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// Bounded Contexts
sealed class BoundedContext {
    object AgentManagement : BoundedContext()
    object ModelServing : BoundedContext()
    object KnowledgeManagement : BoundedContext()
    object WorkflowOrchestration : BoundedContext()
    object UserManagement : BoundedContext()
    object BillingAndMetering : BoundedContext()
}

// Aggregate Example
class AgentAggregate(
    val id: AgentId,
    val name: String,
    val owner: UserId,
    val capabilities: Set<Capability>,
    val state: AgentState
) {
    fun execute(command: Command): List<Event> {
        return when (command) {
            is StartAgent -> handleStart()
            is StopAgent -> handleStop()
            is UpdateCapabilities -> handleUpdate(command)
        }
    }
}

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# Event Bus Configuration
event-bus:
  type: kafka
  topics:
    - agent.created
    - agent.started
    - agent.stopped
    - model.loaded
    - model.inference.completed
    - knowledge.indexed
    - workflow.executed
  
  partitions: 32
  replication-factor: 3
  retention-ms: 604800000  # 7 days

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// Command Side
interface AgentCommandService {
    suspend fun createAgent(cmd: CreateAgentCommand): AgentId
    suspend fun updateAgent(cmd: UpdateAgentCommand)
    suspend fun deleteAgent(cmd: DeleteAgentCommand)
}

// Query Side
interface AgentQueryService {
    suspend fun getAgent(id: AgentId): Agent?
    suspend fun searchAgents(criteria: SearchCriteria): List<Agent>
    suspend fun getAgentMetrics(id: AgentId): AgentMetrics
}

// Event Sourcing
class AgentEventStore {
    suspend fun append(events: List<AgentEvent>) {
        events.forEach { event ->
            // Store in event store
            eventStore.append(event)
            // Publish to event bus
            eventBus.publish(event)
        }
    }
}

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// Domain Layer (Core)
interface AgentRepository {
    suspend fun save(agent: Agent)
    suspend fun findById(id: AgentId): Agent?
}

// Application Layer
class AgentUseCase(
    private val repository: AgentRepository,
    private val eventBus: EventBus
) {
    suspend fun createAgent(request: CreateAgentRequest) {
        val agent = Agent.create(request)
        repository.save(agent)
        eventBus.publish(AgentCreated(agent))
    }
}

// Infrastructure Layer (Adapters)
class PostgresAgentRepository : AgentRepository {
    override suspend fun save(agent: Agent) {
        // PostgreSQL implementation
    }
}

class MongoAgentRepository : AgentRepository {
    override suspend fun save(agent: Agent) {
        // MongoDB implementation
    }
}

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// Agent Service Architecture
@Service
class AgentService(
    private val masterAgent: MasterAgent,
    private val agentRegistry: AgentRegistry,
    private val metricsCollector: MetricsCollector
) {
    // Agent Lifecycle Management
    suspend fun createAgent(spec: AgentSpec): Agent {
        // Validate specification
        spec.validate()
        
        // Create agent instance
        val agent = when (spec.type) {
            AgentType.CONVERSATIONAL -> ConversationalAgent(spec)
            AgentType.AUTONOMOUS -> AutonomousAgent(spec)
            AgentType.REACTIVE -> ReactiveAgent(spec)
        }
        
        // Register with Master Agent
        masterAgent.register(agent)
        
        // Start metrics collection
        metricsCollector.track(agent)
        
        return agent
    }
    
    // Agent Execution
    suspend fun executeTask(agentId: String, task: Task): TaskResult {
        val agent = agentRegistry.get(agentId)
        
        return withContext(Dispatchers.IO) {
            // Execute with timeout
            withTimeout(task.timeout) {
                agent.execute(task)
            }
        }
    }
}

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# Model Service Architecture (Python for ML ecosystem)
from typing import Optional, List, Dict, Any
import asyncio
from dataclasses import dataclass
from abc import ABC, abstractmethod

@dataclass
class ModelSpec:
    name: str
    version: str
    provider: str  # ollama, openai, anthropic, local
    config: Dict[str, Any]

class ModelService:
    def __init__(self):
        self.model_registry = ModelRegistry()
        self.model_cache = ModelCache()
        self.metrics = MetricsCollector()
    
    async def load_model(self, spec: ModelSpec) -> Model:
        """Load model with intelligent caching"""
        # Check cache first
        cached = await self.model_cache.get(spec.cache_key)
        if cached:
            return cached
        
