"""
Pattern 106: Contingency Planning
Description:
The Contingency Planning pattern prepares agents to handle unexpected events
and failures by creating alternative plans before they're needed. This pattern
involves anticipating potential problems, creating backup plans, and enabling
quick adaptation when things don't go as expected.
Contingency planning is crucial for reliable agent systems that operate in
uncertain, dynamic environments. Rather than reacting to failures after they
occur, agents proactively identify risks, assess their likelihood and impact,
and prepare alternative strategies. This reduces downtime, improves robustness,
and enables graceful degradation when primary plans fail.
The pattern includes risk identification, scenario planning, plan B/C/D
creation, trigger condition specification, and rapid plan switching. It
balances the cost of planning for contingencies against the benefit of
being prepared for failures.
Key Components:
1. Risk Identifier: Detects potential failure points
2. Scenario Generator: Creates alternative scenarios
3. Contingency Creator: Develops backup plans
4. Trigger Monitor: Watches for contingency conditions
5. Plan Switcher: Activates alternative plans
6. Impact Assessor: Evaluates risk severity
7. Recovery Planner: Plans return to primary plan
Contingency Types:
1. Alternative Action: Different way to achieve same goal
2. Fallback Goal: Reduced/modified objective
3. Resource Substitution: Use different resources
4. Path Alternative: Different sequence of steps
5. Graceful Degradation: Accept reduced functionality
6. Abort and Recover: Abandon task, clean up
Risk Assessment:
1. Likelihood: Probability of occurrence (low/medium/high)
2. Impact: Severity of consequences (minor/moderate/critical)
3. Priority: Likelihood × Impact
4. Mitigation Cost: Resources to prepare contingency
Planning Phases:
1. Risk Identification: What could go wrong?
2. Impact Analysis: How bad would it be?
3. Contingency Development: What's plan B?
4. Trigger Specification: When to switch plans?
5. Monitoring: Watch for triggers
6. Activation: Switch to contingency
7. Recovery: Return to normal operations
Use Cases:
- Mission-critical operations
- Uncertain environments
- Resource-constrained systems
- High-stakes decision making
- Autonomous systems
- Real-time operations
- Safety-critical applications
Advantages:
- Reduces response time to failures
- Improves system reliability
- Enables graceful degradation
- Proactive vs reactive approach
- Builds resilience
- Reduces costly downtime
- Increases confidence
Challenges:
- Computational cost of planning
- Predicting all possible failures
- Balancing thoroughness vs. efficiency
- Maintaining multiple plans
- Detecting when to switch
- Avoiding over-planning
- Resource allocation for contingencies
LangChain Implementation:
This implementation uses LangChain for:
- LLM-based risk identification
- Alternative plan generation
- Scenario analysis
- Natural language contingency description
Production Considerations:
- Focus on high-impact, high-probability risks
- Use tiered contingency levels (B, C, D plans)
- Implement fast plan switching
- Monitor trigger conditions efficiently
- Test contingency plans regularly
- Document all contingencies
- Balance planning cost vs. benefit
- Enable human override when needed
"""
import os
from typing import List, Dict, Any, Optional
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from dotenv import load_dotenv
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import ChatOpenAI
load_dotenv()
class RiskLevel(Enum):
"""Risk likelihood levels."""
LOW = "low"
MEDIUM = "medium"
HIGH = "high"
class ImpactLevel(Enum):
"""Impact severity levels."""
MINOR = "minor"
MODERATE = "moderate"
CRITICAL = "critical"
class PlanStatus(Enum):
"""Status of a plan."""
PRIMARY = "primary"
CONTINGENCY = "contingency"
ACTIVE = "active"
FAILED = "failed"
SUCCEEDED = "succeeded"
@dataclass
class Risk:
"""
Represents a potential risk or failure point.
Attributes:
risk_id: Unique identifier
description: Risk description
likelihood: Probability of occurrence
impact: Severity of consequences
trigger_condition: Condition that indicates risk occurred
affected_steps: Plan steps affected by this risk
"""
risk_id: str
description: str
likelihood: RiskLevel
impact: ImpactLevel
trigger_condition: str
affected_steps: List[str] = field(default_factory=list)
@property
def priority(self) -> int:
"""Calculate risk priority."""
likelihood_scores = {RiskLevel.LOW: 1, RiskLevel.MEDIUM: 2, RiskLevel.HIGH: 3}
impact_scores = {ImpactLevel.MINOR: 1, ImpactLevel.MODERATE: 2, ImpactLevel.CRITICAL: 3}
return likelihood_scores[self.likelihood] * impact_scores[self.impact]
@dataclass
class Plan:
"""
Represents a plan (primary or contingency).
