"""
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

1. Agent Class: Main implementation of the pattern logic
2. Prompt Engineering: Carefully crafted prompts to guide LLM behavior
3. Tool Integration: Optional tools for extended functionality
4. Memory Management: State handling across interactions
5. 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 .env files 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

1. Prompt Optimization: Minimize tokens while maintaining clarity
2. Caching: Cache repeated queries/responses
3. Model Selection: Use smaller models when appropriate
4. Batch Processing: Group similar requests
5. 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

1. Monitoring: Add logging, metrics, tracing
2. Validation: Input/output schema validation
3. Rate Limiting: Implement request throttling
4. Caching: Redis or in-memory cache for responses
5. Async Support: Non-blocking operations for scalability

Feature Extensions

1. Multi-Model Support: Switch between providers
2. Fallback Models: Graceful degradation strategy
3. Custom Tools: Domain-specific function calling
4. Advanced Memory: Vector stores for long-term memory
5. Evaluation: Automated testing and benchmarking

Code Quality

1. Testing: Unit tests, integration tests
2. Documentation: API docs, usage examples
3. Type Safety: Stricter type hints
4. 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