intent_classifier.classify(preprocessed, context)
        
        # Entity extraction
        entities = await self.entity_extractor.extract(preprocessed, context)
        
        # Sentiment analysis
        sentiment = await self.sentiment_analyzer.analyze(preprocessed, context)
        
        # Context resolution
        resolved_context = await self.context_resolver.resolve(
            text, entities, context
        )
        
        return NLPResult(
            original_text=text,
            preprocessed_text=preprocessed,
            intent=intent,
            entities=entities,
            sentiment=sentiment,
            resolved_context=resolved_context,
            confidence=self.calculate_confidence(intent, entities, sentiment)
        )

class ResponseGenerator:
    def __init__(self):
        self.templates = ResponseTemplateManager()
        self.nlg_engine = NaturalLanguageGeneration()
        self.personalization = PersonalizationEngine()
        
    async def generate_response(self, intent: Intent, entities: List[Entity], context: ConversationContext) -> Response:
        # Select response strategy
        strategy = self.select_response_strategy(intent, context)
        
        if strategy == "template_based":
            response = await self.generate_template_response(intent, entities, context)
        elif strategy == "generative":
            response = await self.generate_nlg_response(intent, entities, context)
        elif strategy == "hybrid":
            response = await self.generate_hybrid_response(intent, entities, context)
        else:
            response = await self.generate_fallback_response(context)
            
        # Personalize response
        personalized = await self.personalization.personalize(response, context.user_profile)
        
        return personalized

class ModalityManager:
    def __init__(self):
        self.modalities = {
            "text": TextModalityHandler(),
            "voice": VoiceModalityHandler(),
            "visual": VisualModalityHandler(),
            "gesture": GestureModalityHandler(),
            "haptic": HapticModalityHandler()
        }
        self.fusion_engine = ModalityFusionEngine()
        
    async def process_multimodal_input(self, input_data: MultiModalInput) -> FusedInput:
        processed_modalities = {}
        
        # Process each modality
        for modality, data in input_data.modalities.items():
            if modality in self.modalities:
                handler = self.modalities[modality]
                processed = await handler.process(data, input_data.context)
                processed_modalities[modality] = processed
                
        # Fuse modalities
        fused_input = await self.fusion_engine.fuse(
            processed_modalities, 
            input_data.context
        )
        
        return fused_input
        
    async def generate_multimodal_response(self, response_intent: ResponseIntent, target_modalities: List[str]) -> MultiModalResponse:
        generated_modalities = {}
        
        for modality in target_modalities:
            if modality in self.modalities:
                handler = self.modalities[modality]
                generated = await handler.generate(response_intent)
                generated_modalities[modality] = generated
                
        # Ensure modality coherence
        coherent_response = await self.ensure_coherence(generated_modalities)
        
        return MultiModalResponse(
            modalities=coherent_response,
            primary_modality=self.determine_primary_modality(target_modalities),
            fallback_modality="text"
        )

class VoiceModalityHandler:
    def __init__(self):
        self.asr_engine = AutomaticSpeechRecognition()
        self.tts_engine = TextToSpeechEngine()
        self.voice_activity_detector = VoiceActivityDetector()
        self.speaker_recognition = SpeakerRecognition()
        
    async def process(self, audio_data: AudioData, context: Context) -> VoiceProcessingResult:
        # Detect voice activity
        vad_result = await self.voice_activity_detector.detect(audio_data)
        if not vad_result.has_speech:
            return VoiceProcessingResult(has_speech=False)
            
        # Perform speech recognition
        asr_result = await self.asr_engine.recognize(audio_data, context.language)
        
        # Identify speaker (if enabled)
        speaker_id = None
        if context.speaker_identification_enabled:
            speaker_result = await self.speaker_recognition.identify(audio_data)
            speaker_id = speaker_result.speaker_id
            