Attributes:
plan_id: Unique identifier
description: Plan description
steps: List of action steps
status: Plan status
parent_plan_id: ID of plan this is contingency for
triggers: Conditions that activate this plan
cost: Estimated cost/resources
success_criteria: How to know if plan succeeded
"""
plan_id: str
description: str
steps: List[str]
status: PlanStatus
parent_plan_id: Optional[str] = None
triggers: List[str] = field(default_factory=list)
cost: float = 1.0
success_criteria: List[str] = field(default_factory=list)
activated_at: Optional[datetime] = None
completed_at: Optional[datetime] = None
class ContingencyPlanner:
"""
Contingency planning system that creates and manages backup plans.
This planner identifies risks, creates alternative plans, monitors
for trigger conditions, and switches to contingencies when needed.
"""
def __init__(self, temperature: float = 0.4):
"""
Initialize contingency planner.
Args:
temperature: LLM temperature
"""
self.llm = ChatOpenAI(temperature=temperature, model="gpt-3.5-turbo")
self.plans: Dict[str, Plan] = {}
self.risks: Dict[str, Risk] = {}
self.active_plan_id: Optional[str] = None
self.plan_counter = 0
self.risk_counter = 0
self.event_log: List[Dict[str, Any]] = []
def create_primary_plan(
self,
description: str,
steps: List[str]
) -> Plan:
"""
Create primary plan.
Args:
description: Plan description
steps: Plan steps
Returns:
Created plan
"""
self.plan_counter += 1
plan = Plan(
plan_id=f"plan_{self.plan_counter}",
description=description,
steps=steps,
status=PlanStatus.PRIMARY
)
self.plans[plan.plan_id] = plan
self.active_plan_id = plan.plan_id
return plan
def identify_risks(self, plan: Plan) -> List[Risk]:
"""
Identify potential risks in a plan using LLM.
Args:
plan: Plan to analyze
Returns:
List of identified risks
"""
prompt = ChatPromptTemplate.from_template(
"Analyze this plan and identify 3-5 potential risks or failure points:\n\n"
"Plan: {description}\n"
"Steps:\n{steps}\n\n"
"For each risk, provide:\n"
"- Description of what could go wrong\n"
"- Likelihood (low/medium/high)\n"
"- Impact (minor/moderate/critical)\n"
"- Trigger condition (how to detect it happened)\n\n"
"Format each risk as:\n"
"Risk: [description]\n"
"Likelihood: [level]\n"
"Impact: [level]\n"
"Trigger: [condition]\n"
"---\n"
)
steps_text = "\n".join(f"{i+1}. {step}" for i, step in enumerate(plan.steps))
chain = prompt | self.llm | StrOutputParser()
result = chain.invoke({
"description": plan.description,
"steps": steps_text
})
# Parse risks from LLM output
risks = []
risk_blocks = result.split("---")
for block in risk_blocks:
if not block.strip():
continue
lines = [line.strip() for line in block.strip().split('\n')]
risk_desc = ""
likelihood = RiskLevel.MEDIUM
impact = ImpactLevel.MODERATE
trigger = ""
for line in lines:
if line.startswith("Risk:"):
risk_desc = line[5:].strip()
elif line.startswith("Likelihood:"):
level = line[11:].strip().lower()
likelihood = RiskLevel(level) if level in [l.value for l in RiskLevel] else RiskLevel.MEDIUM
elif line.startswith("Impact:"):
level = line[7:].strip().lower()
impact = ImpactLevel(level) if level in [i.value for i in ImpactLevel] else ImpactLevel.MODERATE
elif line.startswith("Trigger:"):
trigger = line[8:].strip()
if risk_desc and trigger:
self.risk_counter += 1
risk = Risk(
risk_id=f"risk_{self.risk_counter}",
description=risk_desc,
likelihood=likelihood,
impact=impact,
trigger_condition=trigger
)
self.risks[risk.risk_id] = risk
risks.append(risk)
return risks
def create_contingency_plan(
self,
primary_plan: Plan,
risk: Risk,
level: str = "B"
) -> Plan:
"""
Create contingency plan for a specific risk.
Args:
primary_plan: Primary plan
risk: Risk to mitigate
level: Contingency level (B, C, D, etc.)
