Build a complete self-managing intelligent platform that combines all previous agents (CI/CD, Security, Infrastructure) into a unified system that thinks, learns, and evolves autonomously.
- Multi-agent orchestration at scale
- Self-healing infrastructure patterns
- Predictive operations using AI
- Autonomous decision making
- Platform evolution and learning
- Cost optimization automation
- Full-stack observability
- Enterprise-grade resilience
- Completed Exercises 1 & 2
- Strong understanding of distributed systems
- Knowledge of cloud platforms (AWS/Azure/GCP)
- Experience with Kubernetes
- Understanding of microservices architecture
An Intelligent Platform represents the pinnacle of Agentic DevOps - a fully autonomous system that:
- Self-Manages: Makes decisions without human intervention
- Self-Heals: Fixes problems before they impact users
- Self-Optimizes: Continuously improves performance and cost
- Self-Secures: Adapts to threats in real-time
- Self-Evolves: Learns and improves from experience
graph TB
subgraph "Intelligent Platform Core"
BRAIN[Platform Brain - AI Orchestrator]
MEMORY[Platform Memory - Knowledge Base]
LEARNING[Learning Engine]
DECISION[Decision Engine]
end
subgraph "Agent Ecosystem"
subgraph "Operations Agents"
CI_AGENT[CI/CD Agent]
DEPLOY_AGENT[Deployment Agent]
RELEASE_AGENT[Release Manager Agent]
end
subgraph "Security Agents"
SEC_AGENT[Security Agent]
THREAT_AGENT[Threat Hunter]
COMPLIANCE_AGENT[Compliance Agent]
end
subgraph "Infrastructure Agents"
INFRA_AGENT[Infrastructure Agent]
SCALE_AGENT[Scaling Agent]
HEAL_AGENT[Self-Healing Agent]
COST_AGENT[Cost Optimizer]
end
subgraph "Observability Agents"
MON_AGENT[Monitoring Agent]
LOG_AGENT[Log Analysis Agent]
TRACE_AGENT[Trace Agent]
PREDICT_AGENT[Prediction Agent]
end
end
subgraph "Platform Services"
API[Platform API]
EVENT_BUS[Event Bus]
STATE_STORE[State Store]
METRICS[Metrics Engine]
end
subgraph "External Integrations"
CLOUD[Cloud Providers]
TOOLS[DevOps Tools]
MONITORING[Monitoring Systems]
SECURITY[Security Tools]
end
BRAIN --> DECISION
DECISION --> Agent Ecosystem
Agent Ecosystem --> EVENT_BUS
EVENT_BUS --> BRAIN
BRAIN --> MEMORY
MEMORY --> LEARNING
LEARNING --> BRAIN
Agent Ecosystem --> Platform Services
Platform Services --> External Integrations
style BRAIN fill:#10B981
style LEARNING fill:#3B82F6
style DECISION fill:#F59E0B
style SEC_AGENT fill:#EF4444
Copilot Prompt Suggestion:
# Create an intelligent platform orchestrator that:
# - Coordinates multiple specialized agents
# - Makes autonomous decisions based on platform state
# - Learns from operational patterns
# - Predicts and prevents issues
# - Optimizes for multiple objectives (performance, cost, security)
# - Implements self-evolution capabilities
# Use advanced AI techniques and distributed systems patternsCreate platform/intelligent_platform.py:
import asyncio
import json
from typing import Dict, List, Any, Optional, Set, Tuple
from dataclasses import dataclass, field
from datetime import datetime, timedelta
from enum import Enum
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import StandardScaler
import tensorflow as tf
import networkx as nx
import structlog
import aioredis
from prometheus_client import Counter, Histogram, Gauge, Summary
import ray
from ray import serve
import openai
import anthropic
from abc import ABC, abstractmethod
logger = structlog.get_logger()
class PlatformObjective(Enum):
PERFORMANCE = "performance"
COST = "cost"
SECURITY = "security"
RELIABILITY = "reliability"
COMPLIANCE = "compliance"
USER_EXPERIENCE = "user_experience"
@dataclass
class PlatformState:
"""Current state of the entire platform"""
timestamp: datetime
health_score: float
performance_metrics: Dict[str, float]
security_posture: Dict[str, Any]
cost_metrics: Dict[str, float]
compliance_status: Dict[str, Any]
active_workloads: List[Dict[str, Any]]
resource_utilization: Dict[str, float]
predictions: Dict[str, Any] = field(default_factory=dict)
anomalies: List[Dict[str, Any]] = field(default_factory=list)
@dataclass
class PlatformDecision:
"""Decision made by the platform"""
id: str
timestamp: datetime
decision_type: str
action: str
reasoning: str
confidence: float
expected_impact: Dict[str, float]
risk_assessment: Dict[str, Any]
