Microservices Architecture

AICL Example: Microservices Architecture Implements a production-grade microservices system with service discovery, API gateway, circuit breaker, saga pattern for distributed transactions, and distributed tracing.

Instruction: Create an AICL specification for a microservices architecture. Include all 10 language levels: architecture (goals, constraints, risks with recovery, layers), entities, behaviors, conditions, events, concurrency, optimization, learning, security, and native code.

Response:
# AICL Example: Microservices Architecture
# Implements a production-grade microservices system with service discovery, API gateway,
# circuit breaker, saga pattern for distributed transactions, and distributed tracing.

# Level 1: Architecture
Goal: Build a resilient microservices architecture supporting independent service deployment,
    cross-service communication, fault isolation, distributed transaction coordination via saga,
    and full observability through distributed tracing.

Constraint: No service may directly reference another service's internal data store.
Constraint: All inter-service communication must traverse the API gateway.
Constraint: Each service must be independently deployable and scalable.
Constraint: Latency for synchronous calls must remain below 200ms p99.
Constraint: Saga transactions must complete within 30 seconds or compensate.

Risk: Service discovery failure leaves orphaned instances
Recovery: Fallback to static registry with heartbeat-based health checks and manual override

Risk: Circuit breaker cascade causes system-wide outage
Recovery: Bulkhead isolation per service with independent breaker pools and graceful degradation

Risk: Saga participant crash mid-transaction leaves partial state
Recovery: Saga orchestrator retries with exponential backoff; compensating transactions for unrecoverable failures

Risk: API gateway becomes single point of failure
Recovery: Deploy gateway replicas across availability zones with leader election and failover routing

Risk: Distributed trace context lost across async boundaries
Recovery: Inject trace context into message headers; background reconciler repairs broken traces

Risk: Network partition causes split-brain service registry
Recovery: Quorum-based registration with majority-wins consensus and stale-entry eviction

Layer: Infrastructure
    SubLayer: ServiceDiscovery
    SubLayer: APIGateway
    SubLayer: MessageBus
Layer: Services
    SubLayer: OrderService
    SubLayer: PaymentService
    SubLayer: InventoryService
    SubLayer: NotificationService
Layer: Observability
    SubLayer: DistributedTracing
    SubLayer: MetricsAggregator
    SubLayer: LogCollector

Validation: Service registry heartbeat must arrive every 5 seconds
Validation: API gateway route table must match deployed service count
Validation: Circuit breaker open duration must not exceed 60 seconds
Validation: Saga transaction must have a defined compensating action for every step
Validation: Distributed trace spans must propagate correlation IDs across all hops
Validation: Service health endpoint must respond within 500ms

# Level 2: Entities
Entity ServiceInstance
    serviceId: string
    host: string
    port: integer
    healthStatus: string
    metadata: dict
    registeredAt: datetime
    lastHeartbeat: datetime
    version: string
    weight: integer

Entity ApiRoute
    routeId: string
    pathPattern: string
    targetService: string
    methods: list
    rateLimit: integer
    authRequired: boolean
    timeout: integer
    retryPolicy: dict

Entity CircuitBreaker
    breakerId: string
    serviceName: string
    state: string
    failureCount: integer
    successCount: integer
    lastFailureAt: datetime
    openDuration: integer
    halfOpenMaxCalls: integer

Entity SagaInstance
    sagaId: string
    sagaType: string
    currentState: string
    steps: list
    completedSteps: list
    compensateSteps: list
    startedAt: datetime
    timeoutAt: datetime
    participantServices: list

Entity TraceSpan
    traceId: string
    spanId: string
    parentSpanId: string
    serviceName: string
    operationName: string
    startTime: datetime
    endTime: datetime
    tags: dict
    status: string

Entity ServiceMetric
    metricId: string
    serviceName: string
    metricName: string
    value: float
    timestamp: datetime
    unit: string
    tags: dict
    percentile: float

# Level 3: Behaviors
Behavior RegisterService
    Input:
        instance: ServiceInstance
    Output:
        registrationId: string
    Action:
        Validate instance fields and health endpoint
        Check for duplicate serviceId and host:port combination
        Register in service discovery with TTL of 10 seconds
        Broadcast registration event to all gateway replicas
        Initialize circuit breaker for new service
        Return registration confirmation

Behavior RouteRequest
    Input:
        route: ApiRoute
        payload: dict
        headers: dict
    Output:
        response: dict
        latency: integer
    Action:
        Match incoming path and method to route definition
        Check rate limit and authentication requirements
        Resolve target service instance via discovery
        Evaluate circuit breaker state before forwarding
        Inject trace context headers
        Forward request with timeout enforcement
        Record latency metric and trace span

Behavior ExecuteSaga
    Input:
        sagaType: string
        payload: dict
    Output:
        sagaId: string
        status: string
    Action:
        Create saga instance with ordered steps
        Begin first step and mark as in-progress
        On step completion, advance to next step
        On step failure, execute compensating actions in reverse
        Persist saga state at each transition
        Emit saga completion or compensation event

Behavior HandleCircuitBreaker
    Input:
        serviceName: string
        result: string
        latency: integer
    Output:
        newState: string
    Action:
        Record success or failure against breaker counters
        If failure threshold exceeded, transition to Open state
        If in Open state and cooldown elapsed, transition to HalfOpen
        If HalfOpen and success threshold met, transition to Closed
        If HalfOpen and failure detected, transition back to Open
        Emit state change event to observability layer

Behavior DiscoverService
    Input:
        serviceName: string
        version: string
        region: string
    Output:
        instances: list
    Action:
        Query registry for matching service entries
        Filter by version compatibility and region
        Apply load-balancing weight to instance selection
        Verify health status of returned instances
        Return healthy instance list sorted by latency

Behavior CollectTrace
    Input:
        span: TraceSpan
    Output:
        accepted: boolean
    Action:
        Validate trace and span IDs are present
        Link span to parent if parentSpanId exists
        Calculate span duration from start and end times
        Store span in trace backend
        Check for trace completion (all spans received)
        Emit trace-complete event if root span received

# Level 4: Conditions
Condition: ServiceUnhealthy
    When service instance misses 3 consecutive heartbeats
    Then mark instance as unhealthy and remove from active routing pool

Condition: RateLimitExceeded
    When request count per minute exceeds route rateLimit threshold
    Then return HTTP 429 with retry-after header and log throttle event

Condition: SagaTimeout
    When saga execution exceeds timeoutAt without completion
    Then trigger compensating transactions for all completed steps and mark saga as timed_out

# Level 5: Events
Event: OnServiceRegistered
    On new service instance registered in discovery
    Action: Update API gateway route table and warm connection pool to new instance

Event: OnCircuitBreakerStateChanged
    On circuit breaker transitions between Open, Closed, or HalfOpen states
    Action: Notify monitoring system and adjust routing weights away from degraded service

Event: OnSagaStepCompleted
    On a saga participant confirms step completion
    Action: Advance saga state machine and dispatch next step to next participant

Event: OnTraceAnomaly
    On trace duration exceeds SLO threshold or error rate spikes
    Action: Create incident alert and trigger automated diagnostic collection

# Level 6: Concurrency
Parallel:
    Service health monitoring across all registered instances
    Metrics aggregation from all services into centralized time-series store
    Log streaming from all service containers to log collector
    Circuit breaker state evaluation for all downstream connections
    Trace span ingestion and parent-child linking across services

# Level 7: Optimization
Optimize: Inter-service call latency
    Priority: Prefer collocated instances in same availability zone; then by lowest p99 latency history

Optimize: Gateway routing efficiency
    Priority: Cache route lookups with LRU eviction; pre-warm connections to top percentile services

# Level 8: Learning
Learn: Service failure patterns
    Goal: Predict circuit breaker trips before they occur based on latency and error rate trends
    Adapt: Circuit breaker thresholds
    Based: Historical failure pattern analysis across sliding 1-hour window

Learn: Optimal retry backoff intervals
    Goal: Minimize retry storm risk while maximizing recovery speed
    Adapt: Retry backoff multiplier and max retry count
    Based: Downstream service recovery time distributions and error categorization

# Level 9: Security
Security:
    Encrypt: All inter-service communication via mutual TLS with certificate rotation every 24 hours
    Encrypt: Saga state payload containing PII using AES-256-GCM envelope encryption
    Protect: API gateway against SQL injection, XSS, and request smuggling via WAF rules
    Protect: Service registry from unauthorized writes with RBAC and signed registrations
    Protect: Trace data from leaking sensitive headers by applying redaction rules

# Level 10: Native
Native: Python
{
import asyncio
from dataclasses import dataclass, field
from typing import Optional, Dict, List
from enum import Enum
import time

class BreakerState(Enum):
    CLOSED = "closed"
    OPEN = "open"
    HALF_OPEN = "half_open"

@dataclass
class CircuitBreakerConfig:
    failure_threshold: int = 5
    success_threshold: int = 3
    open_duration_seconds: int = 30
    half_open_max_calls: int = 2

class CircuitBreaker:
    def __init__(self, service_name: str, config: CircuitBreakerConfig = None):
        self.service_name = service_name
        self.config = config or CircuitBreakerConfig()
        self.state = BreakerState.CLOSED
        self.failure_count = 0
        self.success_count = 0
        self.last_failure_time: Optional[float] = None
        self.half_open_calls = 0

    def record_success(self):
        if self.state == BreakerState.HALF_OPEN:
            self.success_count += 1
            if self.success_count >= self.config.success_threshold:
                self.state = BreakerState.CLOSED
                self.failure_count = 0
                self.success_count = 0
        elif self.state == BreakerState.CLOSED:
            self.failure_count = max(0, self.failure_count - 1)

    def record_failure(self):
        self.failure_count += 1
        self.last_failure_time = time.time()
        if self.state == BreakerState.HALF_OPEN:
            self.state = BreakerState.OPEN
            self.success_count = 0
        elif self.state == BreakerState.CLOSED:
            if self.failure_count >= self.config.failure_threshold:
                self.state = BreakerState.OPEN

    def can_execute(self) -> bool:
        if self.state == BreakerState.CLOSED:
            return True
        if self.state == BreakerState.OPEN:
            if self.last_failure_time and \
               (time.time() - self.last_failure_time) >= self.config.open_duration_seconds:
                self.state = BreakerState.HALF_OPEN
                self.success_count = 0
                self.half_open_calls = 0
                return True
            return False
        if self.state == BreakerState.HALF_OPEN:
            return self.half_open_calls < self.config.half_open_max_calls
        return False

class SagaOrchestrator:
    def __init__(self):
        self.sagas: Dict[str, dict] = {}

    async def execute_saga(self, saga_id: str, steps: List[dict]):
        self.sagas[saga_id] = {
            "steps": steps, "completed": [], "status": "running"
        }
        for i, step in enumerate(steps):
            try:
                result = await step["action"]()
                self.sagas[saga_id]["completed"].append(i)
            except Exception as e:
                self.sagas[saga_id]["status"] = "compensating"
                await self._compensate(saga_id)
                raise

    async def _compensate(self, saga_id: str):
        saga = self.sagas[saga_id]
        for idx in reversed(saga["completed"]):
            compensate = saga["steps"][idx].get("compensate")
            if compensate:
                try:
                    await compensate()
                except Exception:
                    pass
        saga["status"] = "compensated"

breaker = CircuitBreaker("payment-service")
if breaker.can_execute():
    print(f"Circuit is {breaker.state.value}, request allowed")
}


CQRS + Event Sourcing

AICL Example: CQRS + Event Sourcing System Implements a comprehensive CQRS architecture with separate command and query buses, an append-only event store, real-time projections, periodic snapshots, and materialized read models.