Returns:
Contingency plan
"""
prompt = ChatPromptTemplate.from_template(
"Create a contingency plan (Plan {level}) for this situation:\n\n"
"Primary Plan: {plan_description}\n"
"Risk: {risk_description}\n"
"Trigger: {trigger}\n\n"
"Design an alternative plan that:\n"
"1. Achieves the same or similar goal\n"
"2. Avoids or mitigates the identified risk\n"
"3. Is practical and feasible\n\n"
"Provide 3-5 concrete steps for the contingency plan.\n"
"List each step on a new line, numbered.\n\n"
"Steps:"
)
chain = prompt | self.llm | StrOutputParser()
result = chain.invoke({
"level": level,
"plan_description": primary_plan.description,
"risk_description": risk.description,
"trigger": risk.trigger_condition
})
# Parse steps
lines = [line.strip() for line in result.strip().split('\n') if line.strip()]
steps = []
for line in lines:
# Remove numbering
step = line
for prefix in ["1.", "2.", "3.", "4.", "5.", "1)", "2)", "3)", "4)", "5)", "-", "*"]:
if step.startswith(prefix):
step = step[len(prefix):].strip()
break
if step:
steps.append(step)
# Create contingency plan
self.plan_counter += 1
contingency = Plan(
plan_id=f"plan_{self.plan_counter}",
description=f"Plan {level}: {primary_plan.description} (contingency for {risk.description})",
steps=steps,
status=PlanStatus.CONTINGENCY,
parent_plan_id=primary_plan.plan_id,
triggers=[risk.trigger_condition]
)
self.plans[contingency.plan_id] = contingency
return contingency
def create_tiered_contingencies(
self,
primary_plan: Plan,
num_tiers: int = 2
) -> List[Plan]:
"""
Create multiple tiers of contingency plans.
Args:
primary_plan: Primary plan
num_tiers: Number of contingency tiers
Returns:
List of contingency plans
"""
# Identify risks
risks = self.identify_risks(primary_plan)
# Sort by priority
risks.sort(key=lambda r: r.priority, reverse=True)
# Create contingencies for top risks
contingencies = []
levels = ["B", "C", "D", "E", "F"]
for i in range(min(num_tiers, len(risks), len(levels))):
contingency = self.create_contingency_plan(
primary_plan,
risks[i],
level=levels[i]
)
contingencies.append(contingency)
return contingencies
def check_trigger_conditions(
self,
observed_events: List[str]
) -> Optional[Plan]:
"""
Check if any contingency should be activated.
Args:
observed_events: List of observed events/conditions
Returns:
Contingency plan to activate, or None
"""
# Check contingency plans for matching triggers
contingency_plans = [
p for p in self.plans.values()
if p.status == PlanStatus.CONTINGENCY
]
for plan in contingency_plans:
for trigger in plan.triggers:
for event in observed_events:
if trigger.lower() in event.lower() or event.lower() in trigger.lower():
return plan
return None
def activate_contingency(
self,
contingency_plan: Plan,
reason: str
):
"""
Activate a contingency plan.
Args:
contingency_plan: Contingency to activate
reason: Reason for activation
"""
# Deactivate current plan
if self.active_plan_id and self.active_plan_id in self.plans:
self.plans[self.active_plan_id].status = PlanStatus.FAILED
# Activate contingency
contingency_plan.status = PlanStatus.ACTIVE
contingency_plan.activated_at = datetime.now()
self.active_plan_id = contingency_plan.plan_id
# Log event
self.event_log.append({
"timestamp": datetime.now(),
"event": "contingency_activated",
"plan_id": contingency_plan.plan_id,
"reason": reason
})
def simulate_execution(
self,
plan: Plan,
simulate_failure: Optional[str] = None
) -> Dict[str, Any]:
"""
Simulate plan execution.
Args:
plan: Plan to execute
simulate_failure: Simulate failure at this step
Returns:
Execution result
"""
results = []
for i, step in enumerate(plan.steps):
# Simulate failure
if simulate_failure and simulate_failure.lower() in step.lower():
return {
"success": False,
"completed_steps": results,
"failed_at": step,
"reason": f"Simulated failure at: {step}"
}
# Successful step
results.append({
"step": step,
"status": "completed",
"timestamp": datetime.now()
})
plan.status = PlanStatus.SUCCEEDED
plan.completed_at = datetime.now()
return {
"success": True,
"completed_steps": results,
"plan_id": plan.plan_id
}
def get_plan_summary(self) -> str:
"""Get summary of all plans."""
lines = []
# Primary plans
primary = [p for p in self.plans.values() if p.status in [PlanStatus.PRIMARY, PlanStatus.ACTIVE]]
if primary:
lines.append("PRIMARY PLAN:")
for plan in primary:
active = " (ACTIVE)" if plan.plan_id == self.active_plan_id else ""
lines.append(f" {plan.plan_id}{active}: {plan.description}")
for i, step in enumerate(plan.steps, 1):
lines.append(f" {i}. {step}")
# Contingencies
contingencies = [p for p in self.plans.values() if p.status == PlanStatus.CONTINGENCY]
if contingencies:
lines.append("\nCONTINGENCY PLANS:")
for plan in contingencies:
lines.append(f" {plan.plan_id}: {plan.description}")
if plan.triggers:
lines.append(f" Triggers: {', '.join(plan.triggers[:2])}...")
for i, step in enumerate(plan.steps, 1):
lines.append(f" {i}. {step}")
return "\n".join(lines)
def get_risk_summary(self) -> str:
"""Get summary of identified risks."""
