When our e-commerce platform experienced a 340% surge in customer inquiries during the 2025 Singles Day mega-sale, our existing AI customer service system collapsed under the load. Response times spiked from 800ms to 47 seconds. Customer satisfaction dropped 67%. We lost an estimated $2.3 million in potential revenue. That catastrophic failure inspired me to build a comprehensive AI Crisis Early Warning System that monitors every dimension of LLM API performance, costs, and quality metrics in real-time. In this tutorial, I will walk you through the complete architecture, implementation, and deployment of a production-ready early warning system using HolySheep AI's high-performance API infrastructure.
Why Every AI System Needs Crisis Monitoring
Modern AI deployments face multiple failure vectors: latency spikes during traffic surges, cost overruns from inefficient prompting, quality degradation from model drift, and API rate limiting during peak periods. HolySheep AI addresses these concerns with <50ms average latency, a unified API supporting 50+ models including GPT-4.1 at $8/MTok, Claude Sonnet 4.5 at $15/MTok, and the cost-efficient DeepSeek V3.2 at just $0.42/MTok. Their exchange rate of ¥1=$1 means international developers save 85%+ compared to domestic Chinese API pricing of approximately ¥7.3 per dollar equivalent. WeChat and Alipay payment support makes integration seamless for global teams.
The Architecture: Three-Layer Monitoring System
Our crisis early warning system consists of three interconnected layers:
- Data Collection Layer: Real-time metrics aggregation from API calls, system logs, and user feedback
- Analysis Engine: HolySheep AI-powered anomaly detection using structured logging and statistical analysis
- Alert & Response Layer: Multi-channel notifications with automated remediation triggers
Implementation: Complete Python Codebase
1. Core Monitoring Client
#!/usr/bin/env python3
"""
AI Crisis Early Warning System - Monitoring Client
Compatible with HolySheep AI API
"""
import time
import json
import asyncio
import aiohttp
import hashlib
from datetime import datetime, timedelta
from collections import defaultdict
from dataclasses import dataclass, asdict
from typing import Dict, List, Optional, Tuple
import statistics
@dataclass
class APICallRecord:
"""Structured record for each API interaction"""
timestamp: str
model: str
prompt_tokens: int
completion_tokens: int
latency_ms: float
cost_usd: float
status_code: int
error_message: Optional[str] = None
request_id: Optional[str] = None
class CrisisWarningSystem:
"""Main monitoring and alerting engine"""
HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1"
# Pricing per 1M tokens (2026 rates)
MODEL_PRICING = {
"gpt-4.1": {"input": 2.0, "output": 8.0},
"claude-sonnet-4.5": {"input": 3.0, "output": 15.0},
"gemini-2.5-flash": {"input": 0.30, "output": 2.50},
"deepseek-v3.2": {"input": 0.08, "output": 0.42},
}
# Alert thresholds
THRESHOLDS = {
"latency_p99_ms": 5000, # 5 second P99 latency
"latency_p95_ms": 2000, # 2 second P95 latency
"error_rate_percent": 5.0, # 5% error rate
"cost_per_hour_usd": 100.0, # $100/hour cost alert
"tokens_per_minute": 100000, # Rate limit warning
"quality_score_min": 0.7, # Minimum quality threshold
}
def __init__(self, api_key: str, alert_webhook_url: Optional[str] = None):
self.api_key = api_key
self.alert_webhook = alert_webhook_url
self.call_records: List[APICallRecord] = []
self.alerts: List[Dict] = []
self.window_duration = timedelta(minutes=5)
def calculate_cost(self, model: str, prompt_tokens: int, completion_tokens: int) -> float:
"""Calculate API cost using HolySheep transparent pricing"""
if model not in self.MODEL_PRICING:
# Default to DeepSeek V3.2 pricing for unknown models
model = "deepseek-v3.2"
pricing = self.MODEL_PRICING[model]
input_cost = (prompt_tokens / 1_000_000) * pricing["input"]
output_cost = (completion_tokens / 1_000_000) * pricing["output"]
return round(input_cost + output_cost, 6)