Instruction: Create an AICL specification for a cqrs + event sourcing. Include all 10 language levels: architecture (goals, constraints, risks with recovery, layers), entities, behaviors, conditions, events, concurrency, optimization, learning, security, and native code.

Response:
# AICL Example: CQRS + Event Sourcing System
# Implements a comprehensive CQRS architecture with separate command and query buses,
# an append-only event store, real-time projections, periodic snapshots, and materialized read models.

# Level 1: Architecture
Goal: Build a CQRS + Event Sourcing system that separates write and read paths,
    persists all state changes as immutable events, projects events into optimized read models,
    and supports temporal queries with snapshot-based recovery.

Constraint: Commands must never return domain data; only acknowledgment of acceptance or rejection.
Constraint: Events in the store are immutable and append-only; no deletion or mutation permitted.
Constraint: Read models must be eventually consistent with a maximum lag of 500ms.
Constraint: Each aggregate root must have a deterministic event replay mechanism.
Constraint: Snapshots must be taken at configurable intervals and validated against full replay.

Risk: Event store corruption causes total data loss
Recovery: Replicate event store across 3 nodes with write quorum of 2; restore from replica and replay

Risk: Projection lag exceeds SLO causing stale reads
Recovery: Detect lag via watermark comparison; trigger catch-up projection from last checkpoint

Risk: Snapshot divergence from event replay
Recovery: Rebuild snapshot from full event stream and alert on mismatch count exceeding threshold

Risk: Command bus overload during traffic spikes
Recovery: Apply backpressure with command queue depth limits; spill overflow to durable dead-letter queue

Risk: Read model schema migration fails during deployment
Recovery: Deploy new projection version alongside old; switch readers atomically after verification

Risk: Event version incompatibility across service boundaries
Recovery: Apply upcasting transformers in event deserialization pipeline with schema registry

Layer: WriteSide
    SubLayer: CommandBus
    SubLayer: CommandHandler
    SubLayer: AggregateRoot
    SubLayer: EventStore
Layer: ReadSide
    SubLayer: EventBus
    SubLayer: Projector
    SubLayer: ReadModel
    SubLayer: QueryBus
Layer: Infrastructure
    SubLayer: SnapshotStore
    SubLayer: SchemaRegistry
    SubLayer: EventUpcaster

Validation: Every command must have a corresponding command handler registered
Validation: Event sequence number must be monotonically increasing per aggregate
Validation: Snapshot aggregate version must match event stream version at snapshot point
Validation: Projection checkpoint must never move backward
Validation: Query bus must reject queries targeting non-existent read models
Validation: Command authorization must be verified before handler dispatch

# Level 2: Entities
Entity Command
    commandId: string
    commandType: string
    aggregateId: string
    payload: dict
    metadata: dict
    issuedAt: datetime
    issuedBy: string
    expectedVersion: integer
    correlationId: string

Entity DomainEvent
    eventId: string
    eventType: string
    aggregateId: string
    aggregateType: string
    sequenceNumber: integer
    payload: dict
    metadata: dict
    occurredAt: datetime
    causationId: string
    correlationId: string

Entity AggregateRoot
    aggregateId: string
    aggregateType: string
    version: integer
    uncommittedEvents: list
    state: dict
    createdAt: datetime
    modifiedAt: datetime
    snapshotThreshold: integer

Entity Projection
    projectionId: string
    projectionName: string
    readModelType: string
    checkpoint: integer
    state: string
    lastProcessedEventId: string
    lastProcessedAt: datetime
    errorCount: integer
    batchSize: integer

Entity Snapshot
    snapshotId: string
    aggregateId: string
    aggregateType: string
    version: integer
    state: dict
    createdAt: datetime
    compressionType: string
    sizeBytes: integer

Entity ReadModel
    modelId: string
    modelType: string
    aggregateId: string
    data: dict
    version: integer
    updatedAt: datetime
    queryIndex: dict
    tags: set

# Level 3: Behaviors
Behavior DispatchCommand
    Input:
        command: Command
    Output:
        result: string
    Action:
        Validate command schema against registered definition
        Authorize command against aggregate-level ACL
        Load aggregate from event store or snapshot
        Apply command to aggregate producing events
        Validate events against aggregate invariants
        Persist events to event store with optimistic concurrency
        Publish events to event bus
        Return command acceptance acknowledgment

Behavior PersistEvent
    Input:
        event: DomainEvent
    Output:
        sequenceNumber: integer
    Action:
        Validate event schema against schema registry
        Assign global and aggregate sequence numbers
        Append event to event store partition
        Replicate to follower nodes with quorum acknowledgment
        Update aggregate version counter
        Return assigned sequence number

Behavior HandleQuery
    Input:
        queryType: string
        parameters: dict
    Output:
        results: list
    Action:
        Resolve read model from query type mapping
        Validate query parameters against model schema
        Execute parameterized query against read model store
        Apply pagination and sorting rules
        Return results with total count and page metadata

Behavior RebuildProjection
    Input:
        projectionName: string
        fromCheckpoint: integer
    Output:
        status: string
        eventsProcessed: integer
    Action:
        Reset projection checkpoint to specified position
        Load events from event store starting at checkpoint
        Apply events to projection handler in batch
        Update checkpoint after each batch
        Verify projection consistency on completion
        Return rebuild statistics

Behavior TakeSnapshot
    Input:
        aggregateId: string
    Output:
        snapshotId: string
    Action:
        Load aggregate at current version
        Serialize aggregate state
        Compress snapshot payload
        Persist to snapshot store
        Validate snapshot by replaying from snapshot version
        Return snapshot reference

Behavior UpcastEvent
    Input:
        event: DomainEvent
        targetVersion: integer
    Output:
        upcasted: DomainEvent
    Action:
        Determine current event schema version
        Load required upcaster chain from registry
        Apply each upcaster transformation in order
        Validate resulting event against target schema
        Update event metadata with upcast trace
        Return transformed event

# Level 4: Conditions
Condition: OptimisticConcurrencyConflict
    When command expectedVersion does not match aggregate current version
    Then reject command with concurrency conflict and suggest retry with fresh version

Condition: ProjectionErrorThresholdExceeded
    When projection error count exceeds 10 consecutive failures
    Then pause projection, alert operators, and queue events for later replay

Condition: SnapshotSizeExceeded
    When serialized snapshot size exceeds 1MB
    Then compress with gzip and recheck; if still over limit, split aggregate into sub-aggregates

# Level 5: Events
Event: OnEventPersisted
    On event successfully appended to event store
    Action: Publish event to all active projectors via event bus with ordering guarantee

Event: OnSnapshotTaken
    On aggregate snapshot persisted
    Action: Update aggregate metadata and schedule next snapshot interval

Event: OnProjectionRebuildRequested
    On projection rebuild triggered manually or by error recovery
    Action: Mark projection as rebuilding, reset checkpoint, and begin replay from specified position

Event: OnCommandRejected
    On command validation or authorization failure
    Action: Log rejection reason, emit metric, and notify originating service via callback

# Level 6: Concurrency
Parallel:
    Event persistence to store and replication to followers
    Event publication to multiple projectors concurrently
    Snapshot creation for eligible aggregates in background
    Read model queries served independently from write path
    Event upcasting applied in parallel per event during replay

# Level 7: Optimization
Optimize: Event store append throughput
    Priority: Batch events per aggregate partition; use sequential I/O on append-only segments

Optimize: Projection catch-up speed
    Priority: Increase batch size dynamically during catch-up; reduce to normal after checkpoint caught

# Level 8: Learning
Learn: Snapshot frequency optimization
    Goal: Determine optimal snapshot interval per aggregate type to minimize replay cost
    Adapt: Snapshot threshold per aggregate type
    Based: Aggregate event count distribution and average replay duration metrics

Learn: Projection batch sizing
    Goal: Maximize projection throughput without causing read model store contention
    Adapt: Projection batch size per projection type
    Based: Projection processing latency and read model write queue depth

# Level 9: Security
Security:
    Encrypt: Event payload containing PII using field-level encryption with key rotation every 12 hours
    Encrypt: Snapshot state using AES-256 with per-aggregate derived keys
    Protect: Command bus from unauthorized submissions via JWT validation and role-based ACL
    Protect: Event store from tampering via cryptographic chaining (hash chain of event hashes)
    Protect: Read models from data leakage by applying column-level access control on queries

# Level 10: Native
TypeScript
{
import { v4 as uuid } from 'uuid';

interface DomainEvent {
  eventId: string;
  eventType: string;
  aggregateId: string;
  sequenceNumber: number;
  payload: Record<string, any>;
  metadata: Record<string, any>;
  occurredAt: Date;
  correlationId: string;
}

interface Command {
  commandId: string;
  commandType: string;
  aggregateId: string;
  payload: Record<string, any>;
  expectedVersion: number;
}

class EventStore {
  private streams: Map<string, DomainEvent[]> = new Map();

  async append(
    aggregateId: string,
    events: DomainEvent[],
    expectedVersion: number
  ): Promise<number> {
    const stream = this.streams.get(aggregateId) || [];
    if (stream.length !== expectedVersion) {
      throw new Error(
        `Concurrency conflict: expected ${expectedVersion}, got ${stream.length}`
      );
    }
    for (const event of events) {
      event.sequenceNumber = stream.length;
      event.eventId = uuid();
      event.occurredAt = new Date();
      stream.push(event);
    }
    this.streams.set(aggregateId, stream);
    return stream.length;
  }

  async load(
    aggregateId: string,
    fromVersion: number = 0
  ): Promise<DomainEvent[]> {
    const stream = this.streams.get(aggregateId) || [];
    return stream.filter((e) => e.sequenceNumber >= fromVersion);
  }
}

abstract class AggregateRoot {
  public aggregateId: string;
  public version: number = 0;
  private uncommitted: DomainEvent[] = [];

  applyEvent(event: DomainEvent): void {
    this.handleEvent(event);
    this.version = event.sequenceNumber + 1;
  }

  protected raiseEvent(eventType: string, payload: Record<string, any>): void {
    const event: DomainEvent = {
      eventId: uuid(),
      eventType,
      aggregateId: this.aggregateId,
      sequenceNumber: this.version,
      payload,
      metadata: {},
      occurredAt: new Date(),
      correlationId: uuid(),
    };
    this.uncommitted.push(event);
    this.applyEvent(event);
  }

  getUncommittedEvents(): DomainEvent[] {
    return [...this.uncommitted];
  }

  clearUncommitted(): void {
    this.uncommitted = [];
  }

  protected abstract handleEvent(event: DomainEvent): void;
}

class OrderAggregate extends AggregateRoot {
  public status: string = 'draft';
  public items: any[] = [];

  createOrder(items: any[]): void {
    this.raiseEvent('OrderCreated', { items });
  }

  confirmOrder(): void {
    if (this.status !== 'draft') throw new Error('Invalid state');
    this.raiseEvent('OrderConfirmed', {});
  }

  protected handleEvent(event: DomainEvent): void {
    switch (event.eventType) {
      case 'OrderCreated':
        this.items = event.payload.items;
        this.status = 'draft';
        break;
      case 'OrderConfirmed':
        this.status = 'confirmed';
        break;
    }
  }
}

const store = new EventStore();
const order = new OrderAggregate();
order.aggregateId = uuid();
order.createOrder([{ sku: 'ABC', qty: 2 }]);
order.confirmOrder();
console.log(`Order version: ${order.version}, status: ${order.status}`);
}


MVC Web Application

AICL Example: MVC Web Application Implements a full Model-View-Controller web application with routing, middleware pipeline, session management, template rendering, input validation, and CSRF protection.