lines = ["IDENTIFIED RISKS (by priority):"]
risks = sorted(self.risks.values(), key=lambda r: r.priority, reverse=True)
for risk in risks:
priority = risk.priority
lines.append(
f"\n [{priority}] {risk.description}\n"
f" Likelihood: {risk.likelihood.value}, Impact: {risk.impact.value}\n"
f" Trigger: {risk.trigger_condition}"
)
return "\n".join(lines)
def demonstrate_contingency_planning():
"""Demonstrate contingency planning pattern."""
print("=" * 80)
print("CONTINGENCY PLANNING PATTERN DEMONSTRATION")
print("=" * 80)
# Example 1: Basic contingency planning
print("\n" + "=" * 80)
print("Example 1: Creating Primary and Contingency Plans")
print("=" * 80)
planner = ContingencyPlanner()
# Create primary plan
primary = planner.create_primary_plan(
"Deploy new software version",
[
"Run automated tests",
"Build production package",
"Deploy to staging",
"Run smoke tests",
"Deploy to production"
]
)
print("\nPrimary Plan:")
print(f" {primary.description}")
for i, step in enumerate(primary.steps, 1):
print(f" {i}. {step}")
print("\nIdentifying risks...")
risks = planner.identify_risks(primary)
print(f"\nIdentified {len(risks)} risks:")
for risk in risks:
print(f" • {risk.description}")
print(f" Likelihood: {risk.likelihood.value}, Impact: {risk.impact.value}")
print(f" Priority: {risk.priority}")
# Create contingency for highest priority risk
if risks:
top_risk = max(risks, key=lambda r: r.priority)
contingency = planner.create_contingency_plan(primary, top_risk, "B")
print(f"\nContingency Plan B (for: {top_risk.description}):")
for i, step in enumerate(contingency.steps, 1):
print(f" {i}. {step}")
# Example 2: Tiered contingencies
print("\n" + "=" * 80)
print("Example 2: Multi-Tier Contingency Planning")
print("=" * 80)
planner2 = ContingencyPlanner()
primary2 = planner2.create_primary_plan(
"Complete important presentation",
[
"Prepare slides",
"Rehearse presentation",
"Set up projector",
"Deliver presentation"
]
)
print("\nCreating tiered contingencies...")
contingencies = planner2.create_tiered_contingencies(primary2, num_tiers=3)
print(f"\nCreated {len(contingencies)} contingency tiers")
print(planner2.get_plan_summary())
# Example 3: Trigger detection and activation
print("\n" + "=" * 80)
print("Example 3: Contingency Activation")
print("=" * 80)
planner3 = ContingencyPlanner()
primary3 = planner3.create_primary_plan(
"Travel to meeting",
[
"Drive to meeting location",
"Park car",
"Enter building"
]
)
# Create simple contingency
planner3.risk_counter += 1
traffic_risk = Risk(
risk_id=f"risk_{planner3.risk_counter}",
description="Heavy traffic delays arrival",
likelihood=RiskLevel.MEDIUM,
impact=ImpactLevel.MODERATE,
trigger_condition="traffic jam detected"
)
planner3.risks[traffic_risk.risk_id] = traffic_risk
contingency3 = planner3.create_contingency_plan(primary3, traffic_risk, "B")
print("\nPrimary Plan:")
print(f" {primary3.description}")
print("\nContingency Plan B:")
print(f" {contingency3.description}")
print("\nSimulating execution with problem...")
observed_events = ["traffic jam detected", "delay expected"]
triggered_plan = planner3.check_trigger_conditions(observed_events)
if triggered_plan:
print(f"\n⚠️ Trigger detected! Activating: {triggered_plan.plan_id}")
planner3.activate_contingency(triggered_plan, "Traffic jam encountered")
print(f" Now executing: {triggered_plan.description}")
# Example 4: Risk prioritization
print("\n" + "=" * 80)
print("Example 4: Risk Assessment and Prioritization")
print("=" * 80)
planner4 = ContingencyPlanner()
primary4 = planner4.create_primary_plan(
"Launch product campaign",
[
"Finalize marketing materials",
"Schedule social media posts",
"Send press releases",
"Monitor campaign metrics"
]
)
print("\nAnalyzing risks...")
risks4 = planner4.identify_risks(primary4)
print(planner4.get_risk_summary())
# Example 5: Execution with fallback
print("\n" + "=" * 80)
print("Example 5: Execution with Automatic Fallback")
print("=" * 80)
planner5 = ContingencyPlanner()
primary5 = planner5.create_primary_plan(
"Process data file",
[
"Download file from server",
"Parse file contents",
"Validate data",
"Store in database"
]
)
# Create contingency
planner5.risk_counter += 1
download_risk = Risk(
risk_id=f"risk_{planner5.risk_counter}",
description="File download fails",
likelihood=RiskLevel.MEDIUM,
impact=ImpactLevel.MODERATE,
trigger_condition="download fails"
)
contingency5 = Plan(
plan_id="plan_backup",
description="Plan B: Use cached file",
steps=[
"Retrieve file from cache",
"Parse file contents",
"Validate data",
"Store in database"
],
status=PlanStatus.CONTINGENCY,
parent_plan_id=primary5.plan_id,
triggers=["download fails"]
)
planner5.plans[contingency5.plan_id] = contingency5
print("\nAttempting primary plan...")