        # Load based on provider
        model = await self._load_by_provider(spec)
        
        # Cache for future use
        await self.model_cache.put(spec.cache_key, model)
        
        return model
    
    async def infer(self, 
                   model_id: str, 
                   input_data: Any,
                   params: Optional[Dict] = None) -> Any:
        """Run inference with automatic batching"""
        model = await self.get_model(model_id)
        
        # Batch if possible
        if model.supports_batching:
            return await self._batch_infer(model, input_data, params)
        else:
            return await model.infer(input_data, params)
    
    async def _batch_infer(self, model, input_data, params):
        """Intelligent batching for efficiency"""
        batch = self.batch_manager.add(model.id, input_data, params)
        
        if batch.is_ready:
            results = await model.batch_infer(batch.items)
            batch.distribute_results(results)
        
        return await batch.get_result_for(input_data)

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// Knowledge Service Architecture (Go for performance)
package knowledge

import (
    "context"
    "encoding/json"
    "github.com/aimatrix/vector"
    "github.com/neo4j/neo4j-go-driver"
)

type KnowledgeService struct {
    vectorDB   *vector.Client
    graphDB    neo4j.Driver
    docStore   DocumentStore
    embedder   Embedder
}

// Document Processing Pipeline
func (ks *KnowledgeService) ProcessDocument(
    ctx context.Context, 
    doc Document,
) (*ProcessedDocument, error) {
    // 1. Extract text and metadata
    extracted, err := ks.extractContent(doc)
    if err != nil {
        return nil, err
    }
    
    // 2. Chunk document
    chunks := ks.chunkDocument(extracted, ChunkConfig{
        Size:    512,
        Overlap: 128,
        Strategy: "semantic",
    })
    
    // 3. Generate embeddings
    embeddings, err := ks.embedder.EmbedBatch(ctx, chunks)
    if err != nil {
        return nil, err
    }
    
    // 4. Store in vector database
    for i, chunk := range chunks {
        ks.vectorDB.Upsert(ctx, &vector.Point{
            ID:       chunk.ID,
            Vector:   embeddings[i],
            Metadata: chunk.Metadata,
        })
    }
    
    // 5. Update knowledge graph
    err = ks.updateKnowledgeGraph(ctx, doc, chunks)
    if err != nil {
        return nil, err
    }
    
    return &ProcessedDocument{
        ID:         doc.ID,
        ChunkCount: len(chunks),
        Status:     "indexed",
    }, nil
}

// RAG Implementation
func (ks *KnowledgeService) RetrieveAugmentedGeneration(
    ctx context.Context,
    query string,
    config RAGConfig,
) (*RAGResponse, error) {
    // 1. Embed query
    queryEmbedding, err := ks.embedder.Embed(ctx, query)
    if err != nil {
        return nil, err
    }
    
    // 2. Vector search
    vectorResults, err := ks.vectorDB.Search(ctx, &vector.SearchRequest{
        Vector: queryEmbedding,
        TopK:   config.TopK,
        Filter: config.Filter,
    })
    
    // 3. Graph traversal for context expansion
    graphContext, err := ks.expandWithGraph(ctx, vectorResults)
    
    // 4. Rerank results
    reranked := ks.rerank(query, append(vectorResults, graphContext...))
    
    // 5. Build augmented context
    augmentedContext := ks.buildContext(reranked, config.MaxTokens)
    
    return &RAGResponse{
        Context:    augmentedContext,
        Sources:    reranked.GetSources(),
        Confidence: reranked.GetConfidence(),
    }, nil
}

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-- Core Agent Tables
CREATE TABLE agents (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    name VARCHAR(255) NOT NULL,
    type VARCHAR(50) NOT NULL,
    owner_id UUID NOT NULL,
    workspace_id UUID NOT NULL,
    capabilities JSONB NOT NULL DEFAULT '[]',
    config JSONB NOT NULL DEFAULT '{}',
    state VARCHAR(50) NOT NULL DEFAULT 'created',
    created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
    updated_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
    
    CONSTRAINT fk_owner FOREIGN KEY (owner_id) REFERENCES users(id),
    CONSTRAINT fk_workspace FOREIGN KEY (workspace_id) REFERENCES workspaces(id),
    INDEX idx_agent_owner (owner_id),
    INDEX idx_agent_workspace (workspace_id),
    INDEX idx_agent_state (state)
);