        # Extract voice characteristics
        voice_characteristics = await self.extract_voice_characteristics(audio_data)
        
        return VoiceProcessingResult(
            has_speech=True,
            transcribed_text=asr_result.text,
            confidence=asr_result.confidence,
            speaker_id=speaker_id,
            voice_characteristics=voice_characteristics,
            language=asr_result.detected_language
        )
        
    async def generate(self, response_intent: ResponseIntent) -> AudioResponse:
        # Generate speech from text
        audio_data = await self.tts_engine.synthesize(
            text=response_intent.text,
            voice_profile=response_intent.voice_settings,
            emotion=response_intent.emotion,
            speaking_rate=response_intent.speaking_rate
        )
        
        return AudioResponse(
            audio_data=audio_data,
            duration=audio_data.duration,
            format=audio_data.format,
            sample_rate=audio_data.sample_rate
        )

class VisualModalityHandler:
    def __init__(self):
        self.image_analyzer = ImageAnalyzer()
        self.video_processor = VideoProcessor()
        self.gesture_recognizer = GestureRecognizer()
        self.ocr_engine = OCREngine()
        
    async def process(self, visual_data: VisualData, context: Context) -> VisualProcessingResult:
        if visual_data.type == "image":
            return await self.process_image(visual_data.data, context)
        elif visual_data.type == "video":
            return await self.process_video(visual_data.data, context)
        else:
            raise UnsupportedVisualDataType(visual_data.type)
            
    async def process_image(self, image_data: ImageData, context: Context) -> ImageProcessingResult:
        # Object detection and recognition
        objects = await self.image_analyzer.detect_objects(image_data)
        
        # Text extraction (OCR)
        text_elements = await self.ocr_engine.extract_text(image_data)
        
        # Scene understanding
        scene_description = await self.image_analyzer.describe_scene(image_data)
        
        # Gesture recognition (if applicable)
        gestures = await self.gesture_recognizer.recognize_gestures(image_data)
        
        return ImageProcessingResult(
            objects=objects,
            text_elements=text_elements,
            scene_description=scene_description,
            gestures=gestures,
            metadata=image_data.metadata
        )

class ConversationContextTracker:
    def __init__(self):
        self.memory_integration = MemoryIntegration()
        self.context_graph = ConversationContextGraph()
        
    async def track_conversation_state(self, conversation_id: str, message: Message) -> ConversationState:
        # Get current conversation state
        current_state = await self.get_conversation_state(conversation_id)
        
        # Update with new message
        updated_state = await self.update_state_with_message(current_state, message)
        
        # Maintain context graph
        await self.context_graph.add_interaction(conversation_id, message, updated_state)
        
        # Integrate with agent memory
        await self.memory_integration.store_interaction(conversation_id, message, updated_state)
        
        return updated_state
        
    async def get_relevant_context(self, conversation_id: str, query: str) -> RelevantContext:
        conversation_history = await self.get_conversation_history(conversation_id)
        
        # Extract relevant context using similarity search
        relevant_interactions = await self.find_relevant_interactions(
            query, conversation_history
        )
        
        # Get related context from other conversations
        related_context = await self.find_related_context(query, conversation_id)
        
        return RelevantContext(
            current_conversation=relevant_interactions,
            related_conversations=related_context,
            context_summary=await self.summarize_context(relevant_interactions),
            confidence_score=self.calculate_context_confidence(relevant_interactions)
        )

class ConversationFlowManager:
    def __init__(self):
        self.flow_patterns = FlowPatternLibrary()
        self.intent_predictor = IntentPredictor()
        self.topic_tracker = TopicTracker()
        
    async def manage_conversation_flow(self, conversation: Conversation, new_message: Message) -> FlowDecision:
        # Analyze current flow state
        current_flow = await self.analyze_current_flow(conversation)
        
        # Predict user intent
        predicted_intent = await self.intent_predictor.predict_next_intent(
            conversation.history, new_message
        )
        