result = planner5.simulate_execution(primary5, simulate_failure="download")
if not result["success"]:
print(f"❌ Primary plan failed: {result['reason']}")
# Check for contingency
events = ["download fails"]
triggered = planner5.check_trigger_conditions(events)
if triggered:
print(f"\n✓ Activating contingency: {triggered.plan_id}")
planner5.activate_contingency(triggered, result["reason"])
print("\nAttempting contingency plan...")
result2 = planner5.simulate_execution(triggered)
if result2["success"]:
print(f"✅ Contingency plan succeeded!")
# Example 6: Graceful degradation
print("\n" + "=" * 80)
print("Example 6: Graceful Degradation Strategy")
print("=" * 80)
planner6 = ContingencyPlanner()
primary6 = planner6.create_primary_plan(
"Generate comprehensive report",
[
"Collect data from all sources",
"Perform advanced analytics",
"Create interactive visualizations",
"Generate PDF report"
]
)
# Degraded plan
degraded = Plan(
plan_id="plan_degraded",
description="Plan B: Generate basic report (degraded)",
steps=[
"Collect data from primary source only",
"Perform basic statistics",
"Create simple charts",
"Generate text report"
],
status=PlanStatus.CONTINGENCY,
parent_plan_id=primary6.plan_id,
triggers=["data source unavailable", "time constraint"]
)
planner6.plans[degraded.plan_id] = degraded
print("\nPrimary Plan (Full Functionality):")
for i, step in enumerate(primary6.steps, 1):
print(f" {i}. {step}")
print("\nDegraded Plan (Reduced Functionality):")
for i, step in enumerate(degraded.steps, 1):
print(f" {i}. {step}")
print("\nStrategy: Accept reduced functionality to meet deadline")
# Example 7: Multiple contingency levels
print("\n" + "=" * 80)
print("Example 7: Cascade of Contingencies")
print("=" * 80)
planner7 = ContingencyPlanner()
primary7 = planner7.create_primary_plan(
"Connect to service",
["Connect to primary server"]
)
plan_b = Plan(
plan_id="plan_b",
description="Plan B: Connect to backup server",
steps=["Connect to backup server"],
status=PlanStatus.CONTINGENCY,
parent_plan_id=primary7.plan_id,
triggers=["primary server down"]
)
plan_c = Plan(
plan_id="plan_c",
description="Plan C: Use cached data",
steps=["Load cached data from local storage"],
status=PlanStatus.CONTINGENCY,
parent_plan_id=plan_b.plan_id,
triggers=["backup server down"]
)
plan_d = Plan(
plan_id="plan_d",
description="Plan D: Operate in offline mode",
steps=["Enable offline mode", "Queue operations"],
status=PlanStatus.CONTINGENCY,
parent_plan_id=plan_c.plan_id,
triggers=["no cached data"]
)
planner7.plans.update({
plan_b.plan_id: plan_b,
plan_c.plan_id: plan_c,
plan_d.plan_id: plan_d
})
print("\nContingency Cascade:")
print(" Primary: Connect to primary server")
print(" Plan B: Connect to backup server (if primary fails)")
print(" Plan C: Use cached data (if backup fails)")
print(" Plan D: Operate offline (if no cache)")
print("\nStrategy: Progressive degradation with multiple fallback levels")
# Example 8: Contingency metrics
print("\n" + "=" * 80)
print("Example 8: Contingency Planning Metrics")
print("=" * 80)
planner8 = ContingencyPlanner()
primary8 = planner8.create_primary_plan(
"Complete project milestone",
[
"Finish feature implementation",
"Write documentation",
"Conduct code review",
"Merge to main branch"
]
)
# Create multiple contingencies
planner8.create_tiered_contingencies(primary8, num_tiers=3)
print("\nContingency Planning Statistics:")
print(f" Total Plans: {len(planner8.plans)}")
print(f" Primary Plans: {sum(1 for p in planner8.plans.values() if p.status == PlanStatus.PRIMARY)}")
print(f" Contingency Plans: {sum(1 for p in planner8.plans.values() if p.status == PlanStatus.CONTINGENCY)}")
print(f" Identified Risks: {len(planner8.risks)}")
print(f" High-Priority Risks: {sum(1 for r in planner8.risks.values() if r.priority >= 6)}")
# Summary
print("\n" + "=" * 80)
print("SUMMARY: Contingency Planning Pattern")
print("=" * 80)
summary = """
The Contingency Planning pattern demonstrated:
1. BASIC CONTINGENCIES (Example 1):
- Risk identification using LLM
- Likelihood and impact assessment
- Priority calculation
- Alternative plan creation