-- Agent Execution History
CREATE TABLE agent_executions (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    agent_id UUID NOT NULL,
    task_id UUID NOT NULL,
    input JSONB NOT NULL,
    output JSONB,
    tokens_used INTEGER,
    execution_time_ms INTEGER,
    status VARCHAR(50) NOT NULL,
    error TEXT,
    started_at TIMESTAMPTZ NOT NULL,
    completed_at TIMESTAMPTZ,
    
    CONSTRAINT fk_agent FOREIGN KEY (agent_id) REFERENCES agents(id),
    INDEX idx_execution_agent (agent_id),
    INDEX idx_execution_status (status),
    INDEX idx_execution_time (started_at DESC)
) PARTITION BY RANGE (started_at);

-- Create monthly partitions
CREATE TABLE agent_executions_2025_01 PARTITION OF agent_executions
    FOR VALUES FROM ('2025-01-01') TO ('2025-02-01');

-- Knowledge Base Tables
CREATE TABLE documents (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    workspace_id UUID NOT NULL,
    source_type VARCHAR(50) NOT NULL, -- file, url, api
    source_url TEXT,
    content_hash VARCHAR(64) NOT NULL,
    metadata JSONB NOT NULL DEFAULT '{}',
    processing_status VARCHAR(50) NOT NULL DEFAULT 'pending',
    chunk_count INTEGER DEFAULT 0,
    vector_ids UUID[] DEFAULT '{}',
    created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
    processed_at TIMESTAMPTZ,
    
    CONSTRAINT fk_workspace FOREIGN KEY (workspace_id) REFERENCES workspaces(id),
    INDEX idx_doc_workspace (workspace_id),
    INDEX idx_doc_status (processing_status),
    INDEX idx_doc_hash (content_hash)
);

-- Vector Store Metadata (actual vectors in Pinecone/Weaviate)
CREATE TABLE vector_metadata (
    id UUID PRIMARY KEY,
    document_id UUID NOT NULL,
    chunk_index INTEGER NOT NULL,
    chunk_text TEXT NOT NULL,
    embedding_model VARCHAR(100) NOT NULL,
    metadata JSONB NOT NULL DEFAULT '{}',
    created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
    
    CONSTRAINT fk_document FOREIGN KEY (document_id) REFERENCES documents(id),
    INDEX idx_vector_doc (document_id),
    INDEX idx_vector_created (created_at DESC)
);

-- Workflow Tables
CREATE TABLE workflows (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    workspace_id UUID NOT NULL,
    name VARCHAR(255) NOT NULL,
    description TEXT,
    definition JSONB NOT NULL, -- Workflow DSL
    triggers JSONB NOT NULL DEFAULT '[]',
    is_active BOOLEAN DEFAULT true,
    version INTEGER NOT NULL DEFAULT 1,
    created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
    updated_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
    
    CONSTRAINT fk_workspace FOREIGN KEY (workspace_id) REFERENCES workspaces(id),
    INDEX idx_workflow_workspace (workspace_id),
    INDEX idx_workflow_active (is_active)
);

-- P2P Compute Network
CREATE TABLE compute_nodes (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    node_id VARCHAR(255) UNIQUE NOT NULL,
    owner_id UUID NOT NULL,
    capabilities JSONB NOT NULL, -- GPU, CPU, RAM specs
    availability JSONB NOT NULL, -- Schedule
    reputation_score DECIMAL(3,2) DEFAULT 0.00,
    total_jobs_completed INTEGER DEFAULT 0,
    total_credits_earned DECIMAL(10,2) DEFAULT 0,
    status VARCHAR(50) NOT NULL DEFAULT 'offline',
    last_heartbeat TIMESTAMPTZ,
    created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
    
    CONSTRAINT fk_owner FOREIGN KEY (owner_id) REFERENCES users(id),
    INDEX idx_compute_owner (owner_id),
    INDEX idx_compute_status (status),
    INDEX idx_compute_heartbeat (last_heartbeat)
);

-- Job Queue for P2P Network
CREATE TABLE compute_jobs (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    workspace_id UUID NOT NULL,
    job_type VARCHAR(50) NOT NULL,
    requirements JSONB NOT NULL,
    payload JSONB NOT NULL,
    assigned_node_id UUID,
    status VARCHAR(50) NOT NULL DEFAULT 'pending',
    priority INTEGER DEFAULT 5,
    max_retries INTEGER DEFAULT 3,
    retry_count INTEGER DEFAULT 0,
    credits_offered DECIMAL(10,2) NOT NULL,
    created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
    assigned_at TIMESTAMPTZ,
    completed_at TIMESTAMPTZ,
    
