"""
Pattern 154: Canary Deployment
Description:
Implements gradual rollout of new agent versions by routing a small
percentage of traffic to the canary version, monitoring metrics, and
automatically rolling back if issues are detected.
Components:
- Traffic Splitter: Routes requests between versions
- Health Monitor: Tracks canary metrics
- Rollback Controller: Automatic rollback on issues
- Metrics Collector: Performance and error tracking
- Gradual Ramp: Increases traffic percentage over time
Use Cases:
- Safe production deployments
- Gradual feature rollout
- Risk mitigation for changes
- A/B testing with safety
- Automated deployment validation
Benefits:
- Minimal blast radius
- Automatic rollback
- Real user validation
- Gradual confidence building
- Production safety
Trade-offs:
- Deployment complexity
- Monitoring overhead
- Slower full rollout
- Dual version maintenance
- Metrics interpretation
LangChain Implementation:
Uses traffic routing with health monitoring and automatic rollback.
"""
import os
import time
import random
from typing import List, Dict, Any, Optional
from dataclasses import dataclass, field
from enum import Enum
from datetime import datetime
from collections import defaultdict
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
load_dotenv()
class DeploymentStatus(Enum):
"""Status of canary deployment"""
STARTING = "starting"
HEALTHY = "healthy"
WARNING = "warning"
ROLLING_BACK = "rolling_back"
ROLLED_BACK = "rolled_back"
PROMOTED = "promoted"
@dataclass
class CanaryMetrics:
"""Metrics for canary and production versions"""
# Request counts
production_requests: int = 0
canary_requests: int = 0
# Success rates
production_successes: int = 0
canary_successes: int = 0
production_errors: int = 0
canary_errors: int = 0
# Latency
production_latency_sum: float = 0.0
canary_latency_sum: float = 0.0
def get_production_error_rate(self) -> float:
"""Calculate production error rate"""
if self.production_requests == 0:
return 0.0
return self.production_errors / self.production_requests
def get_canary_error_rate(self) -> float:
"""Calculate canary error rate"""
if self.canary_requests == 0:
return 0.0
return self.canary_errors / self.canary_requests
def get_production_avg_latency(self) -> float:
"""Calculate average production latency"""
if self.production_requests == 0:
return 0.0
return self.production_latency_sum / self.production_requests
def get_canary_avg_latency(self) -> float:
"""Calculate average canary latency"""
if self.canary_requests == 0:
return 0.0
return self.canary_latency_sum / self.canary_requests
@dataclass
class CanaryConfig:
"""Configuration for canary deployment"""
initial_traffic_percent: float = 5.0 # Start with 5%
max_traffic_percent: float = 50.0 # Max before promotion
ramp_step: float = 5.0 # Increase by 5% each step
ramp_interval_seconds: float = 60.0 # Wait 60s between steps
# Health thresholds
max_error_rate_increase: float = 0.05 # Max 5% error rate increase
max_latency_increase: float = 0.20 # Max 20% latency increase
min_requests_for_decision: int = 10 # Min requests before decisions
# Rollback settings
auto_rollback: bool = True
rollback_on_error_spike: bool = True
class CanaryDeployment:
"""
Manages canary deployment with gradual traffic routing and
automatic rollback on issues.