- Trigger condition specification
2. TIERED PLANNING (Example 2):
- Multiple contingency levels (B, C, D)
- Risk-based prioritization
- Comprehensive backup strategies
- Structured alternatives
3. TRIGGER ACTIVATION (Example 3):
- Real-time monitoring
- Condition matching
- Automatic plan switching
- Event-driven adaptation
- Seamless transition
4. RISK PRIORITIZATION (Example 4):
- Priority scoring (likelihood × impact)
- Risk ranking
- Resource allocation guidance
- Focus on critical risks
5. AUTOMATIC FALLBACK (Example 5):
- Failure detection
- Contingency activation
- Execution retry
- Success tracking
- Resilient operation
6. GRACEFUL DEGRADATION (Example 6):
- Reduced functionality plans
- Partial goal achievement
- Quality-time tradeoffs
- Acceptable alternatives
7. CONTINGENCY CASCADE (Example 7):
- Multi-level fallbacks
- Progressive degradation
- Chain of alternatives
- Comprehensive coverage
8. METRICS TRACKING (Example 8):
- Plan statistics
- Risk counts
- Priority distribution
- Coverage assessment
KEY BENEFITS:
✓ Reduces response time to failures
✓ Improves system reliability
✓ Enables graceful degradation
✓ Proactive preparation
✓ Builds resilience
✓ Reduces costly downtime
✓ Increases confidence
✓ Supports high-stakes operations
USE CASES:
• Mission-critical operations
• Uncertain environments
• Resource-constrained systems
• High-stakes decision making
• Autonomous systems
• Real-time operations
• Safety-critical applications
• Production deployments
CONTINGENCY TYPES:
→ Alternative Action: Different approach
→ Fallback Goal: Modified objective
→ Resource Substitution: Different resources
→ Path Alternative: Different sequence
→ Graceful Degradation: Reduced functionality
→ Abort and Recover: Clean exit
BEST PRACTICES:
1. Focus on high-impact, high-probability risks
2. Use tiered contingency levels (B, C, D)
3. Implement fast plan switching
4. Monitor trigger conditions efficiently
5. Test contingency plans regularly
6. Document all contingencies clearly
7. Balance planning cost vs. benefit
8. Enable human override capability
TRADE-OFFS:
• Planning cost vs. preparedness benefit
• Coverage thoroughness vs. efficiency
• Multiple plans vs. maintenance burden
• Proactive vs. reactive approach
PRODUCTION CONSIDERATIONS:
→ Implement efficient trigger monitoring
→ Cache contingency plans for quick access
→ Test contingencies in simulation
→ Monitor plan activation frequency
→ Track contingency success rates
→ Update plans based on experience
→ Provide clear plan switching notifications
→ Enable rollback to primary plan
→ Log all plan transitions
→ Support human decision overrides
This pattern enables agents to handle unexpected failures
gracefully by preparing alternative plans before they're needed,
monitoring for trigger conditions, and switching quickly when
problems occur.
"""
print(summary)
if __name__ == "__main__":
demonstrate_contingency_planning()
Agentic Design Pattern: Contingency Planning
Pattern 106: Contingency Planning – Line-by-Line Explanation
Overview
This document provides a detailed line-by-line explanation of the Contingency Planning pattern implementation.
Pattern Description
Pattern 106: Contingency Planning
Description: The Contingency Planning pattern prepares agents to handle unexpected events and failures by creating alternative plans before they’re needed. This pattern involves anticipating potential problems, creating backup plans, and enabling quick adaptation when things don’t go as expected.
Contingency planning is crucial for reliable agent systems that operate in
uncertain, dynamic environments. Rather than reacting to failures after they
occur, agents proactively identify risks, assess their likelihood and impact,
and prepare alternative strategies. This reduces downtime, improves robustness,
and enables graceful degradation when primary plans fail.
The pattern includes risk identification, scenario planning, plan B/C/D
creation, trigger condition specification, and rapid plan switching. It
balances the cost of planning for contingencies against the benefit of
being prepared for failures.
