The Key Components of a Production-Ready AI Agent Architecture

class AgentRegistry:
    def __init__(self):
        self.agents = {}
        self.performance_metrics = {}
    
    async def select_agent(self, request_data):
        """Select the best agent for a given request"""
        requirements = await self.analyze_requirements(request_data)
        
        # Find agents with required capabilities
        candidate_agents = self.find_capable_agents(requirements)
        
        # Rank agents by performance and availability
        ranked_agents = await self.rank_agents(candidate_agents, requirements)
        
        # Select best available agent
        return await self.select_best_agent(ranked_agents)
    
    async def health_check(self, agent_id):
        """Check agent health and performance"""
        agent = self.agents.get(agent_id)
        if not agent:
            return False
        
        # Check agent status
        if agent['status'] != 'active':
            return False
        
        # Check performance metrics
        metrics = self.performance_metrics.get(agent_id, {})
        if metrics.get('error_rate', 0) > 0.1:  # 10% error threshold
            return False
        
        if metrics.get('response_time', 0) > 5.0:  # 5 second threshold
            return False
        
        return True

class ContextManager:
    def __init__(self, vector_store, cache, metadata_db):
        self.vector_store = vector_store
        self.cache = cache
        self.metadata_db = metadata_db
    
    async def load_context(self, request_data):
        """Load relevant context for a request"""
        # Extract context requirements
        context_requirements = await self.extract_context_requirements(request_data)
        
        # Check cache first
        cached_context = await self.cache.get(context_requirements['cache_key'])
        if cached_context:
            return cached_context
        
        # Load from vector store
        context_vectors = await self.vector_store.similarity_search(
            query=context_requirements['query'],
            limit=context_requirements['limit'],
            filters=context_requirements['filters']
        )
        
        # Enrich with metadata
        enriched_context = await self.enrich_context(context_vectors)
        
        # Cache for future use
        await self.cache.set(context_requirements['cache_key'], enriched_context)
        
        return enriched_context
    
    async def store_result(self, result, context):
        """Store agent result and update context"""
        # Store result in metadata database
        result_id = await self.metadata_db.store_result(result, context)
        
        # Update vector store with new information
        if result.get('new_information'):
            await self.vector_store.add_documents(
                documents=result['new_information'],
                metadata={'result_id': result_id, 'timestamp': datetime.now()}
            )
        
        # Update cache
        await self.cache.invalidate_related(context['cache_keys'])
    
    async def manage_context_lifecycle(self):
        """Manage context lifecycle and cleanup"""
        # Remove expired context
        expired_context = await self.metadata_db.get_expired_context()
        for context_id in expired_context:
            await self.vector_store.delete_documents(context_id)
            await self.cache.delete(context_id)
        
        # Optimize vector store
        await self.vector_store.optimize()

class CircuitBreaker:
    def __init__(self, failure_threshold=5, timeout=60, retry_timeout=30):
        self.failure_threshold = failure_threshold
        self.timeout = timeout
        self.retry_timeout = retry_timeout
        self.failure_count = 0
        self.last_failure_time = None
        self.state = 'CLOSED'  # CLOSED, OPEN, HALF_OPEN
    
    async def call(self, func, *args, **kwargs):
        """Execute function with circuit breaker protection"""
        if self.state == 'OPEN':
            if self.should_attempt_reset():
                self.state = 'HALF_OPEN'
            else:
                raise CircuitBreakerOpen("Circuit breaker is open")
        
        try:
            result = await func(*args, **kwargs)
            self.on_success()
            return result
        except Exception as e:
            self.on_failure()
            raise e
    
    def on_success(self):
        """Handle successful call"""
        self.failure_count = 0
        self.state = 'CLOSED'
    
    def on_failure(self):
        """Handle failed call"""
        self.failure_count += 1
        self.last_failure_time = datetime.now()
        
        if self.failure_count >= self.failure_threshold:
            self.state = 'OPEN'
    
    def should_attempt_reset(self):
        """Check if circuit breaker should attempt reset"""
        if self.last_failure_time is None:
            return True
        
