Building Production-Ready AI Systems with AgentRouter
· 8 min read
The gap between AI prototypes and production systems is vast. While creating a demo that impresses stakeholders might take days, building a system that reliably serves millions of users requires addressing scalability, reliability, security, and maintainability. AgentRouter bridges this gap by providing production-ready primitives for multi-agent AI systems.
The Production Challenge
Moving AI from prototype to production involves solving complex challenges:
- Scale: From 10 requests to 10 million
- Reliability: From 90% to 99.99% uptime
- Latency: From seconds to milliseconds
- Cost: From unlimited to optimized budgets
- Security: From trust to zero-trust architecture
- Compliance: From flexible to regulated
Production-Ready Architecture Principles
1. Defensive Design
Every production system must assume failure:
from agentrouter import ManagerAgent, CircuitBreaker, RetryPolicy
class ProductionAgent(ManagerAgent):
def __init__(self, **kwargs):
super().__init__(**kwargs)
# Circuit breaker prevents cascade failures
self.circuit_breaker = CircuitBreaker(
failure_threshold=5,
recovery_timeout=30,
expected_exception=APIException
)
# Retry policy for transient failures
self.retry_policy = RetryPolicy(
max_attempts=3,
backoff_strategy="exponential",
max_delay=10
)
# Timeout protection
self.timeout = 30 # seconds
# Fallback responses
self.fallback_enabled = True
2. Observability First
You can't fix what you can't see:
from agentrouter.monitoring import MetricsCollector, TracingProvider
class ObservableAgent(ManagerAgent):
def __init__(self, **kwargs):
super().__init__(**kwargs)
# Metrics collection
self.metrics = MetricsCollector(
namespace="production.agents",
dimensions={"service": "ai-platform"}
)
# Distributed tracing
self.tracer = TracingProvider(
service_name=self.name,
sample_rate=0.1 # Sample 10% of requests
)
async def execute(self, task):
with self.tracer.span("agent.execute") as span:
span.set_tag("task.type", task.type)
start_time = time.time()
try:
result = await super().execute(task)
self.metrics.record_success(
duration=time.time() - start_time
)
return result
except Exception as e:
self.metrics.record_failure(
error_type=type(e).__name__
)
span.set_error(e)
raise
3. Resource Management
Prevent resource exhaustion:
from agentrouter.resources import ResourcePool, RateLimiter
class ResourceManagedSystem:
def __init__(self):
# Connection pooling
self.connection_pool = ResourcePool(
min_size=10,
max_size=100,
idle_timeout=300
)
# Rate limiting per client
self.rate_limiter = RateLimiter(
requests_per_second=100,
burst_size=200,
per_client=True
)
# Memory management
self.memory_limit = 2 * 1024 * 1024 * 1024 # 2GB
self.cache = LRUCache(maxsize=1000)
# Thread pool for parallel execution
self.executor = ThreadPoolExecutor(
max_workers=50,
thread_name_prefix="agent-worker"
)
Essential Production Components
1. Health Checks and Readiness Probes
from agentrouter.health import HealthCheck, ReadinessCheck
class HealthMonitor:
@HealthCheck.register("api_connectivity")
async def check_api_health(self):
"""Verify API endpoints are accessible"""
try:
response = await self.test_api_call()
return response.status_code == 200
except Exception:
return False
@ReadinessCheck.register("model_loaded")
async def check_model_ready(self):
"""Ensure models are loaded and warm"""
return all([
self.primary_model.is_loaded,
self.fallback_model.is_loaded,
self.cache.is_warm
])
async def health_endpoint(self):
"""HTTP endpoint for health checks"""
checks = await HealthCheck.run_all()
status = "healthy" if all(checks.values()) else "unhealthy"
return {
"status": status,
"checks": checks,
"timestamp": datetime.utcnow().isoformat()
}
2. Configuration Management
from agentrouter.config import ConfigManager, SecretManager
class ProductionConfig:
def __init__(self, environment="production"):
# Environment-specific configuration
self.config = ConfigManager(
environment=environment,
config_path="/etc/agentrouter/config.yaml",
schema_validation=True
)
# Secret management
self.secrets = SecretManager(
provider="aws_secrets_manager",
region="us-east-1",
cache_ttl=3600
)
# Feature flags
self.features = FeatureFlagManager(
provider="launchdarkly",
sdk_key=self.secrets.get("feature_flag_key")
)
def get_agent_config(self, agent_name):
return {
"api_key": self.secrets.get(f"{agent_name}_api_key"),
"model": self.config.get(f"agents.{agent_name}.model"),
"temperature": self.config.get(f"agents.{agent_name}.temperature"),
"max_retries": self.config.get("global.max_retries", default=3),
"features": self.features.get_features(agent_name)
}
3. Error Handling and Recovery
from agentrouter.errors import ErrorHandler, RecoveryStrategy
class RobustAgentSystem:
def __init__(self):
self.error_handler = ErrorHandler(
strategies={
TokenLimitExceeded: RecoveryStrategy.CHUNK_AND_RETRY,
