AI-Native Application Architecture

ai_added:
  description: Traditional app with AI features bolted on
  characteristics:
    - AI is optional enhancement
    - Core functionality works without AI
    - AI errors are feature failures

ai_native:
  description: Application designed around AI capabilities
  characteristics:
    - AI is core to value proposition
    - Architecture handles AI uncertainty
    - Feedback loops built in from start

architectural_differences:
  traditional_app:
    - Deterministic logic
    - Exact responses expected
    - Errors are bugs
    - State is precise

  ai_native_app:
    - Probabilistic outcomes
    - Response ranges acceptable
    - Errors are handled gracefully
    - State includes confidence

from dataclasses import dataclass
from typing import Optional

@dataclass
class AIResponse:
    """Response that embraces uncertainty."""
    content: str
    confidence: float  # 0-1
    alternatives: list[str]
    needs_verification: bool
    sources: list[str]

class AIService:
    def generate(self, prompt: str) -> AIResponse:
        result = self.llm.generate(prompt)

        return AIResponse(
            content=result.text,
            confidence=self._assess_confidence(result),
            alternatives=self._get_alternatives(prompt, result),
            needs_verification=result.confidence < 0.8,
            sources=self._extract_sources(result)
        )

class FeedbackAwareService:
    """Service that learns from user feedback."""

    def __init__(self, db, llm):
        self.db = db
        self.llm = llm

    async def generate(self, request: Request) -> Response:
        # Check for similar past requests with feedback
        similar = await self.db.find_similar_requests(request.prompt)
        successful = [s for s in similar if s.feedback == "positive"]

        # Use successful patterns in prompt
        if successful:
            prompt = self._enhance_with_examples(request.prompt, successful)
        else:
            prompt = request.prompt

        response = await self.llm.generate(prompt)

        # Store for future learning
        await self.db.store_request_response(
            request=request,
            response=response,
            request_id=str(uuid.uuid4())
        )

        return response

    async def record_feedback(self, request_id: str, feedback: str):
        """Record user feedback to improve future responses."""
        await self.db.update_feedback(request_id, feedback)

class AIRequestPipeline:
    """Complete request pipeline for AI-native app."""

    async def process(self, request: UserRequest) -> UserResponse:
        # 1. Context enrichment
        context = await self.context_layer.enrich(request)

        # 2. Safety check
        safety = await self.safety_layer.check_input(request)
        if not safety.allowed:
            return self._safe_rejection(safety.reason)

        # 3. AI generation
        ai_response = await self.ai_layer.generate(request, context)

        # 4. Quality validation
        quality = await self.quality_layer.validate(ai_response)
        if not quality.passed:
            ai_response = await self.ai_layer.regenerate(request, quality.feedback)

        # 5. Output safety
        filtered = await self.safety_layer.filter_output(ai_response)

        # 6. Experience enhancement
        response = self.ux_layer.format(filtered)

        # 7. Record for feedback
        await self.feedback_layer.record(request, response)

        return response

class IntelligentRouter:
    """Route requests to appropriate AI models."""

    def __init__(self, models: dict):
        self.models = models
        self.classifier = self._load_classifier()

    async def route(self, request: Request) -> str:
        # Classify request characteristics
        classification = self.classifier.classify(request)

        # Route based on classification
        if classification.needs_reasoning:
            return "gpt-4"
        elif classification.is_simple:
            return "gpt-3.5-turbo"
        elif classification.is_code:
            return "claude-3-sonnet"
        elif classification.needs_speed:
            return "mistral"
        else:
            return "gpt-3.5-turbo"  # Default

    async def execute(self, request: Request) -> Response:
        model = await self.route(request)
        return await self.models[model].generate(request)

ux_patterns:
  confidence_indicators:
    high_confidence: Show as normal
    medium_confidence: Add disclaimer
    low_confidence: Request verification

  progressive_disclosure:
    initial: Summary response
    on_demand: Sources, alternatives
    deep_dive: Full reasoning

  human_escalation:
    automatic: For low confidence
    user_triggered: "Talk to human" option
    seamless: Context transfer

class GracefulDegradation:
    """Handle AI failures gracefully in UX."""

    async def get_response(self, request: Request) -> Response:
        try:
            # Try primary AI
            response = await self.primary_ai.generate(request)
            if response.confidence > 0.7:
                return Response(
                    content=response.content,
                    type="ai_generated"
                )
        except AIError:
            pass

        try:
            # Fall back to simpler AI
            response = await self.fallback_ai.generate(request)
            return Response(
                content=response.content,
                type="ai_generated",
                notice="Using simplified AI"
            )
        except AIError:
            pass

        # Fall back to templates or search
        return Response(
            content=self.template_response(request),
            type="template",
            notice="AI unavailable, showing related information"
        )

knowledge_architecture:
  vector_store:
    purpose: Semantic search, RAG
    content: Documents, FAQs, knowledge base
    updates: Near real-time

  structured_data:
    purpose: Facts, entities, relationships
    content: Database records, catalogs
    updates: Transactional

  conversation_memory:
    purpose: Context within sessions
    content: Recent interactions
    updates: Per message

  learning_store:
    purpose: Feedback and improvements
    content: Request/response pairs, feedback
    updates: Continuous