        # Track topic evolution
        topic_state = await self.topic_tracker.update_topics(conversation, new_message)
        
        # Determine flow decision
        flow_decision = await self.determine_flow_action(
            current_flow, predicted_intent, topic_state
        )
        
        return flow_decision
        
    async def determine_flow_action(self, current_flow: FlowState, predicted_intent: Intent, topic_state: TopicState) -> FlowDecision:
        # Check for flow transitions
        if self.should_escalate_to_human(current_flow, predicted_intent):
            return FlowDecision(action="escalate_to_human", reason="complex_query")
            
        if self.should_transfer_to_specialist(predicted_intent, topic_state):
            specialist_type = self.determine_specialist_type(predicted_intent, topic_state)
            return FlowDecision(action="transfer_to_specialist", specialist=specialist_type)
            
        if self.should_clarify_intent(predicted_intent):
            clarification = await self.generate_clarification_question(predicted_intent)
            return FlowDecision(action="request_clarification", question=clarification)
            
        return FlowDecision(action="continue_conversation", confidence=predicted_intent.confidence)

class PersonalityEngine:
    def __init__(self):
        self.personality_profiles = PersonalityProfileManager()
        self.style_adapters = CommunicationStyleAdapters()
        self.emotion_engine = EmotionEngine()
        
    async def apply_personality(self, response: Response, user_profile: UserProfile, context: Context) -> PersonalizedResponse:
        # Get agent personality profile
        personality = await self.personality_profiles.get_personality(context.agent_id)
        
        # Adapt to user preferences
        adapted_personality = await self.adapt_to_user(personality, user_profile)
        
        # Apply communication style
        styled_response = await self.style_adapters.apply_style(
            response, adapted_personality.communication_style
        )
        
        # Add emotional context
        emotional_response = await self.emotion_engine.add_emotional_context(
            styled_response, adapted_personality.emotional_profile, context
        )
        
        return PersonalizedResponse(
            content=emotional_response.content,
            tone=emotional_response.tone,
            style=adapted_personality.communication_style,
            emotional_context=emotional_response.emotional_markers,
            personality_traits=adapted_personality.dominant_traits
        )

communication_styles:
  formal:
    characteristics:
      - professional_language
      - structured_responses
      - minimal_casual_expressions
      - detailed_explanations
    use_cases:
      - business_communications
      - legal_discussions
      - technical_documentation
      
  casual:
    characteristics:
      - conversational_language
      - informal_expressions
      - shortened_responses
      - relatable_examples
    use_cases:
      - general_inquiries
      - creative_discussions
      - social_interactions
      
  technical:
    characteristics:
      - precise_terminology
      - detailed_specifications
      - step_by_step_instructions
      - code_examples
    use_cases:
      - technical_support
      - development_discussions
      - troubleshooting
      
  empathetic:
    characteristics:
      - understanding_language
      - emotional_validation
      - supportive_responses
      - patient_explanations
    use_cases:
      - customer_support
      - difficult_situations
      - learning_assistance

class InterfaceAdaptationEngine:
    def __init__(self):
        self.user_behavior_analyzer = UserBehaviorAnalyzer()
        self.preference_learner = PreferenceLearner()
        self.context_analyzer = ContextAnalyzer()
        
    async def adapt_interface(self, user_id: str, current_context: Context) -> InterfaceConfiguration:
        # Analyze user behavior patterns
        behavior_profile = await self.user_behavior_analyzer.analyze(user_id)
        
        # Learn user preferences
        preferences = await self.preference_learner.get_preferences(user_id)
        
        # Analyze current context
        context_factors = await self.context_analyzer.analyze(current_context)
        
        # Generate adaptive configuration
        adaptation = await self.generate_adaptation(
            behavior_profile, preferences, context_factors
        )
        
        return InterfaceConfiguration(
            preferred_modalities=adaptation.modalities,
            response_verbosity=adaptation.verbosity,
            interaction_speed=adaptation.speed,
            visual_layout=adaptation.layout,
            accessibility_features=adaptation.accessibility
        )

class WebSocketHandler:
    def __init__(self):
        self.connections = ConnectionManager()
        self.message_router = MessageRouter()
        self.heartbeat_manager = HeartbeatManager()
        
    async def handle_connection(self, websocket: WebSocket):
        connection_id = await self.connections.register(websocket)
        
        try:
            # Setup heartbeat
            await self.heartbeat_manager.start_heartbeat(connection_id)
            