rollback_plan: Optional[Dict[str, Any]] = None
@dataclass
class LearningOutcome:
"""Result of platform learning"""
lesson_id: str
timestamp: datetime
event_type: str
outcome: str
metrics_before: Dict[str, float]
metrics_after: Dict[str, float]
insights: List[str]
model_updates: Dict[str, Any]
class IntelligentPlatformBrain:
"""The brain of the intelligent platform - coordinates everything"""
def __init__(self, config: Dict[str, Any]):
self.config = config
self.agents: Dict[str, 'BaseAgent'] = {}
self.state = None
self.decision_history: List[PlatformDecision] = []
self.learning_history: List[LearningOutcome] = []
self.knowledge_graph = nx.DiGraph()
self.ml_models = {}
self.objectives = self._initialize_objectives()
# Metrics
self.decisions_made = Counter('platform_decisions_total')
self.decision_time = Histogram('platform_decision_duration_seconds')
self.platform_health = Gauge('platform_health_score')
self.learning_cycles = Counter('platform_learning_cycles_total')
# Initialize Ray for distributed computing
if not ray.is_initialized():
ray.init()
async def initialize(self):
"""Initialize the platform brain"""
logger.info("Initializing Intelligent Platform Brain")
# Initialize all agents
await self._initialize_agents()
# Load ML models
await self._load_ml_models()
# Build initial knowledge graph
await self._build_knowledge_graph()
# Start core processes
asyncio.create_task(self.decision_loop())
asyncio.create_task(self.learning_loop())
asyncio.create_task(self.evolution_loop())
asyncio.create_task(self.prediction_loop())
logger.info("Platform Brain initialized")
def _initialize_objectives(self) -> Dict[PlatformObjective, float]:
"""Initialize platform objectives with weights"""
return {
PlatformObjective.PERFORMANCE: 0.25,
PlatformObjective.COST: 0.20,
PlatformObjective.SECURITY: 0.25,
PlatformObjective.RELIABILITY: 0.20,
PlatformObjective.COMPLIANCE: 0.05,
PlatformObjective.USER_EXPERIENCE: 0.05
}
async def _initialize_agents(self):
"""Initialize all platform agents"""
# Import agent classes
from agents.cicd_agent import CICDAgent
from agents.security_agent import SecurityIntelligenceEngine
from agents.infra_agent import InfrastructureAgent
from agents.monitoring_agent import MonitoringAgent
from agents.cost_agent import CostOptimizationAgent
from agents.healing_agent import SelfHealingAgent
# Initialize agents
self.agents = {
'cicd': CICDAgent(self.config),
'security': SecurityIntelligenceEngine(self.config),
'infrastructure': InfrastructureAgent(self.config),
'monitoring': MonitoringAgent(self.config),
'cost_optimization': CostOptimizationAgent(self.config),
'self_healing': SelfHealingAgent(self.config)
}
# Initialize each agent
for name, agent in self.agents.items():
await agent.initialize()
logger.info(f"Initialized agent: {name}")
async def _load_ml_models(self):
"""Load machine learning models"""
# Decision model - predicts outcome of decisions
self.ml_models['decision_predictor'] = await self._load_decision_model()
# Anomaly detection model
self.ml_models['anomaly_detector'] = await self._load_anomaly_model()
# Cost prediction model
self.ml_models['cost_predictor'] = await self._load_cost_model()
# Performance prediction model
self.ml_models['performance_predictor'] = await self._load_performance_model()
# Failure prediction model
self.ml_models['failure_predictor'] = await self._load_failure_model()
async def decision_loop(self):
"""Main decision-making loop"""
while True:
try:
# Collect current state
self.state = await self.collect_platform_state()
# Analyze state
analysis = await self.analyze_state(self.state)
# Make decisions
decisions = await self.make_decisions(analysis)
# Execute decisions
for decision in decisions:
await self.execute_decision(decision)
# Update metrics
self.platform_health.set(self.state.health_score)
await asyncio.sleep(30) # Decision cycle every 30 seconds
except Exception as e:
logger.error(f"Decision loop error: {e}")
async def collect_platform_state(self) -> PlatformState:
"""Collect comprehensive platform state"""
# Collect from all agents
states = await asyncio.gather(*[
agent.get_state() for agent in self.agents.values()
])
# Aggregate metrics
performance_metrics = await self._aggregate_performance_metrics(states)
security_posture = await self._aggregate_security_posture(states)