RateLimitError: RecoveryStrategy.EXPONENTIAL_BACKOFF,
ModelTimeout: RecoveryStrategy.USE_FALLBACK,
InvalidResponse: RecoveryStrategy.RETRY_WITH_REFINEMENT,
CriticalError: RecoveryStrategy.ALERT_AND_FAIL
}
)
async def execute_with_recovery(self, task):
try:
return await self.primary_agent.execute(task)
except Exception as e:
recovery_strategy = self.error_handler.get_strategy(e)
if recovery_strategy == RecoveryStrategy.CHUNK_AND_RETRY:
return await self.execute_chunked(task)
elif recovery_strategy == RecoveryStrategy.USE_FALLBACK:
return await self.fallback_agent.execute(task)
elif recovery_strategy == RecoveryStrategy.ALERT_AND_FAIL:
await self.alert_team(e)
raise
else:
return await recovery_strategy.execute(task, e)
4. Caching and Performance Optimization
from agentrouter.cache import MultiLevelCache, CacheStrategy
class OptimizedAgent:
def __init__(self):
# Multi-level caching
self.cache = MultiLevelCache(
l1_cache=InMemoryCache(size_mb=100, ttl=60),
l2_cache=RedisCache(host="redis.internal", ttl=300),
l3_cache=S3Cache(bucket="agent-cache", ttl=3600)
)
# Cache strategies
self.cache_strategy = CacheStrategy(
key_generator=self.generate_cache_key,
should_cache=self.is_cacheable,
ttl_calculator=self.calculate_ttl
)
async def execute(self, task):
# Check cache first
cache_key = self.cache_strategy.generate_key(task)
cached_result = await self.cache.get(cache_key)
if cached_result:
self.metrics.increment("cache.hit")
return cached_result
# Execute and cache
result = await super().execute(task)
if self.cache_strategy.should_cache(task, result):
ttl = self.cache_strategy.calculate_ttl(task, result)
await self.cache.set(cache_key, result, ttl)
return result
Deployment Strategies
1. Blue-Green Deployment
# kubernetes deployment configuration
apiVersion: apps/v1
kind: Deployment
metadata:
name: agentrouter-blue
spec:
replicas: 10
selector:
matchLabels:
app: agentrouter
version: blue
template:
spec:
containers:
- name: agent
image: agentrouter:v2.0.0
env:
- name: DEPLOYMENT_COLOR
value: "blue"
readinessProbe:
httpGet:
path: /health/ready
port: 8080
initialDelaySeconds: 30
periodSeconds: 10
2. Canary Releases
from agentrouter.deployment import CanaryDeployment
class CanaryManager:
def __init__(self):
self.canary = CanaryDeployment(
stable_version="v1.0.0",
canary_version="v2.0.0",
initial_traffic_percentage=5,
increment_percentage=10,
error_threshold=0.01, # 1% error rate
rollback_on_failure=True
)
async def progressive_rollout(self):
while self.canary.traffic_percentage < 100:
metrics = await self.canary.collect_metrics(duration=300)
if metrics.error_rate > self.canary.error_threshold:
await self.canary.rollback()
raise DeploymentError("Canary failed quality checks")
await self.canary.increase_traffic()
await asyncio.sleep(600) # Wait 10 minutes
await self.canary.promote_to_stable()
Monitoring and Alerting
1. Key Metrics to Track
class ProductionMetrics:
CRITICAL_METRICS = {
"latency_p99": {"threshold": 1000, "unit": "ms"},
"error_rate": {"threshold": 0.001, "unit": "percentage"},
"throughput": {"threshold": 1000, "unit": "requests/second"},
"api_quota_usage": {"threshold": 0.8, "unit": "percentage"},
"memory_usage": {"threshold": 0.9, "unit": "percentage"},
"token_usage": {"threshold": 0.9, "unit": "percentage"},
"cache_hit_rate": {"threshold": 0.7, "unit": "percentage"}
}
async def check_metrics(self):
alerts = []
for metric_name, config in self.CRITICAL_METRICS.items():
value = await self.get_metric_value(metric_name)
if value > config["threshold"]:
alerts.append({
"metric": metric_name,
"value": value,
"threshold": config["threshold"],
"severity": "critical"
})
return alerts
2. Structured Logging
import structlog
class StructuredLogger:
def __init__(self):
self.logger = structlog.get_logger(
processors=[
structlog.stdlib.add_log_level,
structlog.processors.add_timestamp,
structlog.processors.format_exc_info,
structlog.processors.JSONRenderer()
]
)
def log_agent_execution(self, agent_name, task, result, duration):
self.logger.info(
"agent_execution",
agent=agent_name,
task_id=task.id,
task_type=task.type,
success=result.success,
duration_ms=duration * 1000,
token_count=result.token_count,
cache_hit=result.from_cache,
model_version=result.model_version
)
Security Best Practices
1. Input Validation and Sanitization
from agentrouter.security import InputValidator, Sanitizer
class SecureAgent:
def __init__(self):
self.validator = InputValidator(
max_length=10000,
allowed_patterns=[r"^[a-zA-Z0-9\s\.\,\!\?]+$"],
blocked_patterns=[r"<script>", r"DROP TABLE", r"../../"],
encoding="utf-8"
)
self.sanitizer = Sanitizer(
strip_html=True,
escape_special_chars=True,
normalize_unicode=True
)
async def execute(self, task):
# Validate input
if not self.validator.is_valid(task.input):
raise ValidationError("Invalid input detected")
# Sanitize input
sanitized_input = self.sanitizer.clean(task.input)