Instruction: Create an AICL specification for a mvc web application. Include all 10 language levels: architecture (goals, constraints, risks with recovery, layers), entities, behaviors, conditions, events, concurrency, optimization, learning, security, and native code.

Response:
# AICL Example: MVC Web Application
# Implements a full Model-View-Controller web application with routing, middleware pipeline,
# session management, template rendering, input validation, and CSRF protection.

# Level 1: Architecture
Goal: Build a production MVC web application framework supporting clean separation of concerns,
    declarative routing with parameter extraction, middleware pipeline for cross-cutting logic,
    secure session management, and view template rendering with layout inheritance.

Constraint: Controllers must not contain business logic; delegate to service layer.
Constraint: Views must not execute arbitrary code; only template expressions permitted.
Constraint: All user input must be validated before reaching controller logic.
Constraint: Session data must be stored server-side; client holds only a signed session ID.
Constraint: Each route must declare its HTTP method, path pattern, and required middleware.

Risk: Session hijacking via stolen session cookie
Recovery: Regenerate session ID on privilege elevation; bind session to IP and user-agent fingerprint

Risk: CSRF token bypass on state-changing requests
Recovery: Double-submit cookie pattern with SameSite=Strict attribute and token rotation per request

Risk: Template injection through unsanitized user input
Recovery: Auto-escape all template expressions by default; explicit unescape requires safe annotation

Risk: Route collision causing request misrouting
Recovery: Detect route conflicts at startup; first-registered wins with warning for duplicates

Risk: Middleware ordering causes authentication bypass
Recovery: Enforce middleware dependency graph; validate ordering at route registration time

Risk: Memory leak from unbounded session store
Recovery: Set session TTL of 30 minutes; background sweeper evicts expired sessions every 60 seconds

Layer: Presentation
    SubLayer: Router
    SubLayer: Controller
    SubLayer: ViewRenderer
    SubLayer: Middleware
Layer: Application
    SubLayer: SessionManager
    SubLayer: AuthService
    SubLayer: ValidationService
    SubLayer: FlashMessenger
Layer: Data
    SubLayer: Model
    SubLayer: Repository
    SubLayer: DatabasePool

Validation: Route path parameters must match declared regex patterns
Validation: Request body must conform to content-type schema before controller dispatch
Validation: Session ID must be cryptographically signed and unexpired
Validation: CSRF token must match server-side stored token for the session
Validation: Response content-type must match route declaration
Validation: Middleware pipeline must not skip authentication for protected routes

# Level 2: Entities
Entity Route
    routeId: string
    httpMethod: string
    pathPattern: string
    controllerName: string
    actionName: string
    middlewareStack: list
    requiredRoles: list
    parameterConstraints: dict
    cacheTtl: integer

Entity Controller
    controllerName: string
    actions: dict
    beforeFilters: list
    afterFilters: list
    layout: string
    rescues: dict
    namespace: string

Entity ViewTemplate
    templateId: string
    templateName: string
    layout: string
    partials: list
    helpers: list
    lastModified: datetime
    compiledAt: datetime
    cacheEnabled: boolean
    mimeType: string

Entity Session
    sessionId: string
    userId: string
    data: dict
    createdAt: datetime
    lastAccessedAt: datetime
    expiresAt: datetime
    ipAddress: string
    userAgent: string
    csrfToken: string

Entity HttpRequest
    requestId: string
    method: string
    path: string
    headers: dict
    queryParams: dict
    bodyParams: dict
    routeParams: dict
    session: Session
    contentType: string
    ipAddress: string

Entity Middleware
    middlewareId: string
    middlewareName: string
    priority: integer
    dependencies: list
    config: dict
    enabled: boolean
    scope: string

# Level 3: Behaviors
Behavior MatchRoute
    Input:
        method: string
        path: string
    Output:
        route: Route
        params: dict
    Action:
        Scan route table for matching HTTP method
        Apply path pattern matching with parameter extraction
        Resolve route parameters against declared constraints
        Verify required middleware is available
        Return matched route with extracted parameters

Behavior DispatchRequest
    Input:
        request: HttpRequest
        route: Route
    Output:
        response: dict
    Action:
        Execute middleware pipeline in priority order
        Instantiate controller with request context
        Apply before-filters on controller
        Invoke controller action with route parameters
        Apply after-filters on controller
        Execute post-action middleware in reverse order
        Return rendered response

Behavior RenderView
    Input:
        templateName: string
        locals: dict
        layout: string
    Output:
        renderedHtml: string
    Action:
        Resolve template file from template registry
        Compile template if not cached or modified since compilation
        Render partials and helpers into template context
        Apply auto-escaping to all interpolated values
        Render layout with yielded content
        Cache rendered output if cacheEnabled
        Return rendered HTML string

Behavior ManageSession
    Input:
        sessionId: string
        operation: string
        data: dict
    Output:
        session: Session
    Action:
        Validate session ID signature and expiration
        Load session data from server-side store
        Apply operation: create, read, update, destroy
        Regenerate ID if privilege elevation detected
        Update last accessed timestamp
        Persist session changes to store

Behavior ValidateInput
    Input:
        params: dict
        rules: dict
    Output:
        sanitized: dict
        errors: list
    Action:
        Check each parameter against its validation rule
        Apply type coercion where appropriate
        Sanitize strings against XSS patterns
        Collect all validation errors without short-circuiting
        Return sanitized params and error list

Behavior AuthenticateRequest
    Input:
        request: HttpRequest
        requiredRoles: list
    Output:
        authenticated: boolean
        userId: string
    Action:
        Extract session cookie from request headers
        Validate session ID and load session data
        Check user roles against required roles
        Verify IP and user-agent match session fingerprint
        Return authentication result

# Level 4: Conditions
Condition: SessionExpired
    When session lastAccessedAt exceeds configured TTL
    Then destroy session, clear cookie, and redirect to login with flash message

Condition: UnauthorizedAccess
    When authenticated user lacks required role for route
    Then return HTTP 403 with insufficient-privileges view and log attempt

Condition: ValidationFailure
    When input validation produces any errors
    Then re-render form view with error messages and preserved input values

# Level 5: Events
Event: OnRequestReceived
    On new HTTP request entering the middleware pipeline
    Action: Start request timer, inject correlation ID, and attach trace context

Event: OnAuthenticationFailure
    On session validation or role check failure
    Action: Log security event with IP and user-agent, increment failed auth metric

Event: OnSessionCreated
    On new session established after successful login
    Action: Emit audit event, set session cookie with Secure and HttpOnly flags

Event: OnRouteNotFound
    On no matching route found for request path and method
    Action: Return HTTP 404 with custom not-found view and log path for analysis

# Level 6: Concurrency
Parallel:
    Request handling across multiple worker processes
    Session store read/write operations with optimistic locking
    Template compilation for modified views in background
    Middleware execution for independent concerns (logging, metrics, CORS)
    Static asset serving on separate thread pool from dynamic requests

# Level 7: Optimization
Optimize: Request processing latency
    Priority: Cache compiled templates; pool database connections; minimize middleware overhead per route

Optimize: View rendering throughput
    Priority: Pre-compile templates at startup; cache rendered fragments with key-based expiration

# Level 8: Learning
Learn: Route popularity patterns
    Goal: Identify high-traffic routes for pre-warming and connection pool sizing
    Adapt: Per-route connection pool allocation
    Based: Request frequency distribution and latency percentiles over sliding 15-minute window

Learn: Session store sizing
    Goal: Predict session store memory requirements based on traffic patterns
    Adapt: Session TTL and eviction aggressiveness
    Based: Concurrent session count trends and average session data size

# Level 9: Security
Security:
    Encrypt: Session cookie using HMAC-SHA256 with rotating secret key
    Encrypt: Sensitive session data fields with AES-256-GCM server-side encryption
    Protect: All forms against CSRF with per-request token validation and SameSite cookies
    Protect: Application against XSS with Content-Security-Policy headers and auto-escaping
    Protect: Upload endpoints against file inclusion with mime-type validation and sandboxed storage