Key Components: 1. Risk Identifier: Detects potential failure points 2. Scenario Generator: Creates alternative scenarios 3. Contingency Creator: Develops backup plans 4. Trigger Monitor: Watches for contingency conditions 5. Plan Switcher: Activates alternative plans 6. Impact Assessor: Evaluates risk severity 7. Recovery Planner: Plans return to primary plan
Contingency Types: 1. Alternative Action: Different way to achieve same goal 2. Fallback Goal: Reduced/modified objective 3. Resource Substitution: Use different resources 4. Path Alternative: Different sequence of steps 5. Graceful Degradation: Accept reduced functionality 6. Abort and Recover: Abandon task, clean up
Risk Assessment: 1. Likelihood: Probability of occurrence (low/medium/high) 2. Impact: Severity of consequences (minor/moderate/critical) 3. Priority: Likelihood × Impact 4. Mitigation Cost: Resources to prepare contingency
Planning Phases: 1. Risk Identification: What could go wrong? 2. Impact Analysis: How bad would it be? 3. Contingency Development: What’s plan B? 4. Trigger Specification: When to switch plans? 5. Monitoring: Watch for triggers 6. Activation: Switch to contingency 7. Recovery: Return to normal operations
Use Cases: – Mission-critical operations – Uncertain environments – Resource-constrained systems – High-stakes decision making – Autonomous systems – Real-time operations – Safety-critical applications
Advantages: – Reduces response time to failures – Improves system reliability – Enables graceful degradation – Proactive vs reactive approach – Builds resilience – Reduces costly downtime – Increases confidence
Challenges: – Computational cost of planning – Predicting all possible failures – Balancing thoroughness vs. efficiency – Maintaining multiple plans – Detecting when to switch – Avoiding over-planning – Resource allocation for contingencies
LangChain Implementation: This implementation uses LangChain for: – LLM-based risk identification – Alternative plan generation – Scenario analysis – Natural language contingency description
Production Considerations: – Focus on high-impact, high-probability risks – Use tiered contingency levels (B, C, D plans) – Implement fast plan switching – Monitor trigger conditions efficiently – Test contingency plans regularly – Document all contingencies – Balance planning cost vs. benefit – Enable human override when needed
File Statistics
- Total Lines: 1011
- Classes: 6
- Functions: 1
- Constants: 0
Module Documentation
"""
Pattern 106: Contingency Planning
Description:
The Contingency Planning pattern prepares agents to handle unexpected events
and failures by creating alternative plans before they're needed. This pattern
involves anticipating potential problems, creating backup plans, and enabling
quick adaptation when things don't go as expected.
Contingency planning is crucial for reliable agent systems that operate in
uncertain, dynamic environments. Rather than reacting to failures after they
occur, agents proactively identify risks, assess their likelihood and impact,
and prepare alternative strategies. This reduces downtime, improves robustness,
and enables graceful degradation when primary plans fail.
The pattern includes risk identification, scenario planning, plan B/C/D
creation, trigger condition specification, and rapid plan switching. It
balances the cost of planning for contingencies against the benefit of
being prepared for failures.
Key Components:
1. Risk Identifier: Detects potential failure points
2. Scenario Generator: Creates alternative scenarios
3. Contingency Creator: Develops backup plans
4. Trigger Monitor: Watches for contingency conditions
5. Plan Switcher: Activates alternative plans
6. Impact Assessor: Evaluates risk severity
7. Recovery Planner: Plans return to primary plan
Contingency Types:
1. Alternative Action: Different way to achieve same goal
2. Fallback Goal: Reduced/modified objective
3. Resource Substitution: Use different resources
4. Path Alternative: Different sequence of steps
5. Graceful Degradation: Accept reduced functionality
6. Abort and Recover: Abandon task, clean up
Risk Assessment:
1. Likelihood: Probability of occurrence (low/medium/high)
2. Impact: Severity of consequences (minor/moderate/critical)
3. Priority: Likelihood × Impact
4. Mitigation Cost: Resources to prepare contingency
Planning Phases:
1. Risk Identification: What could go wrong?
2. Impact Analysis: How bad would it be?
3. Contingency Development: What's plan B?
4. Trigger Specification: When to switch plans?
5. Monitoring: Watch for triggers
6. Activation: Switch to contingency
7. Recovery: Return to normal operations
Use Cases:
- Mission-critical operations
- Uncertain environments
- Resource-constrained systems
- High-stakes decision making
- Autonomous systems
- Real-time operations
- Safety-critical applications
Advantages:
- Reduces response time to failures
- Improves system reliability
- Enables graceful degradation
- Proactive vs reactive approach
- Builds resilience
- Reduces costly downtime
- Increases confidence
Challenges:
- Computational cost of planning
- Predicting all possible failures
- Balancing thoroughness vs. efficiency
- Maintaining multiple plans
- Detecting when to switch
- Avoiding over-planning
- Resource allocation for contingencies
LangChain Implementation:
This implementation uses LangChain for:
- LLM-based risk identification
- Alternative plan generation
- Scenario analysis
- Natural language contingency description
Production Considerations:
- Focus on high-impact, high-probability risks
- Use tiered contingency levels (B, C, D plans)
- Implement fast plan switching
- Monitor trigger conditions efficiently
- Test contingency plans regularly
- Document all contingencies
- Balance planning cost vs. benefit
- Enable human override when needed
"""
Purpose: This module implements the Contingency Planning pattern, providing a structured approach to agent design and implementation.