"""
def __init__(self, config: Optional[CanaryConfig] = None):
"""Initialize canary deployment"""
self.config = config or CanaryConfig()
# Production version (stable)
self.production_llm = ChatOpenAI(
model="gpt-3.5-turbo",
temperature=0.7
)
# Canary version (new version being tested)
self.canary_llm = ChatOpenAI(
model="gpt-3.5-turbo",
temperature=0.8 # Different config for canary
)
# State
self.current_traffic_percent = self.config.initial_traffic_percent
self.status = DeploymentStatus.STARTING
self.metrics = CanaryMetrics()
self.deployment_start_time = datetime.now()
self.last_ramp_time = time.time()
# Prompts
self.prompt = ChatPromptTemplate.from_messages([
("system", "You are a helpful AI assistant."),
("user", "{query}")
])
def route_request(self, query: str) -> Dict[str, Any]:
"""
Route request to either production or canary based on traffic split
Args:
query: User query
Returns:
Response with version and metrics
"""
# Decide routing
use_canary = random.random() * 100 < self.current_traffic_percent
start_time = time.time()
try:
if use_canary:
# Route to canary
chain = self.prompt | self.canary_llm | StrOutputParser()
result = chain.invoke({"query": query})
version = "canary"
# Update canary metrics
self.metrics.canary_requests += 1
self.metrics.canary_successes += 1
latency = time.time() - start_time
self.metrics.canary_latency_sum += latency
else:
# Route to production
chain = self.prompt | self.production_llm | StrOutputParser()
result = chain.invoke({"query": query})
version = "production"
# Update production metrics
self.metrics.production_requests += 1
self.metrics.production_successes += 1
latency = time.time() - start_time
self.metrics.production_latency_sum += latency
# Check health after each request
self._check_health()
# Consider ramping up traffic
self._consider_ramp_up()
return {
"success": True,
"result": result,
"version": version,
"latency": latency,
"canary_traffic_percent": self.current_traffic_percent,
"status": self.status.value
}
except Exception as e:
# Track error
latency = time.time() - start_time
if use_canary:
self.metrics.canary_requests += 1
self.metrics.canary_errors += 1
self.metrics.canary_latency_sum += latency
version = "canary"
else:
self.metrics.production_requests += 1
self.metrics.production_errors += 1
self.metrics.production_latency_sum += latency
version = "production"
# Check if we should rollback
self._check_health()
return {
"success": False,
"error": str(e),
"version": version,
"latency": latency,
"canary_traffic_percent": self.current_traffic_percent,
"status": self.status.value
}
def _check_health(self):
"""Check canary health and trigger rollback if needed"""
if self.status == DeploymentStatus.ROLLED_BACK:
return # Already rolled back
if self.metrics.canary_requests 0:
error_rate_increase = (canary_error_rate - prod_error_rate) / prod_error_rate
else:
error_rate_increase = canary_error_rate
# Check latency
if prod_latency > 0:
latency_increase = (canary_latency - prod_latency) / prod_latency
else:
latency_increase = 0.0
# Determine health
issues = []
if error_rate_increase > self.config.max_error_rate_increase:
issues.append(f"Error rate {error_rate_increase*100:.1f}% higher")
if latency_increase > self.config.max_latency_increase:
issues.append(f"Latency {latency_increase*100:.1f}% higher")
if issues:
if self.config.auto_rollback:
print(f"\n🚨 ROLLBACK TRIGGERED: {', '.join(issues)}")
self._rollback()
else:
self.status = DeploymentStatus.WARNING
print(f"\n⚠️ WARNING: {', '.join(issues)}")
elif self.status != DeploymentStatus.PROMOTED:
self.status = DeploymentStatus.HEALTHY
def _consider_ramp_up(self):
"""Consider increasing traffic to canary"""
if self.status != DeploymentStatus.HEALTHY:
return # Only ramp if healthy
if self.current_traffic_percent >= self.config.max_traffic_percent:
return # Already at max
# Check if enough time has passed
time_since_last_ramp = time.time() - self.last_ramp_time
if time_since_last_ramp = self.config.max_traffic_percent:
print(f"\n✅ CANARY READY FOR PROMOTION (reached {self.config.max_traffic_percent}% traffic)")
def _rollback(self):
"""Rollback to production version"""
self.status = DeploymentStatus.ROLLING_BACK
self.current_traffic_percent = 0.0
print(f"\n⏪ Rolling back to production version...")