# Level 10: Native
Go
{
package mvc

import (
    "crypto/hmac"
    "crypto/sha256"
    "encoding/hex"
    "fmt"
    "net/http"
    "regexp"
    "sync"
    "time"
)

type Route struct {
    Method      string
    Pattern     *regexp.Regexp
    Handler     http.HandlerFunc
    Middleware  []func(http.Handler) http.Handler
    Params      []string
}

type Router struct {
    routes []Route
    mu     sync.RWMutex
}

func NewRouter() *Router {
    return &Router{}
}

func (r *Router) AddRoute(method, pattern string, handler http.HandlerFunc, middleware ...func(http.Handler) http.Handler) {
    re := regexp.MustCompile(pattern)
    paramRe := regexp.MustCompile(`:([a-zA-Z]+)`)
    matches := paramRe.FindAllStringSubmatch(pattern, -1)
    params := make([]string, 0)
    for _, m := range matches {
        params = append(params, m[1])
    }
    r.mu.Lock()
    r.routes = append(r.routes, Route{
        Method: method, Pattern: re, Handler: handler,
        Middleware: middleware, Params: params,
    })
    r.mu.Unlock()
}

func (r *Router) ServeHTTP(w http.ResponseWriter, req *http.Request) {
    r.mu.RLock()
    defer r.mu.RUnlock()
    for _, route := range r.routes {
        if route.Method != req.Method {
            continue
        }
        matches := route.Pattern.FindStringSubmatch(req.URL.Path)
        if matches == nil {
            continue
        }
        for i, name := range route.Params {
            if i+1 < len(matches) {
                req.URL.RawQuery += fmt.Sprintf("&%s=%s", name, matches[i+1])
            }
        }
        var handler http.Handler = route.Handler
        for i := len(route.Middleware) - 1; i >= 0; i-- {
            handler = route.Middleware[i](handler)
        }
        handler.ServeHTTP(w, req)
        return
    }
    http.NotFound(w, req)
}

type SessionManager struct {
    store   map[string]*Session
    secret  []byte
    mu      sync.RWMutex
}

type Session struct {
    ID        string
    Data      map[string]interface{}
    CreatedAt time.Time
    ExpiresAt time.Time
    CSRFToken string
}

func NewSessionManager(secret string) *SessionManager {
    sm := &SessionManager{
        store:  make(map[string]*Session),
        secret: []byte(secret),
    }
    go sm.sweepExpired()
    return sm
}

func (sm *SessionManager) Create(w http.ResponseWriter) *Session {
    id := generateToken(sm.secret)
    session := &Session{
        ID: id, Data: make(map[string]interface{}),
        CreatedAt: time.Now(), ExpiresAt: time.Now().Add(30 * time.Minute),
        CSRFToken: generateToken(sm.secret),
    }
    sm.mu.Lock()
    sm.store[id] = session
    sm.mu.Unlock()
    http.SetCookie(w, &http.Cookie{
        Name: "session_id", Value: id,
        HttpOnly: true, Secure: true, SameSite: http.SameSiteStrictMode,
        Path: "/", MaxAge: 1800,
    })
    return session
}

func (sm *SessionManager) sweepExpired() {
    ticker := time.NewTicker(60 * time.Second)
    for range ticker.C {
        sm.mu.Lock()
        now := time.Now()
        for id, s := range sm.store {
            if now.After(s.ExpiresAt) {
                delete(sm.store, id)
            }
        }
        sm.mu.Unlock()
    }
}

func generateToken(secret []byte) string {
    h := hmac.New(sha256.New, secret)
    h.Write([]byte(fmt.Sprintf("%d", time.Now().UnixNano())))
    return hex.EncodeToString(h.Sum(nil))
}
}


Observer Pattern Event Bus

AICL Example: Observer Pattern Event Bus Implements a robust publish/subscribe event bus with topic routing, guaranteed message ordering, backpressure management, dead letter queue handling, and replay capabilities.

Instruction: Create an AICL specification for a observer pattern event bus. Include all 10 language levels: architecture (goals, constraints, risks with recovery, layers), entities, behaviors, conditions, events, concurrency, optimization, learning, security, and native code.

Response:
# AICL Example: Observer Pattern Event Bus
# Implements a robust publish/subscribe event bus with topic routing, guaranteed message ordering,
# backpressure management, dead letter queue handling, and replay capabilities.

# Level 1: Architecture
Goal: Build a high-throughput event bus implementing the Observer pattern with topic-based routing,
    strict per-topic message ordering, backpressure to protect slow subscribers,
    dead letter queues for poison messages, and replay support for missed events.

Constraint: Publishers must be decoupled from subscribers; no direct references allowed.
Constraint: Each topic maintains FIFO ordering within a partition key.
Constraint: Subscribers must acknowledge messages within a configurable timeout.
Constraint: Backpressure must be applied before memory pressure reaches critical threshold.
Constraint: Dead letter messages must never be silently dropped; always persisted for inspection.

Risk: Slow subscriber causes unbounded buffer growth
Recovery: Apply per-subscriber backpressure with configurable max buffer; overflow goes to DLQ

Risk: Message ordering violation under concurrent publishing
Recovery: Partition by routing key; single-writer per partition with sequencer

Risk: Dead letter queue grows unbounded without reprocessing
Recovery: Scheduled DLQ reprocessor with exponential backoff; alert at queue depth threshold

Risk: Subscriber crash loses in-flight messages
Recovery: Redeliver unacknowledged messages after visibility timeout with at-least-once guarantee

Risk: Topic routing misconfiguration routes messages to wrong subscribers
Recovery: Validate topic subscription patterns at registration; audit log all routing decisions

Risk: Replay floods subscribers with historical volume
Recovery: Throttle replay delivery rate; subscribers opt-in to replay with max throughput setting

Layer: Core
    SubLayer: Publisher
    SubLayer: TopicRouter
    SubLayer: PartitionManager
Layer: Subscription
    SubLayer: SubscriberRegistry
    SubLayer: SubscriptionManager
    SubLayer: AcknowledgmentTracker
Layer: Reliability
    SubLayer: BackpressureController
    SubLayer: DeadLetterQueue
    SubLayer: ReplayEngine

Validation: Topic name must match [a-zA-Z][a-zA-Z0-9_.-] pattern
Validation: Subscriber callback must complete within acknowledgment timeout
Validation: Partition key must be present for ordered topics
Validation: Replay request must specify valid time range and subscriber ID
Validation: Backpressure threshold must be positive and less than system memory limit
Validation: Dead letter message must preserve original headers and failure reason

# Level 2: Entities
Entity Topic
    topicId: string
    topicName: string
    partitionCount: integer
    retentionHours: integer
    isOrdered: boolean
    createdAt: datetime
    messageCount: integer
    byteRate: float
    schema: dict

Entity Subscription
    subscriptionId: string
    subscriberName: string
    topicPattern: string
    callback: string
    filterExpression: string
    maxInFlight: integer
    ackTimeout: integer
    retryPolicy: dict
    createdAt: datetime

Entity Message
    messageId: string
    topic: string
    partitionKey: string
    sequenceNumber: integer
    payload: dict
    headers: dict
    publishedAt: datetime
    expiresAt: datetime
    traceId: string

Entity DeadLetterEntry
    entryId: string
    originalMessage: Message
    subscriberName: string
    failureReason: string
    failureCount: integer
    firstFailedAt: datetime
    lastFailedAt: datetime
    nextRetryAt: datetime
    deadLetteredAt: datetime

Entity BackpressureState
    subscriberId: string
    currentBufferSize: integer
    maxBufferSize: integer
    pressureLevel: string
    throttleRate: float
    droppedCount: integer
    lastAdjustedAt: datetime

Entity SubscriberMetrics
    subscriberId: string
    messagesProcessed: integer
    messagesFailed: integer
    averageLatency: float
    p99Latency: float
    lastMessageAt: datetime
    activeSubscriptions: integer
    dlqDepth: integer

# Level 3: Behaviors
Behavior PublishMessage
    Input:
        topic: string
        partitionKey: string
        payload: dict
        headers: dict
    Output:
        messageId: string
        sequenceNumber: integer
    Action:
        Validate topic exists and schema matches payload
        Determine partition from partition key hash
        Assign monotonic sequence number within partition
        Persist message to topic log
        Route message to all matching subscriptions
        Apply backpressure check per subscriber before delivery
        Return message ID and sequence number

Behavior Subscribe
    Input:
        topicPattern: string
        callback: string
        config: dict
    Output:
        subscriptionId: string
    Action:
        Validate topic pattern syntax and matching topics
        Register subscription in subscriber registry
        Initialize delivery buffer and backpressure controller
        Begin message delivery from current head
        Return subscription identifier

Behavior AcknowledgeMessage
    Input:
        subscriptionId: string
        messageId: string
        status: string
    Output:
        acknowledged: boolean
    Action:
        Verify message belongs to subscription's in-flight set
        If status is success, remove from in-flight and advance checkpoint
        If status is failure, apply retry policy or move to DLQ
        Update subscriber metrics
        Release backpressure slot
        Return acknowledgment confirmation

Behavior RouteToSubscriber
    Input:
        message: Message
        subscription: Subscription
    Output:
        delivered: boolean
    Action:
        Evaluate filter expression against message headers
        Check subscriber backpressure state
        If pressure is critical, route to DLQ with backpressure reason
        If pressure is moderate, apply throttle delay
        If acceptable, deliver to subscriber callback
        Track delivery in acknowledgment tracker
        Return delivery result

Behavior ProcessDeadLetter
    Input:
        entryId: string
        action: string
    Output:
        result: string
    Action:
        Load dead letter entry from DLQ store
        If action is retry, re-inject message to original subscription
        If action is discard, log and permanently remove
        If action is redirect, publish to alternate topic
        Update DLQ metrics and entry status
        Return action result

Behavior ReplayMessages
    Input:
        subscriptionId: string
        fromTimestamp: datetime
        toTimestamp: datetime
        maxThroughput: integer
    Output:
        replayId: string
        messageCount: integer
    Action:
        Validate subscription and time range
        Load messages from topic log for time range
        Apply max throughput throttling
        Deliver messages to subscriber callback
        Skip already-acknowledged messages
        Track replay progress and completion
        Return replay statistics

# Level 4: Conditions
Condition: BackpressureCritical
    When subscriber buffer reaches 90% of max capacity
    Then redirect new messages to DLQ with backpressure flag and alert subscriber owner

Condition: AcknowledgmentTimeout
    When message remains unacknowledged beyond ackTimeout
    Then redeliver message with incremented delivery attempt counter; after max attempts move to DLQ

Condition: InvalidMessageSchema
    When message payload does not conform to topic schema
    Then reject publication with validation error; if already published, route to DLQ

# Level 5: Events
Event: OnSubscriberRegistered
    On new subscription created for a topic pattern
    Action: Warm subscriber connection pool and validate callback endpoint reachability

Event: OnBackpressureThreshold
    On subscriber buffer crosses 75% capacity
    Action: Apply throttle, notify subscriber via health endpoint, and begin buffering upstream

Event: OnDeadLetterCreated
    On message moved to dead letter queue
    Action: Emit metric, send notification to subscriber owner, and log for audit trail

Event: OnReplayCompleted
    On message replay batch fully delivered
    Action: Log replay summary, update subscriber checkpoint, and emit completion metric

# Level 6: Concurrency
Parallel:
    Message publishing to multiple partitions concurrently
    Message delivery to independent subscribers in parallel
    Dead letter queue reprocessing on separate worker pool
    Backpressure monitoring and adjustment across all subscribers
    Metrics aggregation from all subscriber delivery threads

# Level 7: Optimization
Optimize: Message delivery throughput
    Priority: Batch deliveries per subscriber; minimize lock contention on partition sequencer

Optimize: Topic routing latency
    Priority: Cache subscription pattern matching results; pre-compile filter expressions

# Level 8: Learning
Learn: Subscriber processing capacity
    Goal: Predict optimal max-in-flight setting per subscriber to maximize throughput without overflow
    Adapt: Per-subscriber maxInFlight and buffer size
    Based: Subscriber acknowledgment rate and latency distribution over sliding window

Learn: DLQ root cause patterns
    Goal: Categorize dead-lettered messages by failure type to identify systemic issues
    Adapt: Alerting rules and auto-retry thresholds per failure category
    Based: Historical DLQ entry failure reason distribution and retry success rates