cost_metrics = await self._aggregate_cost_metrics(states)
compliance_status = await self._aggregate_compliance_status(states)
# Calculate health score
health_score = await self.calculate_health_score(
performance_metrics,
security_posture,
cost_metrics,
compliance_status
)
# Get active workloads
active_workloads = await self.agents['infrastructure'].get_active_workloads()
# Get resource utilization
resource_utilization = await self.agents['monitoring'].get_resource_utilization()
# Generate predictions
predictions = await self.generate_predictions(
performance_metrics,
resource_utilization,
active_workloads
)
# Detect anomalies
anomalies = await self.detect_anomalies(states)
return PlatformState(
timestamp=datetime.now(),
health_score=health_score,
performance_metrics=performance_metrics,
security_posture=security_posture,
cost_metrics=cost_metrics,
compliance_status=compliance_status,
active_workloads=active_workloads,
resource_utilization=resource_utilization,
predictions=predictions,
anomalies=anomalies
)
async def analyze_state(self, state: PlatformState) -> Dict[str, Any]:
"""Analyze platform state using AI"""
# Prepare context for AI analysis
context = {
'health_score': state.health_score,
'performance': state.performance_metrics,
'security': state.security_posture,
'cost': state.cost_metrics,
'compliance': state.compliance_status,
'predictions': state.predictions,
'anomalies': state.anomalies,
'objectives': {k.value: v for k, v in self.objectives.items()}
}
# AI-powered analysis
prompt = f"""
Analyze this platform state and provide insights:
{json.dumps(context, indent=2)}
Provide:
1. Key issues requiring immediate attention
2. Optimization opportunities
3. Risk assessment
4. Recommended actions with priority
5. Trade-off analysis between objectives
Format as JSON with: issues, opportunities, risks, recommendations, trade_offs
"""
ai_analysis = await self.call_ai_model(prompt)
# Combine with rule-based analysis
rule_analysis = await self.rule_based_analysis(state)
# Merge analyses
return {
'ai_insights': ai_analysis,
'rule_insights': rule_analysis,
'combined_priority': await self.prioritize_insights(ai_analysis, rule_analysis)
}
async def make_decisions(self, analysis: Dict[str, Any]) -> List[PlatformDecision]:
"""Make autonomous decisions based on analysis"""
decisions = []
# Get prioritized actions
priority_actions = analysis['combined_priority']
for action in priority_actions:
# Evaluate decision
decision_eval = await self.evaluate_decision(action)
if decision_eval['should_proceed']:
decision = PlatformDecision(
id=f"DEC-{datetime.now().timestamp()}",
timestamp=datetime.now(),
decision_type=action['type'],
action=action['action'],
reasoning=decision_eval['reasoning'],
confidence=decision_eval['confidence'],
expected_impact=decision_eval['impact'],
risk_assessment=decision_eval['risks'],
rollback_plan=await self.create_rollback_plan(action)
)
decisions.append(decision)
self.decisions_made.inc()
return decisions
async def evaluate_decision(self, action: Dict[str, Any]) -> Dict[str, Any]:
"""Evaluate whether to proceed with a decision"""
# Predict outcome using ML
predicted_outcome = await self.predict_decision_outcome(action)
# Calculate impact on objectives
objective_impact = await self.calculate_objective_impact(action, predicted_outcome)
# Risk assessment
risks = await self.assess_risks(action)
# Decision criteria
proceed = (
predicted_outcome['success_probability'] > 0.7 and
objective_impact['net_positive'] and
risks['acceptable']
)
return {
'should_proceed': proceed,
'reasoning': self.generate_reasoning(action, predicted_outcome, objective_impact, risks),
'confidence': predicted_outcome['confidence'],
'impact': objective_impact,
'risks': risks
}
async def execute_decision(self, decision: PlatformDecision):
"""Execute a platform decision"""
logger.info(f"Executing decision: {decision.action}")
start_time = datetime.now()
try:
# Route to appropriate agent
if decision.decision_type == 'scaling':
result = await self.agents['infrastructure'].execute_scaling(decision)
elif decision.decision_type == 'deployment':
result = await self.agents['cicd'].execute_deployment(decision)
elif decision.decision_type == 'security_response':
result = await self.agents['security'].execute_response(decision)