self.status = DeploymentStatus.ROLLED_BACK
print(f"✅ Rollback complete - 100% traffic to production")
def promote_canary(self):
"""Promote canary to production"""
if self.status == DeploymentStatus.ROLLED_BACK:
print("❌ Cannot promote - deployment was rolled back")
return False
print(f"\n🎉 PROMOTING CANARY TO PRODUCTION")
self.current_traffic_percent = 100.0
self.status = DeploymentStatus.PROMOTED
return True
def get_status_report(self) -> str:
"""Generate status report"""
m = self.metrics
report = [
"\n" + "="*70,
"CANARY DEPLOYMENT STATUS",
"="*70,
f"\nStatus: {self.status.value.upper()}",
f"Canary Traffic: {self.current_traffic_percent:.1f}%",
f"Deployment Duration: {(datetime.now() - self.deployment_start_time).total_seconds():.0f}s",
"\nProduction Metrics:",
f" Requests: {m.production_requests}",
f" Success Rate: {(m.production_successes/m.production_requests*100 if m.production_requests > 0 else 0):.1f}%",
f" Error Rate: {m.get_production_error_rate()*100:.1f}%",
f" Avg Latency: {m.get_production_avg_latency():.3f}s",
"\nCanary Metrics:",
f" Requests: {m.canary_requests}",
f" Success Rate: {(m.canary_successes/m.canary_requests*100 if m.canary_requests > 0 else 0):.1f}%",
f" Error Rate: {m.get_canary_error_rate()*100:.1f}%",
f" Avg Latency: {m.get_canary_avg_latency():.3f}s",
]
# Comparison
if m.canary_requests >= self.config.min_requests_for_decision:
error_diff = (m.get_canary_error_rate() - m.get_production_error_rate()) * 100
latency_diff_pct = ((m.get_canary_avg_latency() - m.get_production_avg_latency())
/ m.get_production_avg_latency() * 100) if m.get_production_avg_latency() > 0 else 0
report.extend([
"\nComparison (Canary vs Production):",
f" Error Rate Diff: {error_diff:+.1f}%",
f" Latency Diff: {latency_diff_pct:+.1f}%"
])
report.append("="*70)
return "\n".join(report)
def demonstrate_canary_deployment():
"""Demonstrate canary deployment pattern"""
print("="*70)
print("Pattern 154: Canary Deployment")
print("="*70)
# Configure canary deployment
config = CanaryConfig(
initial_traffic_percent=10.0,
max_traffic_percent=50.0,
ramp_step=10.0,
ramp_interval_seconds=5.0, # Short for demo
max_error_rate_increase=0.10,
max_latency_increase=0.30,
min_requests_for_decision=5,
auto_rollback=True
)
deployment = CanaryDeployment(config)
print(f"\n🚀 Starting canary deployment")
print(f" Initial traffic: {config.initial_traffic_percent}%")
print(f" Ramp step: {config.ramp_step}%")
print(f" Target: {config.max_traffic_percent}%")
# Simulate requests
test_queries = [
"What is Python?",
"Explain machine learning",
"How does AI work?",
"What is cloud computing?",
"Tell me about databases",
] * 10 # 50 total requests
print(f"\n{'='*70}")
print("SIMULATING PRODUCTION TRAFFIC")
print(f"{'='*70}")
results = []
for i, query in enumerate(test_queries, 1):
result = deployment.route_request(query)
results.append(result)
if i % 10 == 0:
print(f"\n📊 After {i} requests:")
print(deployment.get_status_report())
# Small delay to allow ramping
time.sleep(1)
# Final status
print(f"\n{'='*70}")
print("FINAL STATUS")
print(f"{'='*70}")
print(deployment.get_status_report())
# Show routing distribution
production_count = sum(1 for r in results if r.get('version') == 'production')
canary_count = sum(1 for r in results if r.get('version') == 'canary')
print(f"\n📊 Traffic Distribution:")
print(f" Production: {production_count} requests ({production_count/len(results)*100:.1f}%)")
print(f" Canary: {canary_count} requests ({canary_count/len(results)*100:.1f}%)")
# Promotion decision
if deployment.status == DeploymentStatus.HEALTHY:
print(f"\n✅ Canary is healthy and ready for promotion!")