# Level 9: Security
Security:
    Encrypt: Message payload in transit via TLS 1.3 with pinned certificates between bus and subscribers
    Encrypt: DLQ entries at rest using AES-256 with per-topic encryption keys
    Protect: Topic publication with IAM-style authorization and topic-level ACL
    Protect: Subscription callbacks from message injection via HMAC message signing
    Protect: Replay endpoints from unauthorized access with admin role requirement and audit logging

# Level 10: Native
Rust
{
use std::collections::HashMap;
use std::sync::{Arc, Mutex};
use std::time::{Duration, Instant};

#[derive(Clone, Debug)]
pub struct Message {
    pub message_id: String,
    pub topic: String,
    pub partition_key: String,
    pub sequence_number: u64,
    pub payload: Vec<u8>,
    pub headers: HashMap<String, String>,
    pub published_at: Instant,
}

#[derive(Clone, Debug)]
pub struct Subscription {
    pub subscription_id: String,
    pub subscriber_name: String,
    pub topic_pattern: String,
    pub max_in_flight: usize,
    pub ack_timeout: Duration,
    pub retry_max: u32,
}

#[derive(Debug)]
pub enum DeliverStatus {
    Acknowledged,
    Failed(String),
    Timeout,
}

pub struct DeadLetterEntry {
    pub entry_id: String,
    pub original_message: Message,
    pub subscriber_name: String,
    pub failure_reason: String,
    pub failure_count: u32,
    pub first_failed_at: Instant,
    pub dead_lettered_at: Instant,
}

pub struct EventBus {
    topics: Arc<Mutex<HashMap<String, Vec<Message>>>>,
    subscriptions: Arc<Mutex<Vec<Subscription>>>,
    in_flight: Arc<Mutex<HashMap<String, (Message, Instant, u32)>>>,
    dead_letter_queue: Arc<Mutex<Vec<DeadLetterEntry>>>,
    max_buffer: usize,
}

impl EventBus {
    pub fn new(max_buffer: usize) -> Self {
        EventBus {
            topics: Arc::new(Mutex::new(HashMap::new())),
            subscriptions: Arc::new(Mutex::new(Vec::new())),
            in_flight: Arc::new(Mutex::new(HashMap::new())),
            dead_letter_queue: Arc::new(Mutex::new(Vec::new())),
            max_buffer,
        }
    }

    pub fn publish(&self, topic: &str, msg: Message) -> u64 {
        let mut topics = self.topics.lock().unwrap();
        let partition = topics.entry(topic.to_string()).or_insert_with(Vec::new);
        let seq = partition.len() as u64;
        let mut msg = msg;
        msg.sequence_number = seq;
        partition.push(msg.clone());
        seq
    }

    pub fn subscribe(&self, sub: Subscription) {
        let mut subs = self.subscriptions.lock().unwrap();
        subs.push(sub);
    }

    pub fn acknowledge(&self, subscription_id: &str, message_id: &str) -> bool {
        let mut in_flight = self.in_flight.lock().unwrap();
        in_flight.remove(&format!("{}:{}", subscription_id, message_id)).is_some()
    }

    pub fn check_timeouts(&self) {
        let mut in_flight = self.in_flight.lock().unwrap();
        let mut dlq = self.dead_letter_queue.lock().unwrap();
        let now = Instant::now();
        let expired: Vec<String> = in_flight.iter()
            .filter(|(_, (msg, sent_at, retries))| {
                now.duration_since(*sent_at) > Duration::from_secs(30) && *retries >= 3
            })
            .map(|(k, _)| k.clone())
            .collect();
        for key in expired {
            if let Some((msg, _, retries)) = in_flight.remove(&key) {
                dlq.push(DeadLetterEntry {
                    entry_id: format!("dlq-{}", msg.message_id),
                    original_message: msg,
                    subscriber_name: String::new(),
                    failure_reason: "ack_timeout_exceeded".to_string(),
                    failure_count: retries,
                    first_failed_at: now,
                    dead_lettered_at: now,
                });
            }
        }
    }

    pub fn dead_letter_count(&self) -> usize {
        self.dead_letter_queue.lock().unwrap().len()
    }
}

fn main() {
    let bus = EventBus::new(10_000);
    let sub = Subscription {
        subscription_id: "sub-1".to_string(),
        subscriber_name: "order-service".to_string(),
        topic_pattern: "orders.*".to_string(),
        max_in_flight: 50,
        ack_timeout: Duration::from_secs(30),
        retry_max: 3,
    };
    bus.subscribe(sub);
    let msg = Message {
        message_id: "msg-1".to_string(),
        topic: "orders.created".to_string(),
        partition_key: "order-123".to_string(),
        sequence_number: 0,
        payload: vec![],
        headers: HashMap::new(),
        published_at: Instant::now(),
    };
    let seq = bus.publish("orders.created", msg);
    println!("Published message with sequence: {}", seq);
}


Repository Pattern Data Layer

AICL Example: Repository Pattern Data Layer Implements a comprehensive Repository pattern data access layer with abstract data access, specification-based queries, unit of work for transactions, multi-level caching, and connection management.

Instruction: Create an AICL specification for a repository pattern data layer. Include all 10 language levels: architecture (goals, constraints, risks with recovery, layers), entities, behaviors, conditions, events, concurrency, optimization, learning, security, and native code.

Response:
# AICL Example: Repository Pattern Data Layer
# Implements a comprehensive Repository pattern data access layer with abstract data access,
# specification-based queries, unit of work for transactions, multi-level caching, and connection management.

# Level 1: Architecture
Goal: Build a repository pattern data access layer that abstracts persistence details,
    supports specification-based composable queries, manages transactions via unit of work,
    provides multi-level caching, and ensures data consistency across operations.

Constraint: Domain layer must never reference infrastructure persistence directly.
Constraint: All queries must be expressed as specification objects, not raw query strings.
Constraint: Each unit of work scope must correspond to exactly one transaction.
Constraint: Cache invalidation must occur within the same transaction as the write.
Constraint: Repository interfaces must be defined in the domain layer; implementations in infrastructure.

Risk: Cache returns stale data after write
Recovery: Invalidate cache entries within write transaction; use write-through for critical entities

Risk: Unit of work rollback leaves cache in inconsistent state
Recovery: Enroll cache in transaction participation; rollback cache operations on transaction abort

Risk: Specification composition creates N+1 query problem
Recovery: Detect eager-load annotations; auto-join related entities via specification visitor

Risk: Connection pool exhaustion under high concurrency
Recovery: Queue requests with timeout; scale pool dynamically based on wait-time metrics

Risk: Optimistic concurrency conflict on high-contention entities
Recovery: Retry with exponential backoff up to 3 times; escalate to pessimistic lock if threshold exceeded

Risk: Repository abstraction leaks persistence details through error types
Recovery: Translate all infrastructure exceptions to domain-specific error types at repository boundary

Layer: Domain
    SubLayer: RepositoryInterface
    SubLayer: Specification
    SubLayer: AggregateRoot
Layer: Application
    SubLayer: UnitOfWork
    SubLayer: TransactionManager
    SubLayer: SpecificationComposer
Layer: Infrastructure
    SubLayer: RepositoryImplementation
    SubLayer: CacheLayer
    SubLayer: ConnectionPool

Validation: Repository method must accept and return domain types only
Validation: Specification must be serializable for cache key generation
Validation: Unit of work must not span multiple database connections
Validation: Cache TTL must be less than or equal to source data freshness requirement
Validation: Connection pool size must match concurrent transaction limit
Validation: All repository write operations must participate in active unit of work

# Level 2: Entities
Entity Repository
    repositoryId: string
    aggregateType: string
    connectionString: string
    cacheEnabled: boolean
    cacheTtl: integer
    batchSize: integer
    readOnly: boolean
    maxRetryCount: integer

Entity Specification
    specificationId: string
    criteria: dict
    orderBy: list
    pageNumber: integer
    pageSize: integer
    includes: list
    cacheable: boolean
    cacheKey: string

Entity UnitOfWork
    unitOfWorkId: string
    transactionId: string
    state: string
    operations: list
    enrolledRepositories: list
    startedAt: datetime
    isolationLevel: string
    timeout: integer

Entity CacheEntry
    cacheKey: string
    value: any
    createdAt: datetime
    expiresAt: datetime
    region: string
    sizeBytes: integer
    hitCount: integer
    version: integer

Entity AggregateRoot
    aggregateId: string
    aggregateType: string
    version: integer
    createdAt: datetime
    modifiedAt: datetime
    createdBy: string
    modifiedBy: string
    isDirty: boolean

Entity QueryResult
    queryId: string
    items: list
    totalCount: integer
    pageNumber: integer
    pageSize: integer
    hasMore: boolean
    executionTime: integer
    cacheHit: boolean

# Level 3: Behaviors
Behavior FindBySpecification
    Input:
        repository: Repository
        specification: Specification
    Output:
        result: QueryResult
    Action:
        Generate cache key from specification hash
        Check L1 in-process cache for hit
        Check L2 distributed cache for hit
        If cache miss, translate specification to query
        Execute query against persistence store
        Hydrate domain entities from result set
        Populate both cache layers with results
        Return paginated query result with metadata

Behavior AddAggregate
    Input:
        repository: Repository
        aggregate: AggregateRoot
        unitOfWork: UnitOfWork
    Output:
        persisted: AggregateRoot
    Action:
        Validate aggregate invariants
        Enroll repository in unit of work if not already
        Add insert operation to unit of work change set
        Mark aggregate as persisted in identity map
        Invalidate cache entries for affected specifications
        Return aggregate with generated ID

Behavior UpdateAggregate
    Input:
        repository: Repository
        aggregate: AggregateRoot
        unitOfWork: UnitOfWork
    Output:
        updated: AggregateRoot
    Action:
        Check optimistic concurrency version match
        Add update operation to unit of work change set
        Increment aggregate version
        Invalidate cache for this aggregate and related queries
        Mark aggregate as clean in identity map
        Return updated aggregate

Behavior CommitUnitOfWork
    Input:
        unitOfWork: UnitOfWork
    Output:
        commitResult: string
    Action:
        Validate all enrolled repositories are in consistent state
        Begin database transaction if not already active
        Execute all operations in change set in order
        Flush all cache invalidations
        Commit transaction
        Update identity map with final state
        Notify change trackers
        Return commit acknowledgment

Behavior ComposeSpecification
    Input:
        left: Specification
        right: Specification
        compositionType: string
    Output:
        composed: Specification
    Action:
        Merge criteria based on composition type (AND, OR, NOT)
        Combine includes and deduplicate
        Resolve ordering conflicts with priority rules
        Recalculate cache key from merged specification
        Validate composed specification is satisfiable
        Return composed specification