elif decision.decision_type == 'cost_optimization':
result = await self.agents['cost_optimization'].execute_optimization(decision)
elif decision.decision_type == 'healing':
result = await self.agents['self_healing'].execute_healing(decision)
else:
result = await self.execute_generic_decision(decision)
# Record outcome
await self.record_decision_outcome(decision, result)
except Exception as e:
logger.error(f"Decision execution failed: {e}")
# Execute rollback if available
if decision.rollback_plan:
await self.execute_rollback(decision)
# Record failure
await self.record_decision_failure(decision, str(e))
finally:
duration = (datetime.now() - start_time).total_seconds()
self.decision_time.observe(duration)
async def learning_loop(self):
"""Continuous learning loop"""
while True:
try:
# Analyze recent decisions
recent_decisions = self.decision_history[-100:]
if recent_decisions:
# Extract patterns
patterns = await self.extract_decision_patterns(recent_decisions)
# Learn from outcomes
learnings = await self.learn_from_outcomes(recent_decisions)
# Update models
await self.update_ml_models(learnings)
# Update knowledge graph
await self.update_knowledge_graph(patterns, learnings)
# Generate insights
insights = await self.generate_learning_insights(patterns, learnings)
# Record learning cycle
self.learning_cycles.inc()
# Store learnings
for insight in insights:
self.learning_history.append(LearningOutcome(
lesson_id=f"LEARN-{datetime.now().timestamp()}",
timestamp=datetime.now(),
event_type='decision_analysis',
outcome='learned',
metrics_before={},
metrics_after={},
insights=[insight],
model_updates={}
))
await asyncio.sleep(3600) # Learn every hour
except Exception as e:
logger.error(f"Learning loop error: {e}")
async def evolution_loop(self):
"""Platform evolution loop - self-improvement"""
while True:
try:
# Analyze platform performance over time
performance_trend = await self.analyze_performance_trend()
# Identify evolution opportunities
evolution_opps = await self.identify_evolution_opportunities(performance_trend)
for opportunity in evolution_opps:
# Evaluate evolution
if await self.should_evolve(opportunity):
# Implement evolution
await self.implement_evolution(opportunity)
# Test evolution
test_result = await self.test_evolution(opportunity)
if test_result['successful']:
# Commit evolution
await self.commit_evolution(opportunity)
logger.info(f"Platform evolved: {opportunity['description']}")
else:
# Rollback evolution
await self.rollback_evolution(opportunity)
await asyncio.sleep(86400) # Evolve daily
except Exception as e:
logger.error(f"Evolution loop error: {e}")
async def prediction_loop(self):
"""Predictive analytics loop"""
while True:
try:
# Collect time series data
time_series_data = await self.collect_time_series_data()
# Generate predictions
predictions = {
'performance': await self.predict_performance(time_series_data),
'failures': await self.predict_failures(time_series_data),
'cost': await self.predict_costs(time_series_data),
'security_threats': await self.predict_threats(time_series_data),
'capacity_needs': await self.predict_capacity(time_series_data)
}
# Take preventive actions
for prediction_type, prediction in predictions.items():
if prediction['confidence'] > 0.8:
await self.take_preventive_action(prediction_type, prediction)
await asyncio.sleep(300) # Predict every 5 minutes
except Exception as e:
logger.error(f"Prediction loop error: {e}")
async def calculate_health_score(self,
performance: Dict[str, float],
security: Dict[str, Any],
cost: Dict[str, float],
compliance: Dict[str, Any]) -> float:
"""Calculate overall platform health score"""
# Weighted scoring
scores = {
'performance': self._score_performance(performance),
'security': self._score_security(security),
'cost': self._score_cost(cost),
'compliance': self._score_compliance(compliance)
}
# Apply objective weights
weighted_score = sum(
scores[obj.value.lower()] * weight
for obj, weight in self.objectives.items()
if obj.value.lower() in scores
)
return min(max(weighted_score, 0), 100)
def _score_performance(self, metrics: Dict[str, float]) -> float:
"""Score performance metrics"""
# Example scoring logic
latency_score = 100 - min(metrics.get('avg_latency_ms', 0) / 10, 100)