Import Statements
Standard Library Imports
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 typing.Any
import typing.Dict
import typing.List
import typing.Optional
Purpose: Core Python functionality:
– 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
– 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: RiskLevel
Lines 112-116
Inherits from: Enum
Purpose:
Risk likelihood levels.
Class: ImpactLevel
Lines 119-123
Inherits from: Enum
Purpose:
Impact severity levels.
Class: PlanStatus
Lines 126-132
Inherits from: Enum
Purpose:
Status of a plan.
Class: Risk
Lines 136-160
Purpose:
Represents a potential risk or failure point.
Attributes:
risk_id: Unique identifier
description: Risk description
likelihood: Probability of occurrence
impact: Severity of consequences
trigger_condition: Condition that indicates risk occurred
affected_steps: Plan steps affected by this risk
Methods:
priority(self)
Lines 156-160
Decorators: property
Purpose:
Calculate risk priority.
Parameters:
Returns: int
Class: Plan
Lines 164-187
Purpose:
Represents a plan (primary or contingency).
Attributes:
plan_id: Unique identifier
description: Plan description
steps: List of action steps
status: Plan status
parent_plan_id: ID of plan this is contingency for
triggers: Conditions that activate this plan
cost: Estimated cost/resources
success_criteria: How to know if plan succeeded
Class: ContingencyPlanner
Lines 190-559
Purpose:
Contingency planning system that creates and manages backup plans.
This planner identifies risks, creates alternative plans, monitors
for trigger conditions, and switches to contingencies when needed.
Methods:
__init__(self, temperature)
Lines 198-211
Purpose:
Initialize contingency planner.
Args:
temperature: LLM temperature
Parameters:
– temperature: Sampling temperature (0-1)
create_primary_plan(self, description, steps)
Lines 213-239
Purpose:
Create primary plan.
Args:
description: Plan description
steps: Plan steps
Returns:
Created plan
Parameters:
– description: Description
– steps: Steps
Returns: Plan
identify_risks(self, plan)
Lines 241-314
Purpose:
Identify potential risks in a plan using LLM.
Args:
plan: Plan to analyze
Returns:
List of identified risks
Parameters:
– plan: Plan
Returns: <ast.Subscript object at 0x000001A06D6AB310>
create_contingency_plan(self, primary_plan, risk, level)
Lines 316-382
Purpose:
Create contingency plan for a specific risk.
Args:
primary_plan: Primary plan
risk: Risk to mitigate
level: Contingency level (B, C, D, etc.)
Returns:
Contingency plan
Parameters:
– primary_plan: Primary plan
– risk: Risk
– level: Level
Returns: Plan
create_tiered_contingencies(self, primary_plan, num_tiers)
Lines 384-417
Purpose:
Create multiple tiers of contingency plans.
Args:
primary_plan: Primary plan
num_tiers: Number of contingency tiers
Returns:
List of contingency plans
Parameters:
– primary_plan: Primary plan
– num_tiers: Num tiers
Returns: <ast.Subscript object at 0x000001A06D608C50>
check_trigger_conditions(self, observed_events)
Lines 419-444
Purpose:
Check if any contingency should be activated.
Args:
observed_events: List of observed events/conditions
Returns:
Contingency plan to activate, or None
Parameters:
– observed_events: Observed events
Returns: <ast.Subscript object at 0x000001A06D83C910>
activate_contingency(self, contingency_plan, reason)
Lines 446-473
Purpose:
Activate a contingency plan.
Args:
contingency_plan: Contingency to activate
reason: Reason for activation
Parameters:
– contingency_plan: Contingency plan
– reason: Reason
simulate_execution(self, plan, simulate_failure)
Lines 475-516
Purpose:
Simulate plan execution.
Args:
plan: Plan to execute
simulate_failure: Simulate failure at this step
Returns:
Execution result
Parameters:
– plan: Plan
– simulate_failure: Simulate failure
Returns: <ast.Subscript object at 0x000001A06D156C50>
get_plan_summary(self)
Lines 518-543
Purpose:
Get summary of all plans.
Parameters:
Returns: str
get_risk_summary(self)
Lines 545-559
Purpose:
Get summary of identified risks.
Parameters:
Returns: str
Functions
demonstrate_contingency_planning()
Lines 562-1006
Purpose:
Demonstrate contingency planning pattern.
Key Concepts Summary
Pattern Overview
The Contingency Planning 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 105
- Pattern 107
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 Contingency Planning 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: 106_contingency_planning.py
Total Lines: 1011
Pattern Category: AI Agent Design Pattern #106
Generated from actual implementation code
For educational and reference purposes
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