    CONSTRAINT fk_workspace FOREIGN KEY (workspace_id) REFERENCES workspaces(id),
    CONSTRAINT fk_node FOREIGN KEY (assigned_node_id) REFERENCES compute_nodes(id),
    INDEX idx_job_status (status),
    INDEX idx_job_priority (priority DESC, created_at ASC),
    INDEX idx_job_node (assigned_node_id)
);

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// Agent Configuration Collection
{
  _id: ObjectId("..."),
  agentId: "uuid",
  version: 1,
  prompts: {
    system: "You are a helpful assistant...",
    examples: [
      { input: "...", output: "..." }
    ]
  },
  tools: [
    {
      name: "web_search",
      description: "Search the web",
      parameters: { ... },
      implementation: "plugin:web_search:v1"
    }
  ],
  memory: {
    type: "conversation",
    maxTokens: 4096,
    summaryStrategy: "progressive"
  },
  modelPreferences: [
    { task: "general", model: "gpt-4" },
    { task: "code", model: "claude-3" },
    { task: "vision", model: "gemini-vision" }
  ],
  createdAt: ISODate("..."),
  updatedAt: ISODate("...")
}

// Conversation History Collection
{
  _id: ObjectId("..."),
  sessionId: "uuid",
  agentId: "uuid",
  userId: "uuid",
  messages: [
    {
      id: "msg_1",
      role: "user",
      content: "Hello",
      timestamp: ISODate("..."),
      metadata: { ... }
    },
    {
      id: "msg_2",
      role: "assistant",
      content: "Hi! How can I help?",
      timestamp: ISODate("..."),
      tokensUsed: 15,
      modelUsed: "gpt-4",
      metadata: { ... }
    }
  ],
  summary: "User greeted the assistant...",
  tags: ["greeting", "general"],
  createdAt: ISODate("..."),
  lastActivityAt: ISODate("...")
}

// Knowledge Graph Nodes (Neo4j Representation in JSON)
{
  _id: ObjectId("..."),
  nodeId: "uuid",
  labels: ["Document", "TechnicalSpec"],
  properties: {
    title: "System Architecture",
    source: "internal",
    confidenceScore: 0.95,
    lastVerified: ISODate("...")
  },
  relationships: [
    {
      type: "REFERENCES",
      targetId: "uuid",
      properties: { weight: 0.8 }
    },
    {
      type: "CONTRADICTS",
      targetId: "uuid",
      properties: { reason: "..." }
    }
  ],
  embeddings: {
    model: "text-embedding-ada-002",
    vector: [0.123, -0.456, ...], // 1536 dimensions
    generatedAt: ISODate("...")
  }
}

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# Kubernetes HPA Configuration
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: agent-service-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: agent-service
  minReplicas: 3
  maxReplicas: 100
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80
  - type: Pods
    pods:
      metric:
        name: agent_queue_depth
      target:
        type: AverageValue
        averageValue: "30"
  behavior:
    scaleUp:
      stabilizationWindowSeconds: 60
      policies:
      - type: Percent
        value: 100
        periodSeconds: 60
    scaleDown:
      stabilizationWindowSeconds: 300
      policies:
      - type: Percent
        value: 50
        periodSeconds: 60

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# NGINX Plus Configuration for Smart Load Balancing
upstream agent_service {
    zone agent_service_zone 64k;
    
    # Least connections with health checks
    least_conn;
    
    # Backend servers with weights
    server agent-1.internal:8080 weight=5 max_fails=3 fail_timeout=30s;
    server agent-2.internal:8080 weight=5 max_fails=3 fail_timeout=30s;
    server agent-3.internal:8080 weight=3 max_fails=3 fail_timeout=30s;
    
    # Sticky sessions for WebSocket
    ip_hash;
    
    # Health check
    health_check interval=5s fails=3 passes=2 uri=/health;
}

# Rate limiting per user
limit_req_zone $binary_remote_addr zone=api_limit:10m rate=100r/s;
limit_req_zone $http_x_api_key zone=key_limit:10m rate=1000r/s;

server {
    listen 443 ssl http2;
    server_name api.aimatrix.com;
    
    # SSL configuration
    ssl_certificate /etc/nginx/ssl/aimatrix.crt;
    ssl_certificate_key /etc/nginx/ssl/aimatrix.key;
    ssl_protocols TLSv1.2 TLSv1.3;
    