        return (datetime.now() - self.last_failure_time).seconds >= self.retry_timeout

class RetryStrategy:
    def __init__(self, max_attempts=3, base_delay=1, max_delay=60, backoff_factor=2):
        self.max_attempts = max_attempts
        self.base_delay = base_delay
        self.max_delay = max_delay
        self.backoff_factor = backoff_factor
    
    async def execute_with_retry(self, func, *args, **kwargs):
        """Execute function with retry logic"""
        last_exception = None
        
        for attempt in range(self.max_attempts):
            try:
                return await func(*args, **kwargs)
            except Exception as e:
                last_exception = e
                
                if attempt == self.max_attempts - 1:
                    break
                
                # Calculate delay with exponential backoff
                delay = min(
                    self.base_delay * (self.backoff_factor ** attempt),
                    self.max_delay
                )
                
                await asyncio.sleep(delay)
        
        raise last_exception

class FallbackManager:
    def __init__(self, fallback_strategies):
        self.fallback_strategies = fallback_strategies
        self.performance_tracker = PerformanceTracker()
    
    async def execute_with_fallback(self, primary_func, context):
        """Execute primary function with fallback options"""
        try:
            # Attempt primary function
            result = await primary_func(context)
            self.performance_tracker.record_success('primary')
            return result
        except Exception as e:
            # Log failure and attempt fallback
            await self.log_failure('primary', e, context)
            
            # Try fallback strategies in order
            for strategy_name, fallback_func in self.fallback_strategies.items():
                try:
                    result = await fallback_func(context)
                    self.performance_tracker.record_success(f'fallback_{strategy_name}')
                    return result
                except Exception as fallback_error:
                    await self.log_failure(f'fallback_{strategy_name}', fallback_error, context)
                    continue
            
            # All strategies failed
            return await self.handle_complete_failure(context)
    
    async def handle_complete_failure(self, context):
        """Handle complete failure of all strategies"""
        # Return minimal response or queue for later processing
        return {
            'status': 'failed',
            'message': 'Service temporarily unavailable',
            'retry_after': 300,  # 5 minutes
            'context': context
        }

import logging
import json
from datetime import datetime

class StructuredLogger:
    def __init__(self, service_name, log_level=logging.INFO):
        self.service_name = service_name
        self.logger = logging.getLogger(service_name)
        self.logger.setLevel(log_level)
        
        # Configure structured logging
        handler = logging.StreamHandler()
        formatter = logging.Formatter(
            '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
        )
        handler.setFormatter(formatter)
        self.logger.addHandler(handler)
    
    def log_request(self, request_id, user_id, endpoint, data):
        """Log incoming request"""
        self.logger.info(json.dumps({
            'event_type': 'request_received',
            'request_id': request_id,
            'user_id': user_id,
            'endpoint': endpoint,
            'timestamp': datetime.now().isoformat(),
            'data_size': len(str(data))
        }))
    
    def log_processing(self, request_id, agent_id, processing_time, status):
        """Log agent processing"""
        self.logger.info(json.dumps({
            'event_type': 'agent_processing',
            'request_id': request_id,
            'agent_id': agent_id,
            'processing_time': processing_time,
            'status': status,
            'timestamp': datetime.now().isoformat()
        }))
    
    def log_error(self, request_id, error_type, error_message, stack_trace):
        """Log error with full context"""
        self.logger.error(json.dumps({
            'event_type': 'error',
            'request_id': request_id,
            'error_type': error_type,
            'error_message': error_message,
            'stack_trace': stack_trace,
            'timestamp': datetime.now().isoformat()
        }))
    
    def log_performance(self, request_id, metrics):
        """Log performance metrics"""
        self.logger.info(json.dumps({
            'event_type': 'performance_metrics',
            'request_id': request_id,
            'metrics': metrics,
            'timestamp': datetime.now().isoformat()
        }))

class MetricsCollector:
    def __init__(self, metrics_backend):
        self.metrics_backend = metrics_backend
        self.counters = {}
        self.gauges = {}
        self.histograms = {}
    
    def increment_counter(self, metric_name, value=1, tags=None):
        """Increment a counter metric"""
        if metric_name not in self.counters:
            self.counters[metric_name] = 0
        self.counters[metric_name] += value
        