throughput_score = min(metrics.get('throughput_rps', 0) / 1000 * 100, 100)
error_score = 100 - min(metrics.get('error_rate', 0) * 1000, 100)
return (latency_score + throughput_score + error_score) / 3
def _score_security(self, posture: Dict[str, Any]) -> float:
"""Score security posture"""
vulnerability_score = 100 - min(posture.get('critical_vulns', 0) * 20, 100)
compliance_score = posture.get('compliance_percentage', 0)
incident_score = 100 - min(posture.get('incidents_24h', 0) * 10, 100)
return (vulnerability_score + compliance_score + incident_score) / 3
def _score_cost(self, metrics: Dict[str, float]) -> float:
"""Score cost efficiency"""
budget_variance = metrics.get('budget_variance_percent', 0)
optimization_score = metrics.get('optimization_score', 0)
variance_score = 100 - abs(budget_variance)
return (variance_score + optimization_score) / 2
def _score_compliance(self, status: Dict[str, Any]) -> float:
"""Score compliance status"""
return status.get('overall_compliance_score', 100)
async def predict_decision_outcome(self, action: Dict[str, Any]) -> Dict[str, Any]:
"""Predict outcome of a decision using ML"""
if 'decision_predictor' in self.ml_models:
# Prepare features
features = await self._extract_decision_features(action)
# Predict
prediction = self.ml_models['decision_predictor'].predict(features)
return {
'success_probability': float(prediction[0]),
'expected_duration': float(prediction[1]),
'confidence': float(prediction[2]),
'predicted_impact': {
'performance': float(prediction[3]),
'cost': float(prediction[4]),
'security': float(prediction[5])
}
}
# Fallback to heuristic
return {
'success_probability': 0.8,
'expected_duration': 300,
'confidence': 0.6,
'predicted_impact': {
'performance': 0,
'cost': 0,
'security': 0
}
}
async def create_rollback_plan(self, action: Dict[str, Any]) -> Dict[str, Any]:
"""Create rollback plan for an action"""
rollback_type = action.get('type')
if rollback_type == 'deployment':
return {
'type': 'rollback_deployment',
'steps': [
'Switch traffic to previous version',
'Scale down new version',
'Restore configuration',
'Verify rollback success'
],
'estimated_time': 300,
'automation_level': 'full'
}
elif rollback_type == 'scaling':
return {
'type': 'rollback_scaling',
'steps': [
'Restore previous replica count',
'Adjust resource limits',
'Update load balancer',
'Monitor stability'
],
'estimated_time': 180,
'automation_level': 'full'
}
elif rollback_type == 'configuration':
return {
'type': 'rollback_configuration',
'steps': [
'Restore previous configuration',
'Restart affected services',
'Verify configuration applied',
'Test functionality'
],
'estimated_time': 120,
'automation_level': 'full'
}
# Generic rollback
return {
'type': 'generic_rollback',
'steps': ['Undo changes', 'Verify state', 'Monitor'],
'estimated_time': 600,
'automation_level': 'partial'
}
async def identify_evolution_opportunities(self,
performance_trend: Dict[str, Any]) -> List[Dict[str, Any]]:
"""Identify opportunities for platform evolution"""
opportunities = []
# Analyze inefficiencies
inefficiencies = await self.analyze_inefficiencies(performance_trend)
for inefficiency in inefficiencies:
# Generate evolution proposal
proposal = await self.generate_evolution_proposal(inefficiency)
if proposal:
opportunities.append({
'type': 'efficiency_improvement',
'description': proposal['description'],
'expected_benefit': proposal['benefit'],
'implementation': proposal['implementation'],
'risk_level': proposal['risk_level']
})
# Analyze new patterns
new_patterns = await self.detect_new_patterns(performance_trend)
for pattern in new_patterns:
# Check if we can adapt
if adaptation := await self.can_adapt_to_pattern(pattern):
opportunities.append({
'type': 'pattern_adaptation',
'description': f"Adapt to {pattern['name']}",
'expected_benefit': adaptation['benefit'],
'implementation': adaptation['steps'],
'risk_level': 'medium'
})
# AI-suggested improvements
ai_suggestions = await self.get_ai_evolution_suggestions()
opportunities.extend(ai_suggestions)
return opportunities
async def implement_evolution(self, opportunity: Dict[str, Any]):
"""Implement a platform evolution"""
logger.info(f"Implementing evolution: {opportunity['description']}")
# Create evolution branch
evolution_id = f"EVO-{datetime.now().timestamp()}"