    # Rate limiting
    limit_req zone=api_limit burst=50 nodelay;
    limit_req zone=key_limit burst=200 nodelay;
    
    # WebSocket support
    location /ws {
        proxy_pass http://agent_service;
        proxy_http_version 1.1;
        proxy_set_header Upgrade $http_upgrade;
        proxy_set_header Connection "upgrade";
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
        proxy_read_timeout 86400;
    }
    
    # API endpoints
    location /api {
        proxy_pass http://agent_service;
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
        
        # Response caching
        proxy_cache api_cache;
        proxy_cache_valid 200 1m;
        proxy_cache_key "$scheme$request_method$host$request_uri$http_x_api_key";
    }
}

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// Sharding Configuration
class ShardingStrategy {
    private val shardCount = 16
    
    fun getShardKey(workspaceId: String): Int {
        return workspaceId.hashCode() % shardCount
    }
    
    fun getConnectionForWorkspace(workspaceId: String): Connection {
        val shardKey = getShardKey(workspaceId)
        return connectionPool.getConnection("shard_$shardKey")
    }
}

// Cross-shard Query Execution
class CrossShardQueryExecutor {
    suspend fun executeQuery(query: String): List<Result> = coroutineScope {
        val results = (0 until shardCount).map { shardId ->
            async {
                connectionPool.getConnection("shard_$shardId").use { conn ->
                    conn.executeQuery(query)
                }
            }
        }.awaitAll()
        
        return@coroutineScope results.flatten()
    }
}

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// Multi-tier Caching
class CacheManager {
    private val l1Cache = CaffeineCache(maxSize = 10000) // In-memory
    private val l2Cache = RedisCache() // Distributed
    private val l3Cache = CDNCache() // Edge
    
    suspend fun get(key: String): Any? {
        // L1 Cache (microseconds)
        l1Cache.get(key)?.let { return it }
        
        // L2 Cache (milliseconds)
        l2Cache.get(key)?.let { 
            l1Cache.put(key, it)
            return it 
        }
        
        // L3 Cache (tens of milliseconds)
        l3Cache.get(key)?.let {
            l2Cache.put(key, it)
            l1Cache.put(key, it)
            return it
        }
        
        return null
    }
    
    suspend fun put(key: String, value: Any, ttl: Duration = 1.hours) {
        // Write-through to all levels
        l1Cache.put(key, value, ttl)
        l2Cache.put(key, value, ttl)
        l3Cache.put(key, value, ttl)
    }
}

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-- Optimized query with proper indexing
EXPLAIN ANALYZE
WITH active_agents AS (
    SELECT 
        a.id,
        a.name,
        a.capabilities,
        COUNT(ae.id) as execution_count,
        AVG(ae.execution_time_ms) as avg_execution_time,
        SUM(ae.tokens_used) as total_tokens
    FROM agents a
    LEFT JOIN agent_executions ae ON a.id = ae.agent_id
        AND ae.started_at >= NOW() - INTERVAL '24 hours'
    WHERE a.workspace_id = $1
        AND a.state = 'active'
    GROUP BY a.id
),
ranked_agents AS (
    SELECT 
        *,
        ROW_NUMBER() OVER (
            ORDER BY execution_count DESC, avg_execution_time ASC
        ) as rank
    FROM active_agents
)
SELECT * FROM ranked_agents
WHERE rank <= 10;

-- Execution plan shows index scans and hash joins
-- Cost: 125.43..456.78 (actual time=0.045..0.123)

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// Security Layer Implementation
class SecurityGateway {
    private val authService = AuthenticationService()
    private val authzService = AuthorizationService()
    private val encryptionService = EncryptionService()
    private val auditService = AuditService()
    
    suspend fun processRequest(request: Request): Response {
        // 1. Authentication
        val identity = authService.authenticate(request.token)
            ?: throw UnauthorizedException()
        
        // 2. Authorization
        if (!authzService.authorize(identity, request.resource, request.action)) {
            throw ForbiddenException()
        }
        
        // 3. Decrypt request if needed
        val decryptedPayload = if (request.isEncrypted) {
            encryptionService.decrypt(request.payload, identity.keyId)
        } else {
            request.payload
        }
        
        // 4. Process request
        val response = processSecurely(decryptedPayload)
        
        // 5. Audit
        auditService.log(
            identity = identity,
            action = request.action,
            resource = request.resource,
            result = response.status
        )
        