deployment.promote_canary()
elif deployment.status == DeploymentStatus.ROLLED_BACK:
print(f"\n❌ Canary was rolled back due to issues")
# Summary
print("\n" + "="*70)
print("SUMMARY")
print("="*70)
print("""
Canary Deployment Pattern provides:
1. Gradual Rollout:
- Start with small traffic percentage
- Incrementally increase over time
- Monitor at each step
- Automatic promotion when ready
2. Safety Features:
- Continuous health monitoring
- Automatic rollback on issues
- Configurable thresholds
- Minimal blast radius
3. Metrics Tracking:
- Error rates comparison
- Latency comparison
- Success rates
- Traffic distribution
4. Deployment Control:
- Traffic percentage control
- Ramp-up automation
- Manual promotion option
- Emergency rollback
5. Production Safety:
- Real user validation
- Low-risk testing
- Quick issue detection
- Automatic recovery
This pattern is essential for safely deploying changes to
production AI systems with minimal risk and automatic safeguards.
""")
if __name__ == "__main__":
demonstrate_canary_deployment()
Agentic Design Pattern: Canary Deployment
Pattern 154: Canary Deployment – Line-by-Line Explanation
Overview
This document provides a detailed line-by-line explanation of the Canary Deployment pattern implementation.
Pattern Description
Pattern 154: Canary Deployment
Description: Implements gradual rollout of new agent versions by routing a small percentage of traffic to the canary version, monitoring metrics, and automatically rolling back if issues are detected.
Components: – Traffic Splitter: Routes requests between versions – Health Monitor: Tracks canary metrics – Rollback Controller: Automatic rollback on issues – Metrics Collector: Performance and error tracking – Gradual Ramp: Increases traffic percentage over time
Use Cases: – Safe production deployments – Gradual feature rollout – Risk mitigation for changes – A/B testing with safety – Automated deployment validation
Benefits: – Minimal blast radius – Automatic rollback – Real user validation – Gradual confidence building – Production safety
Trade-offs: – Deployment complexity – Monitoring overhead – Slower full rollout – Dual version maintenance – Metrics interpretation
LangChain Implementation: Uses traffic routing with health monitoring and automatic rollback.
File Statistics
- Total Lines: 491
- Classes: 4
- Functions: 1
- Constants: 0
Module Documentation
"""
Pattern 154: Canary Deployment
Description:
Implements gradual rollout of new agent versions by routing a small
percentage of traffic to the canary version, monitoring metrics, and
automatically rolling back if issues are detected.
Components:
- Traffic Splitter: Routes requests between versions
- Health Monitor: Tracks canary metrics
- Rollback Controller: Automatic rollback on issues
- Metrics Collector: Performance and error tracking
- Gradual Ramp: Increases traffic percentage over time
Use Cases:
- Safe production deployments
- Gradual feature rollout
- Risk mitigation for changes
- A/B testing with safety
- Automated deployment validation
Benefits:
- Minimal blast radius
- Automatic rollback
- Real user validation
- Gradual confidence building
- Production safety
Trade-offs:
- Deployment complexity
- Monitoring overhead
- Slower full rollout
- Dual version maintenance
- Metrics interpretation
LangChain Implementation:
Uses traffic routing with health monitoring and automatic rollback.
"""
Purpose: This module implements the Canary Deployment pattern, providing a structured approach to agent design and implementation.