Behavior InvalidateCache
    Input:
        region: string
        keys: list
        propagation: string
    Output:
        invalidated: integer
    Action:
        Remove entries from L1 in-process cache
        Remove entries from L2 distributed cache
        If propagation is immediate, block until L2 confirms
        If propagation is eventual, queue invalidation for async processing
        Track invalidated count for metrics
        Return count of invalidated entries

# Level 4: Conditions
Condition: ConcurrencyConflict
    When aggregate version at persist differs from loaded version
    Then raise concurrency exception with current version; suggest reload and retry strategy

Condition: CacheStampede
    When multiple concurrent requests cache-miss for the same key
    Then allow only one request to compute; others wait and share the computed result

Condition: TransactionTimeout
    When unit of work exceeds configured timeout duration
    Then rollback transaction, release locks, and invalidate any partially committed cache

# Level 5: Events
Event: OnCacheMiss
    On specification query results not found in either cache layer
    Action: Increment miss metric, trigger cache warm for related specifications

Event: OnTransactionCommitted
    On unit of work successfully committed to persistence store
    Action: Finalize cache invalidations, release identity map locks, emit domain events

Event: OnSpecificationExecuted
    On specification translated and executed against persistence
    Action: Log query performance, update slow-query metrics, suggest indexes if threshold exceeded

Event: OnConcurrencyRetry
    On optimistic concurrency conflict triggering automatic retry
    Action: Increment retry metric, log contention hot-spot, alert if retry count exceeds threshold

# Level 6: Concurrency
Parallel:
    L1 and L2 cache lookups executed concurrently
    Multiple repository read operations within same unit of work
    Cache invalidation propagation to distributed nodes
    Connection pool health monitoring and scaling
    Specification compilation and cache key generation

# Level 7: Optimization
Optimize: Query execution performance
    Priority: Compile specifications to parameterized queries; reuse execution plans; batch reads

Optimize: Cache hit ratio
    Priority: Pre-warm caches for top-accessed specifications; adjust TTL based on access frequency

# Level 8: Learning
Learn: Optimal cache TTL per entity type
    Goal: Maximize cache hit ratio while minimizing staleness for each aggregate type
    Adapt: Per-entity-type cache TTL values
    Based: Cache hit ratio, access frequency, and data mutation rate over sliding 1-hour window

Learn: Query plan optimization
    Goal: Identify slow specifications and suggest index improvements
    Adapt: Database index recommendations and specification rewrite hints
    Based: Query execution time distribution and full table scan detection

# Level 9: Security
Security:
    Encrypt: Database connection strings using vault-managed secrets with automatic rotation
    Encrypt: Cached PII fields using envelope encryption before storage in distributed cache
    Protect: Repository against SQL injection via parameterized query compilation from specifications
    Protect: Cache data from unauthorized access with per-region ACL and tenant isolation
    Protect: Unit of work operations from tampering via audit trail of all change set modifications

# Level 10: Native
CSharp
{
using System;
using System.Collections.Generic;
using System.Linq.Expressions;
using System.Threading.Tasks;

public interface ISpecification<T>
{
    Expression<Func<T, bool>> ToExpression();
    List<string> Includes { get; }
    bool IsCacheable { get; }
    string CacheKey { get; }
}

public class AndSpecification<T> : ISpecification<T>
{
    private readonly ISpecification<T> _left;
    private readonly ISpecification<T> _right;

    public AndSpecification(ISpecification<T> left, ISpecification<T> right)
    {
        _left = left;
        _right = right;
        Includes = new List<string>(_left.Includes);
        foreach (var inc in _right.Includes)
            if (!Includes.Contains(inc)) Includes.Add(inc);
    }

    public Expression<Func<T, bool>> ToExpression()
    {
        var leftExpr = _left.ToExpression();
        var rightExpr = _right.ToExpression();
        var param = Expression.Parameter(typeof(T));
        var body = Expression.AndAlso(
            Expression.Invoke(leftExpr, param),
            Expression.Invoke(rightExpr, param)
        );
        return Expression.Lambda<Func<T, bool>>(body, param);
    }

    public List<string> Includes { get; }
    public bool IsCacheable => _left.IsCacheable && _right.IsCacheable;
    public string CacheKey => $"{_left.CacheKey}&{_right.CacheKey}";
}

public interface IRepository<T> where T : class
{
    Task<T?> FindByIdAsync(Guid id);
    Task<QueryResult<T>> FindBySpecificationAsync(ISpecification<T> spec);
    Task AddAsync(T aggregate, IUnitOfWork uow);
    Task UpdateAsync(T aggregate, IUnitOfWork uow);
    Task DeleteAsync(T aggregate, IUnitOfWork uow);
}

public interface IUnitOfWork : IDisposable
{
    string UnitOfWorkId { get; }
    Task CommitAsync();
    Task RollbackAsync();
    void Enroll(string repositoryId);
}

public class QueryResult<T>
{
    public List<T> Items { get; set; } = new();
    public int TotalCount { get; set; }
    public int PageNumber { get; set; }
    public int PageSize { get; set; }
    public bool HasMore { get; set; }
    public long ExecutionTimeMs { get; set; }
    public bool CacheHit { get; set; }
}

public class CacheEntry<T>
{
    public string CacheKey { get; set; } = string.Empty;
    public T? Value { get; set; }
    public DateTime ExpiresAt { get; set; }
    public int HitCount { get; set; }
    public int Version { get; set; }
}

public class TwoLevelCache<T>
{
    private readonly Dictionary<string, CacheEntry<T>> _l1 = new();
    private readonly Dictionary<string, CacheEntry<T>> _l2 = new();
    private readonly object _lock = new();

    public bool TryGet(string key, out T? value)
    {
        lock (_lock)
        {
            if (_l1.TryGetValue(key, out var entry) && entry.ExpiresAt > DateTime.UtcNow)
            {
                entry.HitCount++;
                value = entry.Value;
                return true;
            }
            if (_l2.TryGetValue(key, out entry) && entry.ExpiresAt > DateTime.UtcNow)
            {
                entry.HitCount++;
                _l1[key] = entry;
                value = entry.Value;
                return true;
            }
        }
        value = default;
        return false;
    }

    public void Set(string key, T value, TimeSpan ttl)
    {
        lock (_lock)
        {
            var entry = new CacheEntry<T>
            {
                CacheKey = key, Value = value,
                ExpiresAt = DateTime.UtcNow.Add(ttl), Version = 1
            };
            _l1[key] = entry;
            _l2[key] = entry;
        }
    }

    public void Invalidate(string key)
    {
        lock (_lock) { _l1.Remove(key); _l2.Remove(key); }
    }

    public void InvalidateRegion(string prefix)
    {
        lock (_lock)
        {
            var keys = _l1.Keys.Where(k => k.StartsWith(prefix)).ToList();
            foreach (var k in keys) { _l1.Remove(k); _l2.Remove(k); }
        }
    }
}
}


Strategy Pattern Engine

AICL Example: Strategy Pattern Engine Implements a strategy pattern engine with dynamic algorithm selection, runtime strategy switching, performance benchmarking, strategy composition, and fallback mechanisms.

Instruction: Create an AICL specification for a strategy pattern engine. Include all 10 language levels: architecture (goals, constraints, risks with recovery, layers), entities, behaviors, conditions, events, concurrency, optimization, learning, security, and native code.

Response:
# AICL Example: Strategy Pattern Engine
# Implements a strategy pattern engine with dynamic algorithm selection, runtime strategy switching,
# performance benchmarking, strategy composition, and fallback mechanisms.

# Level 1: Architecture
Goal: Build a strategy pattern engine that enables runtime algorithm selection and switching,
    supports automated benchmarking for strategy comparison, allows strategy composition
    for complex operations, and provides graceful fallback when strategies fail.

Constraint: Each strategy must implement a common interface with identical input and output contracts.
Constraint: Strategy selection must be deterministic given the same context and input.
Constraint: Benchmarking must not affect production strategy execution latency.
Constraint: Strategy switching must be atomic with no in-flight operations using the old strategy.
Constraint: Composite strategies must maintain the execution order declared in composition.

Risk: Strategy switch during active execution causes inconsistent results
Recovery: Complete in-flight operations with old strategy; apply new strategy to subsequent requests only

Risk: Benchmark sample too small produces unreliable comparison
Recovery: Require minimum sample size of 1000 executions; extend benchmark until confidence interval narrows

Risk: Composite strategy partial failure leaves partial output
Recovery: Apply compensating actions for completed steps; fall back to simpler non-composite strategy

Risk: Memory leak from accumulated benchmark data
Recovery: Sliding window of 1 hour for benchmark samples; compress older data into summary statistics

Risk: Strategy selection oscillates between equivalent strategies causing instability
Recovery: Hysteresis threshold requiring 10% improvement over incumbent before switching

Risk: Custom strategy injection compromises system integrity
Recovery: Sandbox custom strategies with resource limits; validate against strategy contract at registration

Layer: Core
    SubLayer: StrategyRegistry
    SubLayer: StrategySelector
    SubLayer: StrategyExecutor
Layer: Benchmarking
    SubLayer: BenchmarkRunner
    SubLayer: PerformanceCollector
    SubLayer: ComparisonEngine
Layer: Composition
    SubLayer: CompositeStrategy
    SubLayer: FallbackChain
    SubLayer: StrategyMerger

Validation: Strategy must implement required execute method signature
Validation: Benchmark results must include confidence interval and sample size
Validation: Strategy context must contain all fields required by candidate strategies
Validation: Composite strategy steps must form a valid directed acyclic graph
Validation: Fallback chain must terminate with a guaranteed-success strategy
Validation: Strategy registration must pass all contract compliance tests

# Level 2: Entities
Entity Strategy
    strategyId: string
    strategyName: string
    strategyType: string
    version: string
    inputSchema: dict
    outputSchema: dict
    constraints: dict
    priority: integer
    isEnabled: boolean

Entity StrategyContext
    contextId: string
    operationType: string
    inputPayload: dict
    environment: dict
    preferences: dict
    deadline: datetime
    budget: float
    qualityRequirement: string

Entity BenchmarkResult
    benchmarkId: string
    strategyId: string
    sampleSize: integer
    meanLatency: float
    p50Latency: float
    p99Latency: float
    throughput: float
    errorRate: float
    confidenceInterval: dict
    executedAt: datetime

Entity CompositeStrategy
    compositeId: string
    name: string
    steps: list
    executionOrder: list
    mergeStrategy: string
    fallbackStrategy: string
    timeoutPerStep: integer
    allowPartialResults: boolean

Entity FallbackChain
    chainId: string
    operationType: string
    strategies: list
    currentPosition: integer
    failureThreshold: integer
    retryCount: integer
    lastSwitchAt: datetime

Entity PerformanceMetric
    metricId: string
    strategyId: string
    executionTime: float
    memoryUsed: integer
    cpuTime: float
    success: boolean
    timestamp: datetime
    inputSize: integer
    outputSize: integer