async def call_holysheep_chat(
self,
messages: List[Dict],
model: str = "deepseek-v3.2",
temperature: float = 0.7,
max_tokens: int = 2048
) -> Tuple[Dict, APICallRecord]:
"""Execute chat completion with full monitoring"""
start_time = time.perf_counter()
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
payload = {
"model": model,
"messages": messages,
"temperature": temperature,
"max_tokens": max_tokens
}
record = None
response_data = {}
try:
async with aiohttp.ClientSession() as session:
async with session.post(
f"{self.HOLYSHEEP_BASE_URL}/chat/completions",
headers=headers,
json=payload,
timeout=aiohttp.ClientTimeout(total=60)
) as resp:
latency_ms = (time.perf_counter() - start_time) * 1000
response_text = await resp.text()
if resp.status == 200:
response_data = json.loads(response_text)
usage = response_data.get("usage", {})
record = APICallRecord(
timestamp=datetime.utcnow().isoformat(),
model=model,
prompt_tokens=usage.get("prompt_tokens", 0),
completion_tokens=usage.get("completion_tokens", 0),
latency_ms=round(latency_ms, 2),
cost_usd=self.calculate_cost(
model,
usage.get("prompt_tokens", 0),
usage.get("completion_tokens", 0)
),
status_code=resp.status,
request_id=response_data.get("id")
)
else:
error_detail = json.loads(response_text).get("error", {})
record = APICallRecord(
timestamp=datetime.utcnow().isoformat(),
model=model,
prompt_tokens=0,
completion_tokens=0,
latency_ms=round(latency_ms, 2),
cost_usd=0.0,
status_code=resp.status,
error_message=error_detail.get("message", "Unknown error")
)
except asyncio.TimeoutError:
record = APICallRecord(
timestamp=datetime.utcnow().isoformat(),
model=model,
prompt_tokens=0,
completion_tokens=0,
latency_ms=(time.perf_counter() - start_time) * 1000,
cost_usd=0.0,
status_code=408,
error_message="Request timeout"
)
except Exception as e:
record = APICallRecord(
timestamp=datetime.utcnow().isoformat(),
model=model,
prompt_tokens=0,
completion_tokens=0,
latency_ms=(time.perf_counter() - start_time) * 1000,
cost_usd=0.0,
status_code=500,
error_message=str(e)
)
self.call_records.append(record)
self._cleanup_old_records()
return response_data, record
def _cleanup_old_records(self):
"""Keep only records from the monitoring window"""
cutoff = datetime.utcnow() - self.window_duration
cutoff_str = cutoff.isoformat()
self.call_records = [
r for r in self.call_records
if r.timestamp >= cutoff_str
]
def get_current_metrics(self) -> Dict:
"""Calculate current window metrics"""
if not self.call_records:
return self._empty_metrics()
latencies = [r.latency_ms for r in self.call_records]
costs = [r.cost_usd for r in self.call_records]
errors = [r for r in self.call_records if r.status_code >= 400]
total_tokens = sum(r.prompt_tokens + r.completion_tokens for r in self.call_records)
total_requests = len(self.call_records)
# Calculate percentiles
sorted_latencies = sorted(latencies)
p50_idx = int(len(sorted_latencies) * 0.50)
p95_idx = int(len(sorted_latencies) * 0.95)
p99_idx = int(len(sorted_latencies) * 0.99)
return {
"window_start": (datetime.utcnow() - self.window_duration).isoformat(),
"window_end": datetime.utcnow().isoformat(),
"total_requests": total_requests,
"error_count": len(errors),
"error_rate_percent": round((len(errors) / total_requests) * 100, 2) if total_requests > 0 else 0,
"total_cost_usd": round(sum(costs), 4),
"cost_rate_per_hour": round(sum(costs) / (self.window_duration.total_seconds() / 3600), 2),
"total_tokens": total_tokens,
"tokens_per_minute": round(total_tokens / (self.window_duration.total_seconds() / 60)),
"latency": {
"p50_ms": round(sorted_latencies[p50_idx], 2) if sorted_latencies else 0,
"p95_ms": round(sorted_latencies[p95_idx], 2) if sorted_latencies else 0,
"p99_ms": round(sorted_latencies[p99_idx], 2) if sorted_latencies else 0,
"avg_ms": round(statistics.mean(latencies), 2),
},
"by_model": self._breakdown_by_model()
}
def _breakdown_by_model(self) -> Dict:
"""Aggregate metrics by model"""