            # Message handling loop
            async for message in websocket:
                await self.process_message(connection_id, message)
                
        except WebSocketDisconnect:
            await self.handle_disconnect(connection_id)
        except Exception as e:
            await self.handle_error(connection_id, e)
        finally:
            await self.cleanup_connection(connection_id)
            
    async def process_message(self, connection_id: str, message: WebSocketMessage):
        # Parse message
        parsed_message = await self.parse_message(message)
        
        # Route to appropriate handler
        response = await self.message_router.route(parsed_message)
        
        # Send response
        await self.send_response(connection_id, response)

class HTTPHandler:
    def __init__(self):
        self.api_router = APIRouter()
        self.middleware = MiddlewareStack()
        self.rate_limiter = RateLimiter()
        
    async def handle_request(self, request: HTTPRequest) -> HTTPResponse:
        # Apply middleware
        processed_request = await self.middleware.process_request(request)
        
        # Check rate limits
        rate_limit_result = await self.rate_limiter.check_limit(processed_request)
        if not rate_limit_result.allowed:
            return HTTPResponse(status=429, body=rate_limit_result.error_message)
            
        # Route to handler
        try:
            response = await self.api_router.route(processed_request)
            return response
        except Exception as e:
            return await self.handle_exception(e, processed_request)

class StreamManager:
    def __init__(self):
        self.streams = {}
        self.quality_controller = StreamQualityController()
        self.buffer_manager = StreamBufferManager()
        
    async def create_stream(self, stream_config: StreamConfiguration) -> Stream:
        stream_id = self.generate_stream_id()
        
        stream = Stream(
            id=stream_id,
            type=stream_config.type,
            quality=stream_config.quality,
            participants=stream_config.participants
        )
        
        # Setup stream pipeline
        pipeline = await self.setup_stream_pipeline(stream)
        stream.pipeline = pipeline
        
        # Start quality monitoring
        await self.quality_controller.start_monitoring(stream)
        
        self.streams[stream_id] = stream
        return stream
        
    async def setup_stream_pipeline(self, stream: Stream) -> StreamPipeline:
        pipeline = StreamPipeline()
        
        # Add appropriate processors based on stream type
        if stream.type == "audio":
            pipeline.add_processor(AudioEncoder())
            pipeline.add_processor(AudioProcessor())
            pipeline.add_processor(AudioDecoder())
        elif stream.type == "video":
            pipeline.add_processor(VideoEncoder())
            pipeline.add_processor(VideoProcessor())
            pipeline.add_processor(VideoDecoder())
        elif stream.type == "data":
            pipeline.add_processor(DataSerializer())
            pipeline.add_processor(DataProcessor())
            pipeline.add_processor(DataDeserializer())
            
        return pipeline

class AuthenticationManager:
    def __init__(self):
        self.auth_providers = {
            "jwt": JWTAuthProvider(),
            "oauth2": OAuth2AuthProvider(),
            "api_key": APIKeyAuthProvider(),
            "mutual_tls": MutualTLSAuthProvider()
        }
        
    async def authenticate(self, credentials: Credentials, method: str) -> AuthenticationResult:
        provider = self.auth_providers.get(method)
        if not provider:
            raise UnsupportedAuthMethodError(method)
            
        # Authenticate with provider
        auth_result = await provider.authenticate(credentials)
        
        if auth_result.success:
            # Generate session token
            session_token = await self.generate_session_token(auth_result.identity)
            
            # Log authentication event
            await self.log_authentication_event(auth_result, session_token)
            
            return AuthenticationResult(
                success=True,
                identity=auth_result.identity,
                session_token=session_token,
                expires_at=auth_result.expires_at
            )
        else:
            await self.log_authentication_failure(credentials, method, auth_result.error)
            return auth_result

class AuthorizationEngine:
    def __init__(self):
        self.policy_engine = PolicyEngine()
        self.permission_resolver = PermissionResolver()
        
    async def authorize(self, identity: Identity, action: Action, resource: Resource, context: Context) -> AuthorizationResult:
        # Resolve permissions
        permissions = await self.permission_resolver.resolve_permissions(identity)
        