# Level 3: Behaviors
Behavior SelectStrategy
    Input:
        context: StrategyContext
        candidates: list
    Output:
        selectedStrategy: Strategy
        selectionReason: string
    Action:
        Filter candidates by enabled status and constraint compatibility
        Evaluate context preferences against strategy attributes
        Apply hysteresis to avoid oscillation with incumbent strategy
        Consider benchmark performance data for tie-breaking
        Apply priority ordering for final selection
        Return selected strategy with justification

Behavior ExecuteStrategy
    Input:
        strategy: Strategy
        context: StrategyContext
    Output:
        result: dict
        metrics: PerformanceMetric
    Action:
        Validate input against strategy schema
        Initialize execution timer and resource monitor
        Execute strategy algorithm with context payload
        Collect performance metrics during execution
        Validate output against strategy output schema
        Handle execution failure with fallback chain
        Return result and performance metrics

Behavior RunBenchmark
    Input:
        strategyIds: list
        sampleSize: integer
        context: StrategyContext
    Output:
        results: list
        winner: string
    Action:
        Generate representative test inputs for each strategy
        Execute each strategy for required sample size
        Collect latency, throughput, and error metrics
        Calculate statistical confidence intervals
        Compare strategies using paired statistical test
        Declare winner with confidence level
        Return benchmark results and recommendation

Behavior ComposeStrategies
    Input:
        stepStrategies: list
        mergeStrategy: string
        fallback: string
    Output:
        compositeId: string
    Action:
        Validate DAG ordering of step dependencies
        Verify output-input compatibility between adjacent steps
        Register composite strategy in registry
        Configure per-step timeout and fallback
        Set up merge strategy for combining step outputs
        Return composite strategy identifier

Behavior SwitchStrategy
    Input:
        operationType: string
        fromStrategy: string
        toStrategy: string
    Output:
        switchResult: string
    Action:
        Validate target strategy is registered and enabled
        Wait for in-flight executions to complete
        Atomically update strategy selector mapping
        Begin benchmarking new strategy in shadow mode
        Notify all consumers of strategy change
        Log switch with justification and timestamp

Behavior ExecuteFallbackChain
    Input:
        chain: FallbackChain
        context: StrategyContext
    Output:
        result: dict
        usedStrategy: string
    Action:
        Attempt execution at current chain position
        If failure, advance to next strategy in chain
        Track consecutive failures per strategy
        If terminal strategy fails, return degraded result
        Reset chain position on success
        Return result with strategy that succeeded

# Level 4: Conditions
Condition: StrategyExecutionTimeout
    When strategy execution exceeds deadline specified in context
    Then abort execution, record timeout metric, and invoke fallback chain

Condition: BenchmarkConfidenceInsufficient
    When benchmark confidence interval width exceeds 20% of mean
    Then extend benchmark by doubling sample size up to maximum limit

Condition: StrategyHealthDegraded
    When strategy error rate exceeds 5% over last 100 executions
    Then automatically demote strategy priority and trigger benchmark re-evaluation

# Level 5: Events
Event: OnStrategyRegistered
    On new strategy added to the registry
    Action: Run validation suite against contract; execute preliminary benchmark

Event: OnStrategySwitched
    On active strategy for an operation type changed
    Action: Log switch event, update monitoring dashboards, begin shadow benchmarking

Event: OnBenchmarkCompleted
    On benchmark run finished with statistical results
    Action: Publish results, trigger auto-switch if improvement exceeds hysteresis threshold

Event: OnFallbackActivated
    On fallback chain invoked due to strategy failure
    Action: Alert on-call team, increment fallback metric, schedule root cause investigation

# Level 6: Concurrency
Parallel:
    Strategy execution and benchmarking in shadow mode
    Multiple operation types selecting strategies concurrently
    Performance metric collection from all active strategy executions
    Fallback chain execution attempts without blocking main thread
    Benchmark sample collection across parallel strategy invocations

# Level 7: Optimization
Optimize: Strategy selection latency
    Priority: Cache selection results for identical contexts; pre-compute ranking for frequent operations

Optimize: Benchmark accuracy with minimal overhead
    Priority: Use stratified sampling; adjust sample rate based on variance observed

# Level 8: Learning
Learn: Optimal strategy per context profile
    Goal: Automatically discover which strategy performs best for different input characteristics
    Adapt: Strategy selection weights per operation type and context profile
    Based: Historical execution performance correlated with input features using multi-armed bandit

Learn: Benchmark sample size requirements
    Goal: Minimize benchmark overhead while maintaining statistical validity
    Adapt: Required sample size per strategy and operation type
    Based: Observed variance in execution times and desired confidence level

# Level 9: Security
Security:
    Encrypt: Strategy execution context containing sensitive parameters with field-level encryption
    Encrypt: Benchmark result payloads transmitted between nodes with TLS 1.3
    Protect: Strategy registry from unauthorized modifications with admin-only ACL and audit logging
    Protect: Custom strategy code execution via sandboxed runtime with CPU, memory, and I/O limits
    Protect: Strategy selection from manipulation by validating all benchmark data against signed sources

# Level 10: Native
Java
{
import java.util.*;
import java.util.concurrent.*;
import java.util.function.Function;

public interface Strategy<I, O> {
    String getId();
    String getName();
    O execute(I input) throws StrategyException;
    boolean canHandle(I input);
    int getPriority();
}

public class StrategyException extends Exception {
    public StrategyException(String msg, Throwable cause) { super(msg, cause); }
}

public class StrategySelector<I, O> {
    private final Map<String, Strategy<I, O>> registry = new ConcurrentHashMap<>();
    private final Map<String, String> activeStrategies = new ConcurrentHashMap<>();
    private final Map<String, BenchmarkResult> benchmarks = new ConcurrentHashMap<>();
    private final double hysteresisThreshold = 0.10;

    public void register(Strategy<I, O> strategy) {
        registry.put(strategy.getId(), strategy);
    }

    public Strategy<I, O> select(String operationType, I input) {
        List<Strategy<I, O>> candidates = registry.values().stream()
            .filter(Strategy::canHandle)
            .filter(s -> s.canHandle(input))
            .sorted(Comparator.comparingInt(Strategy::getPriority).reversed())
            .toList();

        if (candidates.isEmpty()) {
            throw new IllegalStateException("No strategy available for: " + operationType);
        }

        String currentId = activeStrategies.get(operationType);
        Strategy<I, O> best = candidates.get(0);

        if (currentId != null && !currentId.equals(best.getId())) {
            Strategy<I, O> current = registry.get(currentId);
            BenchmarkResult currentBench = benchmarks.get(currentId);
            BenchmarkResult bestBench = benchmarks.get(best.getId());
            if (currentBench != null && bestBench != null) {
                double improvement = (currentBench.meanLatency - bestBench.meanLatency)
                    / currentBench.meanLatency;
                if (improvement < hysteresisThreshold) {
                    best = current;
                }
            }
        }
        activeStrategies.put(operationType, best.getId());
        return best;
    }

    public O executeWithFallback(String operationType, I input, List<Strategy<I, O>> fallbacks) {
        Strategy<I, O> primary = select(operationType, input);
        try {
            return primary.execute(input);
        } catch (StrategyException e) {
            for (Strategy<I, O> fallback : fallbacks) {
                try {
                    return fallback.execute(input);
                } catch (StrategyException ignored) {}
            }
            throw new RuntimeException("All strategies failed", e);
        }
    }
}

public class BenchmarkRunner<I, O> {
    private final int defaultSampleSize = 1000;

    public BenchmarkResult run(Strategy<I, O> strategy, Function<Void, I> inputGenerator, int sampleSize) {
        List<Long> latencies = new ArrayList<>();
        int errors = 0;

        for (int i = 0; i < sampleSize; i++) {
            I input = inputGenerator.apply(null);
            long start = System.nanoTime();
            try {
                strategy.execute(input);
            } catch (StrategyException e) {
                errors++;
            }
            latencies.add(System.nanoTime() - start);
        }

        Collections.sort(latencies);
        long sum = latencies.stream().mapToLong(Long::longValue).sum();
        double mean = (double) sum / sampleSize;
        long p50 = latencies.get((int)(sampleSize * 0.50));
        long p99 = latencies.get((int)(sampleSize * 0.99));

        return new BenchmarkResult(
            strategy.getId(), sampleSize, mean, p50, p99,
            (double) errors / sampleSize
        );
    }
}

record BenchmarkResult(
    String strategyId, int sampleSize, double meanLatency,
    long p50Latency, long p99Latency, double errorRate
) {}
}


Factory Pattern System

AICL Example: Factory Pattern System Implements a comprehensive factory pattern system with abstract factory, builder pattern, prototype-based cloning, lazy initialization, and dependency injection container integration.

Instruction: Create an AICL specification for a factory pattern system. Include all 10 language levels: architecture (goals, constraints, risks with recovery, layers), entities, behaviors, conditions, events, concurrency, optimization, learning, security, and native code.

Response:
# AICL Example: Factory Pattern System
# Implements a comprehensive factory pattern system with abstract factory, builder pattern,
# prototype-based cloning, lazy initialization, and dependency injection container integration.

# Level 1: Architecture
Goal: Build a factory pattern system that provides abstract factories for product families,
    builder pattern for complex object construction, prototype registry for efficient cloning,
    lazy initialization for deferred resource allocation, and DI container integration for
    automatic dependency resolution and lifecycle management.

Constraint: Factories must return abstract types; concrete types never exposed to consumers.
Constraint: Builder steps must be validated in order before build() is called.
Constraint: Prototype clones must be deep copies with no shared mutable references.
Constraint: Lazy instances must be thread-safe on first access.
Constraint: DI container must detect circular dependencies at registration time.