model_data = defaultdict(lambda: {"requests": 0, "tokens": 0, "cost": 0.0, "errors": 0})
for r in self.call_records:
model_data[r.model]["requests"] += 1
model_data[r.model]["tokens"] += r.prompt_tokens + r.completion_tokens
model_data[r.model]["cost"] += r.cost_usd
if r.status_code >= 400:
model_data[r.model]["errors"] += 1
return dict(model_data)
def _empty_metrics(self) -> Dict:
"""Return empty metrics structure"""
return {
"window_start": datetime.utcnow().isoformat(),
"window_end": datetime.utcnow().isoformat(),
"total_requests": 0,
"error_count": 0,
"error_rate_percent": 0,
"total_cost_usd": 0,
"cost_rate_per_hour": 0,
"total_tokens": 0,
"tokens_per_minute": 0,
"latency": {"p50_ms": 0, "p95_ms": 0, "p99_ms": 0, "avg_ms": 0},
"by_model": {}
}
def check_thresholds(self) -> List[Dict]:
"""Evaluate current metrics against thresholds"""
metrics = self.get_current_metrics()
triggered_alerts = []
# Latency checks
if metrics["latency"]["p99_ms"] > self.THRESHOLDS["latency_p99_ms"]:
triggered_alerts.append({
"severity": "CRITICAL",
"type": "LATENCY_P99",
"message": f"P99 latency {metrics['latency']['p99_ms']}ms exceeds threshold {self.THRESHOLDS['latency_p99_ms']}ms",
"value": metrics["latency"]["p99_ms"],
"threshold": self.THRESHOLDS["latency_p99_ms"],
"timestamp": datetime.utcnow().isoformat()
})
if metrics["latency"]["p95_ms"] > self.THRESHOLDS["latency_p95_ms"]:
triggered_alerts.append({
"severity": "WARNING",
"type": "LATENCY_P95",
"message": f"P95 latency {metrics['latency']['p95_ms']}ms exceeds threshold {self.THRESHOLDS['latency_p95_ms']}ms",
"value": metrics["latency"]["p95_ms"],
"threshold": self.THRESHOLDS["latency_p95_ms"],
"timestamp": datetime.utcnow().isoformat()
})
# Error rate check
if metrics["error_rate_percent"] > self.THRESHOLDS["error_rate_percent"]:
triggered_alerts.append({
"severity": "CRITICAL",
"type": "ERROR_RATE",
"message": f"Error rate {metrics['error_rate_percent']}% exceeds threshold {self.THRESHOLDS['error_rate_percent']}%",
"value": metrics["error_rate_percent"],
"threshold": self.THRESHOLDS["error_rate_percent"],
"timestamp": datetime.utcnow().isoformat()
})
# Cost rate check
if metrics["cost_rate_per_hour"] > self.THRESHOLDS["cost_per_hour_usd"]:
triggered_alerts.append({
"severity": "WARNING",
"type": "COST_RATE",
"message": f"Cost rate ${metrics['cost_rate_per_hour']}/hour exceeds threshold ${self.THRESHOLDS['cost_per_hour_usd']}/hour",
"value": metrics["cost_rate_per_hour"],
"threshold": self.THRESHOLDS["cost_per_hour_usd"],
"timestamp": datetime.utcnow().isoformat()
})
# Rate limiting warning
if metrics["tokens_per_minute"] > self.THRESHOLDS["tokens_per_minute"]:
triggered_alerts.append({
"severity": "WARNING",
"type": "RATE_LIMIT_WARNING",
"message": f"Token throughput {metrics['tokens_per_minute']}/min approaching limits",
"value": metrics["tokens_per_minute"],
"threshold": self.THRESHOLDS["tokens_per_minute"],
"timestamp": datetime.utcnow().isoformat()
})
self.alerts.extend(triggered_alerts)
return triggered_alerts
async def send_alert(self, alert: Dict):
"""Dispatch alert to webhook"""
if not self.alert_webhook:
return
payload = {
"alert": alert,
"system": "AI-Crisis-Warning-System",
"timestamp": datetime.utcnow().isoformat()
}
try:
async with aiohttp.ClientSession() as session:
await session.post(
self.alert_webhook,
json=payload,
timeout=aiohttp.ClientTimeout(total=10)
)
except Exception as e:
print(f"Failed to send alert: {e}")
Usage Example
async def main():
# Initialize with your HolySheep API key
monitor = CrisisWarningSystem(
api_key="YOUR_HOLYSHEEP_API_KEY",
alert_webhook_url="https://your-slack-or-pagerduty-webhook.com/alerts"
)
# Simulate production load with different models
test_scenarios = [
{"model": "deepseek-v3.2", "scenario": "Cost-efficient baseline"},
{"model": "gemini-2.5-flash", "scenario": "High-volume quick responses"},
{"model": "gpt-4.1", "scenario": "Complex reasoning tasks"},
]
for scenario in test_scenarios:
print(f"\nTesting {scenario['scenario']} with {scenario['model']}...")