# Execute with sanitized input
return await super().execute(sanitized_input)
2. API Key Management
from agentrouter.security import APIKeyRotation
class SecureAPIManager:
def __init__(self):
self.key_rotation = APIKeyRotation(
rotation_interval_days=30,
grace_period_hours=24,
vault_provider="hashicorp",
notification_channel="slack"
)
async def get_api_key(self, service):
key = await self.key_rotation.get_current_key(service)
# Check if rotation is needed
if await self.key_rotation.should_rotate(service):
await self.key_rotation.initiate_rotation(service)
return key
Cost Optimization Strategies
1. Model Selection Optimization
class CostOptimizedRouter:
def __init__(self):
self.model_costs = {
"usf-mini": 0.003, # per 1k tokens
"usf-mini-x1": 0.0002, # per 1k tokens
}
def select_model(self, task):
if task.complexity == "simple":
return "usf-mini" # Use cheapest for simple tasks
elif task.complexity == "medium":
return "usf-mini" # Balance cost and capability
elif task.requires_reasoning:
return "usf-mini-x1" # Better reasoning
else:
return "usf-mini" # Maximum capability
2. Request Batching
from agentrouter.optimization import BatchProcessor
class BatchingSystem:
def __init__(self):
self.batch_processor = BatchProcessor(
batch_size=100,
max_wait_time=1.0, # seconds
parallel_batches=5
)
async def process_requests(self, requests):
# Batch similar requests
batches = self.batch_processor.create_batches(requests)
# Process batches in parallel
results = await asyncio.gather(*[
self.process_batch(batch) for batch in batches
])
# Return individual results
return self.batch_processor.unbatch(results)
Testing in Production
1. Shadow Testing
class ShadowTesting:
async def shadow_test(self, request):
# Send to production system
prod_task = self.prod_system.execute(request)
# Send to shadow system (non-blocking)
shadow_task = asyncio.create_task(
self.shadow_system.execute(request)
)
# Return production result immediately
prod_result = await prod_task
# Compare results asynchronously
asyncio.create_task(
self.compare_results(request, prod_result, shadow_task)
)
return prod_result
2. A/B Testing
from agentrouter.testing import ABTest
class ABTestingFramework:
def __init__(self):
self.ab_test = ABTest(
name="new_agent_algorithm",
control_group="current_algorithm",
treatment_group="optimized_algorithm",
sample_size=10000,
significance_level=0.05
)
async def route_request(self, request, user_id):
group = self.ab_test.assign_group(user_id)
if group == "control":
result = await self.current_agent.execute(request)
else:
result = await self.optimized_agent.execute(request)
# Track metrics
self.ab_test.record_result(
group=group,
metrics={
"latency": result.latency,
"accuracy": result.accuracy,
"user_satisfaction": result.satisfaction_score
}
)
return result
Scaling Strategies
1. Horizontal Scaling
from agentrouter.scaling import AutoScaler
class ScalableAgentSystem:
def __init__(self):
self.auto_scaler = AutoScaler(
min_instances=2,
max_instances=100,
target_cpu_utilization=70,
target_memory_utilization=80,
scale_up_threshold=60, # seconds
scale_down_threshold=300 # seconds
)
async def monitor_and_scale(self):
while True:
metrics = await self.collect_metrics()
if self.auto_scaler.should_scale_up(metrics):
await self.add_agent_instances(
count=self.auto_scaler.calculate_scale_up_count(metrics)
)
elif self.auto_scaler.should_scale_down(metrics):
await self.remove_agent_instances(
count=self.auto_scaler.calculate_scale_down_count(metrics)
)
await asyncio.sleep(30)
Production Checklist
Before deploying to production, ensure:
- Monitoring: Metrics, logs, and traces configured
- Alerting: Critical alerts set up with proper escalation
- Security: Input validation, authentication, and encryption
- Performance: Load tested to 2x expected traffic
- Reliability: Circuit breakers and retry logic implemented
- Scalability: Auto-scaling configured and tested
- Documentation: Runbooks and troubleshooting guides ready
- Compliance: Data privacy and regulatory requirements met
- Disaster Recovery: Backup and restore procedures tested
- Cost Management: Budget alerts and optimization in place
Conclusion
Building production-ready AI systems requires careful attention to reliability, scalability, security, and maintainability. AgentRouter provides the foundation and tools needed to transform AI prototypes into robust production systems that can serve millions of users reliably.
The journey from prototype to production is complex, but with the right architecture, monitoring, and operational practices, you can build AI systems that deliver value at scale while maintaining high availability and performance.
Ready to deploy to production? Check our Enterprise Architecture Guide.
Share your production experiences: Join our community to discuss production challenges and solutions with other engineers deploying AI at scale.