        // 6. Encrypt response
        return encryptionService.encrypt(response, identity.publicKey)
    }
}

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# API Gateway Security Configuration (Kong)
plugins:
  - name: rate-limiting
    config:
      minute: 100
      hour: 10000
      policy: redis
      
  - name: jwt
    config:
      key_claim_name: kid
      secret_is_base64: true
      
  - name: ip-restriction
    config:
      whitelist:
        - 10.0.0.0/8
        - 172.16.0.0/12
      
  - name: request-transformer
    config:
      add:
        headers:
          - X-Request-ID:$(uuid)
          - X-Timestamp:$(timestamp)
      
  - name: response-transformer
    config:
      remove:
        headers:
          - Server
          - X-Powered-By

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// Metrics Implementation
class MetricsCollector {
    private val meterRegistry = PrometheusMeterRegistry(PrometheusConfig.DEFAULT)
    
    // Counter metrics
    val agentExecutions = meterRegistry.counter("agent.executions.total")
    val apiRequests = meterRegistry.counter("api.requests.total")
    
    // Gauge metrics
    val activeAgents = meterRegistry.gauge("agents.active.count", AtomicInteger(0))
    val queueDepth = meterRegistry.gauge("queue.depth", AtomicInteger(0))
    
    // Histogram metrics
    val executionDuration = meterRegistry.timer("agent.execution.duration")
    val tokenUsage = meterRegistry.summary("agent.tokens.used")
    
    // Custom metrics
    fun recordExecution(agentId: String, duration: Duration, tokensUsed: Int) {
        executionDuration.record(duration)
        tokenUsage.record(tokensUsed.toDouble())
        agentExecutions.increment()
        
        // Add tags for detailed analysis
        meterRegistry.counter(
            "agent.executions.by_type",
            "agent_id", agentId,
            "status", "success"
        ).increment()
    }
}

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// OpenTelemetry Integration
class TracingService {
    private val tracer = OpenTelemetry.getTracer("aimatrix")
    
    suspend fun <T> traceOperation(
        operationName: String,
        attributes: Map<String, String> = emptyMap(),
        block: suspend () -> T
    ): T {
        val span = tracer.spanBuilder(operationName)
            .setSpanKind(SpanKind.INTERNAL)
            .startSpan()
        
        return try {
            attributes.forEach { (key, value) ->
                span.setAttribute(key, value)
            }
            
            span.makeCurrent().use {
                block()
            }
        } catch (e: Exception) {
            span.recordException(e)
            throw e
        } finally {
            span.end()
        }
    }
}

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#!/bin/bash
# Automated Backup Script

# Database backups
pg_dump -h $DB_HOST -U $DB_USER -d aimatrix_prod \
    --format=custom --compress=9 \
    > /backups/postgres/aimatrix_$(date +%Y%m%d_%H%M%S).dump

# MongoDB backup
mongodump --uri=$MONGO_URI \
    --archive=/backups/mongo/aimatrix_$(date +%Y%m%d_%H%M%S).archive \
    --gzip

# Vector database export
curl -X POST $PINECONE_API/indexes/aimatrix/backup \
    -H "Api-Key: $PINECONE_KEY" \
    -d '{"destination": "s3://aimatrix-backups/vectors/"}'

# Upload to S3 with versioning
aws s3 sync /backups/ s3://aimatrix-backups/ \
    --storage-class GLACIER_IR \
    --metadata backup-date=$(date +%Y%m%d)

# Cleanup old backups (keep 30 days)
find /backups/ -type f -mtime +30 -delete

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// GDPR Implementation
class GDPRComplianceService {
    // Right to be forgotten
    suspend fun deleteUserData(userId: String) {
        transaction {
            // Delete from all tables
            agentRepository.deleteByOwner(userId)
            documentRepository.deleteByOwner(userId)
            executionRepository.deleteByOwner(userId)
            
            // Remove from vector stores
            vectorStore.deleteByMetadata("owner_id", userId)
            
            // Purge from caches
            cacheManager.purgeUser(userId)
            
            // Log deletion for compliance
            auditLog.logDeletion(userId, timestamp = now())
        }
    }
    
    // Data portability
    suspend fun exportUserData(userId: String): UserDataExport {
        return UserDataExport(
            profile = userRepository.findById(userId),
            agents = agentRepository.findByOwner(userId),
            documents = documentRepository.findByOwner(userId),
            executions = executionRepository.findByOwner(userId),
            exportedAt = now()
        )
    }
}