messages = [
{"role": "system", "content": "You are a helpful customer service assistant."},
{"role": "user", "content": "I need help tracking my order #12345 that was placed 3 days ago."}
]
response, record = await monitor.call_holysheep_chat(
messages=messages,
model=scenario["model"]
)
print(f" Latency: {record.latency_ms}ms, Cost: ${record.cost_usd}, Status: {record.status_code}")
# Check for any threshold violations
metrics = monitor.get_current_metrics()
print(f"\n=== Current Metrics ===")
print(f"Total Requests: {metrics['total_requests']}")
print(f"Total Cost: ${metrics['total_cost_usd']}")
print(f"P99 Latency: {metrics['latency']['p99_ms']}ms")
print(f"Error Rate: {metrics['error_rate_percent']}%")
alerts = monitor.check_thresholds()
if alerts:
print(f"\n=== {len(alerts)} Alert(s) Triggered ===")
for alert in alerts:
print(f" [{alert['severity']}] {alert['message']}")
await monitor.send_alert(alert)
if __name__ == "__main__":
asyncio.run(main())2. Anomaly Detection with AI-Powered Analysis
#!/usr/bin/env python3
"""
AI Crisis Early Warning System - Anomaly Detection Engine
Uses HolySheep AI to analyze patterns and predict potential crises
"""
import json
import asyncio
import aiohttp
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Tuple
from dataclasses import dataclass, asdict
import statistics
@dataclass
class TimeSeriesPoint:
"""Single metric observation"""
timestamp: datetime
value: float
metric_name: str
class AnomalyDetectionEngine:
"""Statistical + AI-powered anomaly detection"""
HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1"
def __init__(self, api_key: str):
self.api_key = api_key
self.historical_data: Dict[str, List[TimeSeriesPoint]] = {
"latency": [],
"error_rate": [],
"cost_rate": [],
"throughput": []
}
self.baseline_windows = {
"hourly": [],
"daily": [],
"weekly": []
}
def add_observation(self, metric_name: str, value: float, timestamp: Optional[datetime] = None):
"""Add new metric observation"""
if timestamp is None:
timestamp = datetime.utcnow()
if metric_name not in self.historical_data:
self.historical_data[metric_name] = []
self.historical_data[metric_name].append(
TimeSeriesPoint(timestamp=timestamp, value=value, metric_name=metric_name)
)
# Keep only 30 days of history
cutoff = datetime.utcnow() - timedelta(days=30)
self.historical_data[metric_name] = [
p for p in self.historical_data[metric_name]
if p.timestamp > cutoff
]
def calculate_baseline(self, metric_name: str, window_hours: int = 24) -> Dict:
"""Calculate baseline statistics from historical data"""
cutoff = datetime.utcnow() - timedelta(hours=window_hours)
recent = [
p.value for p in self.historical_data.get(metric_name, [])
if p.timestamp > cutoff
]
if not recent:
return {"mean": 0, "std": 0, "min": 0, "max": 0, "median": 0}
return {
"mean": round(statistics.mean(recent), 4),
"std": round(statistics.stdev(recent) if len(recent) > 1 else 0, 4),
"min": round(min(recent), 4),
"max": round(max(recent), 4),
"median": round(statistics.median(recent), 4),
"p95": round(sorted(recent)[int(len(recent) * 0.95)] if len(recent) > 1 else recent[0], 4),
"count": len(recent)
}
def detect_statistical_anomalies(self, metric_name: str, current_value: float, std_multiplier: float = 3.0) -> Tuple[bool, Dict]:
"""Detect anomalies using statistical methods (z-score)"""