        # Send to metrics backend
        self.metrics_backend.send_counter(metric_name, value, tags)
    
    def set_gauge(self, metric_name, value, tags=None):
        """Set a gauge metric"""
        self.gauges[metric_name] = value
        self.metrics_backend.send_gauge(metric_name, value, tags)
    
    def record_histogram(self, metric_name, value, tags=None):
        """Record a histogram metric"""
        if metric_name not in self.histograms:
            self.histograms[metric_name] = []
        self.histograms[metric_name].append(value)
        
        self.metrics_backend.send_histogram(metric_name, value, tags)
    
    def get_performance_summary(self):
        """Get performance summary"""
        return {
            'counters': self.counters,
            'gauges': self.gauges,
            'histogram_stats': {
                name: {
                    'count': len(values),
                    'mean': sum(values) / len(values),
                    'min': min(values),
                    'max': max(values)
                }
                for name, values in self.histograms.items()
            }
        }

import uuid
from contextvars import ContextVar

class DistributedTracer:
    def __init__(self, trace_backend):
        self.trace_backend = trace_backend
        self.trace_context = ContextVar('trace_context')
    
    def start_trace(self, operation_name, parent_trace_id=None):
        """Start a new trace"""
        trace_id = str(uuid.uuid4())
        span_id = str(uuid.uuid4())
        
        trace_context = {
            'trace_id': trace_id,
            'span_id': span_id,
            'operation_name': operation_name,
            'parent_trace_id': parent_trace_id,
            'start_time': datetime.now(),
            'tags': {},
            'logs': []
        }
        
        self.trace_context.set(trace_context)
        return trace_context
    
    def add_span(self, operation_name, tags=None):
        """Add a new span to the current trace"""
        current_context = self.trace_context.get()
        if not current_context:
            return self.start_trace(operation_name)
        
        span_id = str(uuid.uuid4())
        span = {
            'span_id': span_id,
            'operation_name': operation_name,
            'parent_span_id': current_context['span_id'],
            'start_time': datetime.now(),
            'tags': tags or {},
            'logs': []
        }
        
        current_context['child_spans'] = current_context.get('child_spans', [])
        current_context['child_spans'].append(span)
        
        return span
    
    def finish_trace(self, status='success', error=None):
        """Finish the current trace"""
        current_context = self.trace_context.get()
        if not current_context:
            return
        
        current_context['end_time'] = datetime.now()
        current_context['duration'] = (
            current_context['end_time'] - current_context['start_time']
        ).total_seconds()
        current_context['status'] = status
        
        if error:
            current_context['error'] = str(error)
            current_context['status'] = 'error'
        
        # Send trace to backend
        self.trace_backend.send_trace(current_context)

# Dockerfile for AI Agent Service
FROM python:3.9-slim

WORKDIR /app

# Install system dependencies
RUN apt-get update && apt-get install -y \
    gcc \
    g++ \
    && rm -rf /var/lib/apt/lists/*

# Copy requirements and install Python dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy application code
COPY . .

# Create non-root user
RUN useradd -m -u 1000 agent && chown -R agent:agent /app
USER agent

# Health check
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
    CMD python health_check.py

# Expose port
EXPOSE 8000

# Start application
CMD ["python", "main.py"]

version: '3.8'
services:
  ai-agent-service:
    build: .
    ports:
      - "8000:8000"
    environment:
      - DATABASE_URL=postgresql://user:pass@postgres:5432/ai_agents
      - REDIS_URL=redis://redis:6379
      - LOG_LEVEL=INFO
    depends_on:
      - postgres
      - redis
      - vector-db
    restart: unless-stopped
    
  postgres:
    image: postgres:13
    environment:
      - POSTGRES_DB=ai_agents
      - POSTGRES_USER=user
      - POSTGRES_PASSWORD=pass
    volumes:
      - postgres_data:/var/lib/postgresql/data
    restart: unless-stopped
    
  redis:
    image: redis:alpine
    ports:
      - "6379:6379"
    restart: unless-stopped
    
  vector-db:
    image: qdrant/qdrant:latest
    ports:
      - "6333:6333"
    volumes:
      - qdrant_data:/qdrant/storage
    restart: unless-stopped

volumes:
  postgres_data:
  qdrant_data:

# deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: ai-agent-service
spec:
  replicas: 3
  selector:
    matchLabels:
      app: ai-agent-service
  template:
    metadata:
      labels:
        app: ai-agent-service
    spec:
      containers:
      - name: ai-agent
        image: ai-agent:latest
        ports:
        - containerPort: 8000
        env:
        - name: DATABASE_URL
          valueFrom:
            secretKeyRef:
              name: ai-agent-secrets
              key: database-url
        - name: REDIS_URL
          valueFrom:
            secretKeyRef:
              name: ai-agent-secrets
              key: redis-url
        resources:
          requests:
            memory: "512Mi"
            cpu: "250m"
          limits:
            memory: "1Gi"
            cpu: "500m"
        livenessProbe:
          httpGet:
            path: /health
            port: 8000
          initialDelaySeconds: 30
          periodSeconds: 10
        readinessProbe:
          httpGet:
            path: /ready
            port: 8000
          initialDelaySeconds: 5
          periodSeconds: 5

---
apiVersion: v1
kind: Service
metadata:
  name: ai-agent-service
spec:
  selector:
    app: ai-agent-service
  ports:
  - port: 80
    targetPort: 8000
  type: LoadBalancer

---
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: ai-agent-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: ai-agent-service
  minReplicas: 3
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80

class AutoScaler:
    def __init__(self, kubernetes_client, metrics_collector):
        self.k8s_client = kubernetes_client
        self.metrics_collector = metrics_collector
        self.scaling_policies = {
            'cpu_threshold': 70,
            'memory_threshold': 80,
            'request_rate_threshold': 1000,
            'response_time_threshold': 2.0
        }
    
    async def evaluate_scaling_need(self):
        """Evaluate if scaling is needed"""
        metrics = await self.metrics_collector.get_current_metrics()
        
        scaling_decision = {
            'scale_up': False,
            'scale_down': False,
            'reason': None
        }
        
        # Check CPU utilization
        if metrics['cpu_utilization'] > self.scaling_policies['cpu_threshold']:
            scaling_decision['scale_up'] = True
            scaling_decision['reason'] = 'High CPU utilization'
        
        # Check memory utilization
        elif metrics['memory_utilization'] > self.scaling_policies['memory_threshold']:
            scaling_decision['scale_up'] = True
            scaling_decision['reason'] = 'High memory utilization'
        
        # Check request rate
        elif metrics['request_rate'] > self.scaling_policies['request_rate_threshold']:
            scaling_decision['scale_up'] = True
            scaling_decision['reason'] = 'High request rate'
        
        # Check response time
        elif metrics['avg_response_time'] > self.scaling_policies['response_time_threshold']:
            scaling_decision['scale_up'] = True
            scaling_decision['reason'] = 'High response time'
        
        # Check for scale down conditions
        elif (metrics['cpu_utilization'] < 30 and 
              metrics['memory_utilization'] < 40 and
              metrics['request_rate'] < 100):
            scaling_decision['scale_down'] = True
            scaling_decision['reason'] = 'Low resource utilization'
        
        return scaling_decision
    
    async def execute_scaling(self, decision):
        """Execute scaling decision"""
        if decision['scale_up']:
            await self.scale_up()
        elif decision['scale_down']:
            await self.scale_down()
    
    async def scale_up(self):
        """Scale up the service"""
        current_replicas = await self.k8s_client.get_replica_count()
        new_replicas = min(current_replicas + 2, 10)  # Max 10 replicas
        
        await self.k8s_client.scale_deployment(new_replicas)
        await self.log_scaling_event('scale_up', current_replicas, new_replicas)
    
    async def scale_down(self):
        """Scale down the service"""
        current_replicas = await self.k8s_client.get_replica_count()
        new_replicas = max(current_replicas - 1, 3)  # Min 3 replicas
        
        await self.k8s_client.scale_deployment(new_replicas)
        await self.log_scaling_event('scale_down', current_replicas, new_replicas)