Risk: Circular dependency causes infinite resolution loop
Recovery: Detect cycles via graph traversal at registration; throw descriptive error with dependency chain

Risk: Prototype shallow clone leaks shared state
Recovery: Enforce deep copy validation via serialization round-trip test at prototype registration

Risk: Builder incomplete construction produces invalid object
Recovery: Validate all required fields before build; throw with list of missing fields

Risk: Lazy initialization race condition creates duplicate instances
Recovery: Use double-checked locking with volatile reference; only one thread creates instance

Risk: Factory produces object with unresolved dependencies
Recovery: Validate dependency graph completeness before factory invocation; fail-fast with missing list

Risk: Memory leak from singleton accumulation in DI container
Recovery: Implement disposable lifecycle; container shutdown triggers dispose on all singletons

Layer: Factory
    SubLayer: AbstractFactory
    SubLayer: ConcreteFactory
    SubLayer: FactoryRegistry
Layer: Builder
    SubLayer: StepBuilder
    SubLayer: ValidationEngine
    SubLayer: ObjectAssembler
Layer: Prototype
    SubLayer: PrototypeRegistry
    SubLayer: CloneEngine
Layer: DI
    SubLayer: Container
    SubLayer: LifecycleManager
    SubLayer: DependencyResolver

Validation: Factory output must implement declared abstract interface
Validation: Builder must have all required steps completed before build
Validation: Prototype clone must pass deep-copy equality test (original != clone, values equal)
Validation: Lazy initializer must produce exactly one instance across concurrent access
Validation: DI registration must not introduce circular dependency
Validation: Singleton lifecycle must include dispose method for container shutdown

# Level 2: Entities
Entity FactoryRegistration
    registrationId: string
    abstractType: string
    concreteType: string
    scope: string
    dependencies: list
    creationMethod: string
    isEnabled: boolean
    priority: integer

Entity BuilderStep
    stepId: string
    builderType: string
    stepName: string
    fieldName: string
    fieldType: string
    isRequired: boolean
    defaultValue: any
    validationRule: dict
    stepOrder: integer

Entity PrototypeEntry
    prototypeId: string
    prototypeType: string
    instance: any
    cloneMethod: string
    registrationTime: datetime
    cloneCount: integer
    lastClonedAt: datetime
    deepCopyValidated: boolean

Entity LazyInstance
    instanceId: string
    instanceType: string
    factoryMethod: string
    isCreated: boolean
    instance: any
    createdAt: datetime
    accessCount: integer
    creationDuration: integer

Entity DIRegistration
    registrationId: string
    serviceType: string
    implementationType: string
    lifetime: string
    factory: string
    dependencies: list
    isResolved: boolean
    instance: any

Entity DependencyGraph
    graphId: string
    nodes: list
    edges: list
    hasCycle: boolean
    cyclePath: list
    resolutionOrder: list
    totalRegistrations: integer

# Level 3: Behaviors
Behavior CreateViaFactory
    Input:
        abstractType: string
        parameters: dict
    Output:
        instance: any
    Action:
        Look up concrete factory for abstract type
        Resolve factory dependencies via DI container
        Validate parameters against factory contract
        Create instance via factory method
        Inject post-construction dependencies
        Register instance in lifecycle manager if scoped
        Return created instance

Behavior BuildObject
    Input:
        builderType: string
        steps: dict
    Output:
        instance: any
    Action:
        Validate all required steps have been provided
        Apply step values in declared order
        Run validation rules for each field
        Apply default values for optional missing fields
        Assemble final object from accumulated state
        Run cross-field validation on assembled object
        Return built instance

Behavior ClonePrototype
    Input:
        prototypeType: string
        overrides: dict
    Output:
        clonedInstance: any
    Action:
        Look up prototype in registry
        Execute deep clone via registered method
        Apply any override values to cloned instance
        Validate cloned instance integrity
        Update prototype clone metrics
        Return cloned instance

Behavior ResolveLazy
    Input:
        instanceId: string
    Output:
        instance: any
    Action:
        Check if instance already created
        If not created, acquire creation lock
        Double-check instance still null
        Execute factory method to create instance
        Set created flag and store instance
        Release creation lock
        Increment access counter
        Return instance

Behavior RegisterDependency
    Input:
        serviceType: string
        implementationType: string
        lifetime: string
    Output:
        registrationId: string
    Action:
        Validate service and implementation types
        Analyze constructor for dependency requirements
        Add node to dependency graph
        Check for circular dependency introduction
        If cycle detected, reject registration with cycle path
        Store registration in container
        Return registration ID

Behavior ResolveDependency
    Input:
        serviceType: string
    Output:
        instance: any
    Action:
        Look up registration for service type
        If singleton and already resolved, return cached instance
        If scoped, return from current scope
        If transient, create new instance
        Recursively resolve constructor dependencies
        Invoke factory or constructor with resolved deps
        Cache instance if singleton or scoped
        Return resolved instance

# Level 4: Conditions
Condition: MissingRequiredBuilderStep
    When build() called without all required steps completed
    Then throw BuilderIncompleteException listing missing required field names

Condition: DependencyCycleDetected
    When registration creates cycle in dependency graph
    Then reject registration with error showing full cycle path from root back to cycle entry

Condition: PrototypeCloneFailure
    When deep clone produces instance that shares mutable references with original
    Then reject clone, log validation failure, and fall back to serialization-based clone

# Level 5: Events
Event: OnFactoryRegistered
    On new factory mapping registered in factory registry
    Action: Validate factory contract, run smoke test, update dependency graph

Event: OnInstanceCreated
    On factory or builder successfully produces a new instance
    Action: Track instance in lifecycle manager, emit creation metric

Event: OnPrototypeCloned
    On prototype deep clone operation completed
    Action: Increment clone counter, validate clone integrity, update metrics

Event: OnLazyInitialized
    On lazy instance first accessed and created
    Action: Log creation duration, validate instance, alert if creation exceeds threshold

# Level 6: Concurrency
Parallel:
    Multiple factory invocations creating independent instances
    Builder step validation for different builder instances
    Lazy instance resolution with double-checked locking
    Dependency graph cycle detection for concurrent registrations
    Prototype clone operations for independent prototypes

# Level 7: Optimization
Optimize: Instance creation throughput
    Priority: Cache reflection results for factory methods; pool expensive resource allocations

Optimize: Dependency resolution speed
    Priority: Pre-compute resolution order at registration time; cache singleton instances eagerly

# Level 8: Learning
Learn: Factory creation cost profiles
    Goal: Identify expensive factory methods for optimization or lazy initialization
    Adapt: Switch eager factories to lazy when creation cost exceeds threshold
    Based: Instance creation duration distribution and access frequency per type

Learn: Optimal DI lifetime choices
    Goal: Suggest singleton vs scoped vs transient based on actual usage patterns
    Adapt: Lifetime recommendations per registration
    Based: Instance access patterns, state mutation frequency, and concurrency requirements

# Level 9: Security
Security:
    Encrypt: Factory configuration containing connection strings and credentials with vault integration
    Encrypt: Prototype instances containing sensitive data before storage using AES-256
    Protect: DI container from unauthorized registration via signed assembly validation
    Protect: Factory methods from injection attacks by validating all input parameters against schema
    Protect: Lazy initialization from resource exhaustion by enforcing per-type instance limits

# Level 10: Native
Kotlin
{
import java.util.concurrent.ConcurrentHashMap
import java.util.concurrent.atomic.AtomicReference
import java.util.concurrent.locks.ReentrantLock

interface Product {
    val id: String
    val type: String
    fun validate(): Boolean
}

data class ConcreteProductA(
    override val id: String,
    override val type: String = "A",
    val name: String,
    val value: Int,
    val metadata: Map<String, String> = emptyMap()
) : Product {
    override fun validate(): Boolean = id.isNotBlank() && name.isNotBlank()
}

data class ConcreteProductB(
    override val id: String,
    override val type: String = "B",
    val label: String,
    val score: Double,
    val tags: Set<String> = emptySet()
) : Product {
    override fun validate(): Boolean = id.isNotBlank() && label.isNotBlank()
}

interface AbstractFactory {
    fun create(parameters: Map<String, Any>): Product
}

class ProductAFactory : AbstractFactory {
    override fun create(parameters: Map<String, Any>): Product {
        return ConcreteProductA(
            id = parameters["id"] as String,
            name = parameters["name"] as String,
            value = parameters["value"] as Int,
            metadata = parameters["metadata"] as? Map<String, String> ?: emptyMap()
        )
    }
}

class ProductBFactory : AbstractFactory {
    override fun create(parameters: Map<String, Any>): Product {
        return ConcreteProductB(
            id = parameters["id"] as String,
            label = parameters["label"] as String,
            score = parameters["score"] as Double,
            tags = parameters["tags"] as? Set<String> ?: emptySet()
        )
    }
}

class ProductBuilder<T : Product>(private val factory: AbstractFactory) {
    private val steps = mutableMapOf<String, Any>()
    private val required = mutableListOf<String>()

    fun withStep(name: String, value: Any, isRequired: Boolean = false): ProductBuilder<T> {
        steps[name] = value
        if (isRequired) required.add(name)
        return this
    }

    fun build(): T {
        val missing = required.filter { it !in steps.keys }
        require(missing.isEmpty()) { "Missing required steps: $missing" }
        return factory.create(steps) as T
    }
}

class PrototypeRegistry {
    private val prototypes = ConcurrentHashMap<String, Product>()

    fun register(key: String, prototype: Product) {
        val clone = deepClone(prototype)
        require(clone !== prototype) { "Shallow clone detected for $key" }
        prototypes[key] = prototype
    }

    fun clone(key: String, overrides: Map<String, Any> = emptyMap()): Product {
        val original = prototypes[key] ?: throw IllegalArgumentException("No prototype: $key")
        val cloned = deepClone(original)
        return applyOverrides(cloned, overrides)
    }

    private fun deepClone(product: Product): Product = when (product) {
        is ConcreteProductA -> product.copy(metadata = product.metadata.toMap())
        is ConcreteProductB -> product.copy(tags = product.tags.toSet())
        else -> throw IllegalArgumentException("Unknown product type")
    }

    private fun applyOverrides(product: Product, overrides: Map<String, Any>): Product {
        return product
    }
}

class LazyInitializer<T>(private val factory: () -> T) {
    private val ref = AtomicReference<T?>()
    private val lock = ReentrantLock()

    val instance: T
        get() {
            ref.get()?.let { return it }
            lock.lock()
            try {
                ref.get()?.let { return it }
                val created = factory()
                ref.set(created)
                return created
            } finally {
                lock.unlock()
            }
        }
}

class DIContainer {
    private val registrations = mutableMapOf<Class<*>, DIReg<*>>()
    private val singletons = ConcurrentHashMap<Class<*>, Any>()

    data class DIReg<T>(
        val type: Class<T>,
        val factory: (DIContainer) -> T,
        val lifetime: Lifetime
    )

    enum class Lifetime { SINGLETON, SCOPPED, TRANSIENT }

    inline fun <reified T> register(noinline factory: (DIContainer) -> T, lifetime: Lifetime) {
        registrations[T::class.java] = DIReg(T::class.java, factory, lifetime)
    }

    @Suppress("UNCHECKED_CAST")
    fun <T> resolve(type: Class<T>): T {
        val reg = registrations[type] as? DIReg<T>
            ?: throw IllegalArgumentException("No registration for ${type.name}")
        return when (reg.lifetime) {
            Lifetime.SINGLETON -> singletons.getOrPut(type) { reg.factory(this) } as T
            Lifetime.TRANSIENT -> reg.factory(this)
            Lifetime.SCOPPED -> reg.factory(this)
        }
    }
}

fun main() {
    val registry = PrototypeRegistry()
    registry.register("productA", ConcreteProductA("proto-1", name = "Prototype", value = 42))
    val cloned = registry.clone("productA")
    println("Cloned: $cloned")

    val lazy = LazyInitializer { ConcreteProductB("lazy-1", label = "Lazy", score = 99.9) }
    println("Lazy instance: ${lazy.instance}")
}