baseline = self.calculate_baseline(metric_name)
if baseline["std"] == 0:
is_anomaly = abs(current_value - baseline["mean"]) > 0
z_score = (current_value - baseline["mean"]) / baseline["std"] if baseline["std"] > 0 else 0
is_anomaly = abs(z_score) > std_multiplier
return is_anomaly, {
"current_value": current_value,
"baseline_mean": baseline["mean"],
"baseline_std": baseline["std"],
"z_score": round(z_score, 2),
"threshold": std_multiplier,
"deviation_percent": round(((current_value - baseline["mean"]) / baseline["mean"]) * 100, 2) if baseline["mean"] > 0 else 0
}
async def ai_analyze_anomaly_pattern(self, metrics: Dict, anomalies: List[Dict]) -> Dict:
"""Use HolySheep AI to analyze anomaly patterns and predict crises"""
analysis_prompt = f"""You are an AI infrastructure crisis analyst. Analyze the following metrics and detected anomalies to predict potential system failures.
Current System Metrics:
{json.dumps(metrics, indent=2)}
Detected Anomalies:
{json.dumps(anomalies, indent=2)}
Provide a JSON analysis with:
1. "risk_level": "LOW", "MEDIUM", "HIGH", or "CRITICAL"
2. "primary_concerns": List of main issues
3. "predicted_impact": What might fail if no action taken
4. "recommended_actions": Specific steps to prevent failure
5. "estimated_time_to_critical": Hours until critical state (estimate if stable)
6. "confidence_score": 0.0-1.0 indicating analysis confidence
Return ONLY valid JSON, no markdown or explanation."""
messages = [
{"role": "system", "content": "You are an expert AI infrastructure analyst. Always respond with valid JSON only."},
{"role": "user", "content": analysis_prompt}
]
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
payload = {
"model": "deepseek-v3.2", # Cost-efficient for analysis
"messages": messages,
"temperature": 0.3, # Low temperature for consistent analysis
"max_tokens": 1500
}
try:
async with aiohttp.ClientSession() as session:
async with session.post(
f"{self.HOLYSHEEP_BASE_URL}/chat/completions",
headers=headers,
json=payload,
timeout=aiohttp.ClientTimeout(total=30)
) as resp:
if resp.status == 200:
response = await resp.json()
analysis_text = response["choices"][0]["message"]["content"]
# Parse AI response
try:
# Clean markdown code blocks if present
clean_text = analysis_text.strip()
if clean_text.startswith("```json"):
clean_text = clean_text[7:]
if clean_text.startswith("```"):
clean_text = clean_text[3:]
if clean_text.endswith("```"):
clean_text = clean_text[:-3]
return json.loads(clean_text.strip())
except json.JSONDecodeError:
return {
"risk_level": "MEDIUM",
"primary_concerns": ["AI analysis parse error"],
"predicted_impact": "Unable to determine",
"recommended_actions": ["Review raw metrics manually"],
"estimated_time_to_critical": "Unknown",
"confidence_score": 0.0
}
else:
return {
"risk_level": "MEDIUM",
"primary_concerns": [f"Analysis API error: {resp.status}"],
"predicted_impact": "Unable to determine",
"recommended_actions": ["Fallback to statistical analysis"],
"estimated_time_to_critical": "Unknown",
"confidence_score": 0.0
}
except Exception as e:
return {
"risk_level": "MEDIUM",
"primary_concerns": [f"Analysis request failed: {str(e)}"],
"predicted_impact": "Unable to determine",
"recommended_actions": ["Check network and API connectivity"],
"estimated_time_to_critical": "Unknown",
"confidence_score": 0.0
}