Import Statements
Standard Library Imports
import collections.defaultdict
import dataclasses.dataclass
import dataclasses.field
import datetime.datetime
import enum.Enum
import langchain_core.output_parsers.StrOutputParser
import langchain_core.prompts.ChatPromptTemplate
import os
import random
import time
import typing.Any
import typing.Dict
import typing.List
import typing.Optional
Purpose: Core Python functionality:
– collections.defaultdict: Specialized container datatypes
– dataclasses.dataclass: Simplified class definitions with @dataclass
– dataclasses.field: Simplified class definitions with @dataclass
– datetime.datetime: Date and time handling
– enum.Enum: Enumeration support
– langchain_core.output_parsers.StrOutputParser: Regular expression operations
– langchain_core.prompts.ChatPromptTemplate: Regular expression operations
– os: Environment variable access and system operations
– random: Random number generation
– time: Time-related functions
– typing.Any: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.Dict: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.List: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.Optional: Type hints for better code documentation (List, Dict, Any, Optional)
LangChain Imports
from langchain_openai.ChatOpenAI
Purpose: LangChain framework components: – LLM Interface: Wrapper for OpenAI models – Chains: Composable building blocks for complex workflows
Other Imports
from dotenv.load_dotenv
Purpose: Additional dependencies for specialized functionality.
Classes
Class: DeploymentStatus
Lines 58-65
Inherits from: Enum
Purpose:
Status of canary deployment
Class: CanaryMetrics
Lines 69-107
Purpose:
Metrics for canary and production versions
Methods:
get_production_error_rate(self)
Lines 85-89
Purpose:
Calculate production error rate
Parameters:
Returns: float
get_canary_error_rate(self)
Lines 91-95
Purpose:
Calculate canary error rate
Parameters:
Returns: float
get_production_avg_latency(self)
Lines 97-101
Purpose:
Calculate average production latency
Parameters:
Returns: float
get_canary_avg_latency(self)
Lines 103-107
Purpose:
Calculate average canary latency
Parameters:
Returns: float
Class: CanaryConfig
Lines 111-125
Purpose:
Configuration for canary deployment
Class: CanaryDeployment
Lines 128-373
Purpose:
Manages canary deployment with gradual traffic routing and
automatic rollback on issues.
Methods:
__init__(self, config)
Lines 134-161
Purpose:
Initialize canary deployment
Parameters:
– config: Config
route_request(self, query)
Lines 163-243
Purpose:
Route request to either production or canary based on traffic split
Args:
query: User query
Returns:
Response with version and metrics
Parameters:
– query: Input query or question
Returns: <ast.Subscript object at 0x000001A06D60EAD0>
_check_health(self)
Lines 245-289
Purpose:
Check canary health and trigger rollback if needed
Parameters:
_consider_ramp_up(self)
Lines 291-316
Purpose:
Consider increasing traffic to canary
Parameters:
_rollback(self)
Lines 318-324
Purpose:
Rollback to production version
Parameters:
promote_canary(self)
Lines 326-335
Purpose:
Promote canary to production
Parameters:
get_status_report(self)
Lines 337-373
Purpose:
Generate status report
Parameters:
Returns: str
Functions
demonstrate_canary_deployment()
Lines 376-486
Purpose:
Demonstrate canary deployment pattern
Key Concepts Summary
Pattern Overview
The Canary Deployment pattern provides a structured approach to building AI agents with specific capabilities and behaviors. This implementation leverages LangChain’s composable components to create a robust and maintainable solution.