        # Evaluate policies
        policy_result = await self.policy_engine.evaluate(
            identity=identity,
            action=action,
            resource=resource,
            context=context,
            permissions=permissions
        )
        
        return AuthorizationResult(
            authorized=policy_result.allow,
            reason=policy_result.reason,
            conditions=policy_result.conditions,
            audit_info=policy_result.audit_info
        )

class EncryptionManager:
    def __init__(self):
        self.key_manager = KeyManager()
        self.cipher_suite = CipherSuite()
        
    async def encrypt_message(self, message: Message, recipient: str) -> EncryptedMessage:
        # Get recipient's public key
        public_key = await self.key_manager.get_public_key(recipient)
        
        # Generate session key
        session_key = await self.generate_session_key()
        
        # Encrypt message with session key
        encrypted_content = await self.cipher_suite.encrypt(message.content, session_key)
        
        # Encrypt session key with recipient's public key
        encrypted_session_key = await self.cipher_suite.encrypt_key(session_key, public_key)
        
        return EncryptedMessage(
            encrypted_content=encrypted_content,
            encrypted_session_key=encrypted_session_key,
            algorithm=self.cipher_suite.algorithm,
            metadata=message.metadata
        )
        
    async def decrypt_message(self, encrypted_message: EncryptedMessage, recipient: str) -> Message:
        # Get recipient's private key
        private_key = await self.key_manager.get_private_key(recipient)
        
        # Decrypt session key
        session_key = await self.cipher_suite.decrypt_key(
            encrypted_message.encrypted_session_key, private_key
        )
        
        # Decrypt message content
        decrypted_content = await self.cipher_suite.decrypt(
            encrypted_message.encrypted_content, session_key
        )
        
        return Message(
            content=decrypted_content,
            metadata=encrypted_message.metadata
        )

class InterfaceMemoryIntegration:
    def enhance_conversation_with_memory(self, conversation: Conversation, agent_id: str) -> EnhancedConversation:
        # Retrieve relevant conversation memories
        conversation_memories = self.memory_system.retrieve_conversation_memories(
            agent_id, conversation.topic, conversation.participants
        )
        
        # Retrieve procedural memories for conversation handling
        conversation_procedures = self.memory_system.retrieve_procedures(
            agent_id, "conversation_management"
        )
        
        # Enhance conversation with memories
        enhanced = conversation.copy()
        enhanced.relevant_memories = conversation_memories
        enhanced.conversation_procedures = conversation_procedures
        
        return enhanced

class InterfaceContextIntegration:
    def enrich_communication_with_context(self, message: Message, context: Context) -> EnrichedMessage:
        # Add environmental context
        environmental_context = self.context_system.get_environmental_context()
        
        # Add business context
        business_context = self.context_system.get_business_context()
        
        # Add conversational context
        conversational_context = self.context_system.get_conversational_context(message.conversation_id)
        
        return EnrichedMessage(
            original_message=message,
            environmental_context=environmental_context,
            business_context=business_context,
            conversational_context=conversational_context,
            context_relevance_score=self.calculate_relevance(message, context)
        )

interface_metrics:
  latency_metrics:
    - message_processing_time
    - response_generation_time
    - multimodal_fusion_time
    - end_to_end_conversation_latency
    
  throughput_metrics:
    - messages_per_second
    - concurrent_conversations
    - simultaneous_connections
    - data_transfer_rate
    
  quality_metrics:
    - conversation_completion_rate
    - user_satisfaction_score
    - intent_recognition_accuracy
    - response_relevance_score
    
  reliability_metrics:
    - connection_uptime
    - message_delivery_rate
    - error_rate_by_type
    - fallback_activation_rate

# Check interface system health
sindhan-cli interface health --all-modalities
 
# Test conversation flow
sindhan-cli interface test-conversation --agent-id=<agent_id>
 
# Analyze communication patterns
sindhan-cli interface analyze-patterns --period=24h
 
# Debug multimodal processing
sindhan-cli interface debug-multimodal --session-id=<session_id>
 
# Export conversation data
sindhan-cli interface export-conversations --format=json