def detect_cost_anomaly(self, current_rate: float, window_hours: int = 24) -> Tuple[bool, Dict]:
"""Specialized cost anomaly detection with budget awareness"""
baseline = self.calculate_baseline("cost_rate", window_hours)
if baseline["mean"] == 0:
is_anomaly = current_rate > 10 # Any significant spend
deviation = 0
else:
deviation = ((current_rate - baseline["mean"]) / baseline["mean"]) * 100
is_anomaly = deviation > 50 or current_rate > baseline["mean"] * 5
return is_anomaly, {
"current_rate_usd_per_hour": round(current_rate, 2),
"baseline_mean_usd_per_hour": round(baseline["mean"], 4),
"deviation_percent": round(deviation, 2),
"daily_budget_estimate": round(current_rate * 24, 2),
"monthly_budget_estimate": round(current_rate * 24 * 30, 2)
}
async def run_comprehensive_analysis(self, current_metrics: Dict) -> Dict:
"""Run full anomaly detection pipeline"""
results = {
"timestamp": datetime.utcnow().isoformat(),
"statistical_anomalies": [],
"cost_analysis": {},
"ai_analysis": {},
"overall_status": "HEALTHY"
}
# Check latency anomalies
if "latency" in self.historical_data and current_metrics.get("latency", {}).get("p99_ms", 0) > 0:
self.add_observation("latency", current_metrics["latency"]["p99_ms"])
is_anomaly, details = self.detect_statistical_anomalies(
"latency",
current_metrics["latency"]["p99_ms"]
)
if is_anomaly:
results["statistical_anomalies"].append({
"metric": "latency_p99",
"severity": "HIGH",
**details
})
# Check error rate anomalies
if current_metrics.get("error_rate_percent", 0) > 0:
self.add_observation("error_rate", current_metrics["error_rate_percent"])
is_anomaly, details = self.detect_statistical_anomalies(
"error_rate",
current_metrics["error_rate_percent"]
)
if is_anomaly:
results["statistical_anomalies"].append({
"metric": "error_rate",
"severity": "CRITICAL",
**details
})
# Cost anomaly detection
if current_metrics.get("cost_rate_per_hour", 0) > 0:
is_anomaly, cost_details = self.detect_cost_anomaly(
current_metrics["cost_rate_per_hour"]
)
results["cost_analysis"] = cost_details
if is_anomaly:
results["statistical_anomalies"].append({
"metric": "cost_rate",
"severity": "WARNING",
**cost_details
})
# AI-powered deep analysis if anomalies detected
if results["statistical_anomalies"]:
results["ai_analysis"] = await self.ai_analyze_anomaly_pattern(
current_metrics,
results["statistical_anomalies"]
)
# Determine overall status
risk_level = results["ai_analysis"].get("risk_level", "MEDIUM")
results["overall_status"] = {
"LOW": "HEALTHY",
"MEDIUM": "WARNING",
"HIGH": "DEGRADED",
"CRITICAL": "CRISIS"
}.get(risk_level, "UNKNOWN")
return results
Example usage
async def demo():
engine = AnomalyDetectionEngine(api_key="YOUR_HOLYSHEEP_API_KEY")
# Simulate historical data (normal operation)
print("Building historical baseline...")
for i in range(100):
import random
engine.add_observation("latency", random.gauss(200, 50))
engine.add_observation("error_rate", random.gauss(1.5, 0.5))
engine.add_observation("cost_rate", random.gauss(15, 3))
# Simulate current metrics with anomaly
current_metrics = {
"total_requests": 5420,
"error_rate_percent": 12.5, # Anomaly: significantly higher
"cost_rate_per_hour": 45.0, # Anomaly: 3x baseline
"latency": {
"p50_ms": 180,
"p95_ms": 450,
"p99_ms": 1200 # Anomaly: 6x baseline
},
"tokens_per_minute": 85000
}
print("\nRunning comprehensive anomaly detection...")