Core Components
- Agent Class: Main implementation of the pattern logic
- Prompt Engineering: Carefully crafted prompts to guide LLM behavior
- Tool Integration: Optional tools for extended functionality
- Memory Management: State handling across interactions
- Output Parsing: Structured response processing
When to Use
✅ Good for: – Scenarios requiring this specific pattern’s strengths – Applications needing structured agent behavior – Systems where explainability is important – Production environments requiring reliability
❌ Not ideal for: – Simple single-prompt tasks – Scenarios requiring different pattern characteristics – Resource-constrained environments (if computationally intensive)
Best Practices Demonstrated
1. Environment Configuration
- ✅ Use
.envfiles for API keys - ✅ Never hardcode credentials
- ✅ Validate environment variables on startup
2. Error Handling
- ✅ Graceful degradation on failures
- ✅ Informative error messages
- ✅ Logging for debugging
3. Prompt Engineering
- ✅ Clear, specific instructions
- ✅ Structured output formats
- ✅ Few-shot examples when beneficial
4. Code Organization
- ✅ Separation of concerns (class-based design)
- ✅ Type hints for clarity
- ✅ Docstrings for documentation
5. Performance Optimization
- ✅ Appropriate temperature settings
- ✅ Token budget management
- ✅ Caching where applicable
LangChain Components Used
- ChatOpenAI: OpenAI model interface
- Chains: Sequential component composition
Why LangChain? – Composable abstractions for LLM applications – Rich ecosystem of integrations – Production-ready patterns – Active community and development
Execution Flow
1. Initialize agent with configuration
↓
2. Load environment and dependencies
↓
3. Prepare prompt with input data
↓
4. Invoke LLM through LangChain
↓
5. Parse and validate response
↓
6. Return formatted result
Key Decision Points: – Model selection (temperature, model type) – Prompt structure (few-shot vs zero-shot) – Error handling strategy – Output format validation
Performance Considerations
Computational Cost
- LLM Calls: Primary cost factor (based on tokens)
- Latency: Depends on model and input size
- Memory: Minimal for most patterns
Optimization Strategies
- Prompt Optimization: Minimize tokens while maintaining clarity
- Caching: Cache repeated queries/responses
- Model Selection: Use smaller models when appropriate
- Batch Processing: Group similar requests
- Streaming: For long responses, stream tokens
Cost Estimates (Approximate)
- GPT-3.5-turbo: ~$0.002 per request
- GPT-4: ~$0.03 per request
- GPT-4-turbo: ~$0.01 per request
Actual costs vary based on input/output token counts
Common Pitfalls to Avoid
1. Prompt Design Issues
❌ Vague or ambiguous instructions
✅ Clear, specific, testable prompts
2. Error Handling
❌ Ignoring API errors or rate limits
✅ Implement retry logic and graceful degradation
3. Token Management
❌ Exceeding model token limits
✅ Truncate inputs, chunk processing
4. Security
❌ Exposing API keys in code
✅ Use environment variables
5. Testing
❌ Only testing happy paths
✅ Test edge cases, failures, malformed inputs
6. Cost Management
❌ Unmonitored API usage
✅ Implement budgets, alerts, and monitoring
Further Improvements
Production Enhancements
- Monitoring: Add logging, metrics, tracing
- Validation: Input/output schema validation
- Rate Limiting: Implement request throttling
- Caching: Redis or in-memory cache for responses
- Async Support: Non-blocking operations for scalability
Feature Extensions
- Multi-Model Support: Switch between providers
- Fallback Models: Graceful degradation strategy
- Custom Tools: Domain-specific function calling
- Advanced Memory: Vector stores for long-term memory
- Evaluation: Automated testing and benchmarking
Code Quality
- Testing: Unit tests, integration tests
- Documentation: API docs, usage examples
- Type Safety: Stricter type hints
- Configuration: Externalized config files
Related Patterns
- Pattern 153
- Pattern 155
Pattern Families: – Reasoning Patterns (001-011) – Multi-Agent Patterns (012-019) – Tool Use Patterns (020-025) – Memory Patterns (026-031) – And many more…
Summary
The Canary Deployment pattern provides a powerful approach to building AI agents with specific capabilities. This implementation demonstrates:
✅ Key Strengths: – Clean, maintainable code structure – LangChain integration for composability – Type hints and documentation – Production-ready patterns
🎯 Best Use Cases: – Applications requiring this pattern’s characteristics – Systems needing structured agent behavior – Production environments requiring reliability
📚 Learning Resources: – LangChain Documentation: https://python.langchain.com/ – Pattern Documentation: See related patterns – OpenAI API Reference: https://platform.openai.com/docs/
File: 154_canary_deployment.py
Total Lines: 491
Pattern Category: AI Agent Design Pattern #154
Generated from actual implementation code
For educational and reference purposes