results = await engine.run_comprehensive_analysis(current_metrics)
print(f"\n=== Analysis Results ===")
print(f"Overall Status: {results['overall_status']}")
print(f"Anomalies Detected: {len(results['statistical_anomalies'])}")
for anomaly in results['statistical_anomalies']:
print(f"\n [{anomaly['severity']}] {anomaly['metric']}")
print(f" Details: {json.dumps(anomaly, indent=4)}")
if results.get('ai_analysis'):
print(f"\n=== AI Analysis ===")
print(f"Risk Level: {results['ai_analysis'].get('risk_level')}")
print(f"Confidence: {results['ai_analysis'].get('confidence_score')}")
print(f"Primary Concerns: {results['ai_analysis'].get('primary_concerns')}")
print(f"Recommended Actions: {results['ai_analysis'].get('recommended_actions')}")
if __name__ == "__main__":
asyncio.run(demo())3. Automated Remediation and Scaling
#!/usr/bin/env python3
"""
AI Crisis Early Warning System - Auto-Remediation Engine
Automated responses to detected anomalies using HolySheep AI
"""
import asyncio
import aiohttp
import json
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Callable
from enum import Enum
from dataclasses import dataclass, asdict
class RemediationAction(Enum):
"""Available automated remediation actions"""
SCALE_UP = "scale_up"
SCALE_DOWN = "scale_down"
SWITCH_MODEL = "switch_model"
RETRY_BACKOFF = "retry_backoff"
CIRCUIT_BREAK = "circuit_break"
FALLBACK_ROUTING = "fallback_routing"
NOTIFY_TEAM = "notify_team"
THROTTLE_REQUESTS = "throttle_requests"
@dataclass
class RemediationResult:
action: RemediationAction
success: bool
details: Dict
execution_time_ms: float
class AutoRemediationEngine:
"""Automated crisis response system"""
HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1"
# Model hierarchy for failover (cost-efficient to premium)
MODEL_HIERARCHY = [
"deepseek-v3.2", # $0.42/MTok - cheapest
"gemini-2.5-flash", # $2.50/MTok - balanced
"claude-sonnet-4.5", # $15/MTok - premium
"gpt-4.1", # $8/MTok - alternative premium
]
def __init__(self, api_key: str):
self.api_key = api_key
self.circuit_breaker_state: Dict[str, bool] = {} # model -> is_open
self.circuit_breaker_counts: Dict[str, int] = {}
self.fallback_config: Dict[str, str] = {}
self.action_history: List[RemediationResult] = []
# Initialize circuit breakers for all models
for model in self.MODEL_HIERARCHY:
self.circuit_breaker_state[model] = False
self.circuit_breaker_counts[model] = 0
def _record_action(self, action: RemediationAction, success: bool, details: Dict, exec_time: float):
"""Record action for history tracking"""
self.action_history.append(RemediationResult(
action=action,
success=success,
details=details,
execution_time_ms=exec_time
))
# Keep last 100 actions
self.action_history = self.action_history[-100:]
async def execute_scale_up(self, current_rps: int, target_rps: int) -> RemediationResult:
"""Scale up API capacity"""
import time
start = time.perf_counter()
details = {
"current_rps": current_rps,
"target_rps": target_rps,
"scaling_factor": round(target_rps / current_rps, 2),
"estimated_cost_increase_percent": round((target_rps / current_rps - 1) * 100, 2)
}
# In production, this would trigger infrastructure scaling
# For HolySheep AI, scaling is automatic based on rate limits
await asyncio.sleep(0.1) # Simulate scaling action
self._record_action(
RemediationAction.SCALE_UP,
success=True,
details=details,
exec_time=(time.perf_counter() - start) * 1000
)
return RemediationResult(
action=RemediationAction.SCALE_UP,
success=True,
details=details,
execution_time_ms=(time.perf_counter() - start) * 1000
)
async def execute_model_switch(
self,
from_model: str,
to_model: str,
reason: str
) -> RemediationResult:
"""Switch to a more reliable or cost-effective model"""
import time
start = time.perf_counter()
# Check if circuit breaker is open for target model
if self.circuit_breaker_state.get(to_model, False):
return RemediationResult(
action=Rem