# Implement changes based on type
if opportunity['type'] == 'efficiency_improvement':
await self.implement_efficiency_improvement(opportunity, evolution_id)
elif opportunity['type'] == 'pattern_adaptation':
await self.implement_pattern_adaptation(opportunity, evolution_id)
elif opportunity['type'] == 'ai_suggested':
await self.implement_ai_suggestion(opportunity, evolution_id)
# Record evolution
await self.record_evolution(evolution_id, opportunity)
async def generate_platform_insights(self) -> Dict[str, Any]:
"""Generate comprehensive platform insights"""
# Collect data from all sources
decision_insights = await self.analyze_decision_effectiveness()
performance_insights = await self.analyze_performance_patterns()
cost_insights = await self.analyze_cost_trends()
security_insights = await self.analyze_security_posture()
# Generate predictive insights
predictions = {
'next_24h': await self.predict_next_24_hours(),
'next_week': await self.predict_next_week(),
'capacity_needs': await self.predict_capacity_requirements(),
'cost_forecast': await self.predict_cost_trajectory()
}
# Generate recommendations
recommendations = await self.generate_ai_recommendations()
return {
'timestamp': datetime.now().isoformat(),
'platform_health': self.state.health_score if self.state else 0,
'decision_effectiveness': decision_insights,
'performance_analysis': performance_insights,
'cost_analysis': cost_insights,
'security_analysis': security_insights,
'predictions': predictions,
'recommendations': recommendations,
'evolution_status': await self.get_evolution_status(),
'learning_summary': await self.get_learning_summary()
}
async def handle_crisis(self, crisis_type: str, crisis_data: Dict[str, Any]):
"""Handle platform crisis situations"""
logger.critical(f"CRISIS DETECTED: {crisis_type}")
# Immediate stabilization
await self.stabilize_platform(crisis_type)
# Activate crisis mode
await self.activate_crisis_mode()
# Coordinate response
response_plan = await self.create_crisis_response_plan(crisis_type, crisis_data)
# Execute response
for action in response_plan['immediate_actions']:
await self.execute_crisis_action(action)
# Monitor stabilization
await self.monitor_crisis_recovery()
# Learn from crisis
await self.analyze_crisis_aftermath(crisis_type, crisis_data)
async def generate_executive_dashboard(self) -> Dict[str, Any]:
"""Generate executive-level platform dashboard"""
return {
'platform_status': {
'health': self.state.health_score if self.state else 0,
'status': self.get_platform_status(),
'uptime': await self.calculate_uptime(),
'sla_compliance': await self.calculate_sla_compliance()
},
'key_metrics': {
'performance': {
'latency_p99': self.state.performance_metrics.get('p99_latency', 0),
'throughput': self.state.performance_metrics.get('throughput_rps', 0),
'error_rate': self.state.performance_metrics.get('error_rate', 0)
},
'cost': {
'current_spend': self.state.cost_metrics.get('current_spend', 0),
'projected_monthly': self.state.cost_metrics.get('projected_monthly', 0),
'savings_achieved': self.state.cost_metrics.get('savings_ytd', 0)
},
'security': {
'threat_level': self.state.security_posture.get('threat_level', 'normal'),
'vulnerabilities': self.state.security_posture.get('open_vulnerabilities', 0),
'compliance_score': self.state.compliance_status.get('overall_score', 100)
}
},
'ai_insights': await self.generate_executive_insights(),
'predictions': {
'performance_trend': self.state.predictions.get('performance_24h', {}),
'cost_projection': self.state.predictions.get('cost_projection', {}),
'capacity_forecast': self.state.predictions.get('capacity_needs', {})
},
'recent_decisions': [
{
'timestamp': d.timestamp.isoformat(),
'action': d.action,
'impact': d.expected_impact,
'status': 'executed'
}
for d in self.decision_history[-5:]
],
'recommendations': await self.generate_executive_recommendations()
}Create agents/infra_agent.py:
import asyncio
from typing import Dict, List, Any, Optional
from datetime import datetime, timedelta
import kubernetes
from kubernetes import client, config, watch
import boto3
import azure.mgmt.compute
from google.cloud import compute_v1
import structlog
from prometheus_client import Counter, Gauge, Histogram
import numpy as np
from dataclasses import dataclass
logger = structlog.get_logger()
@dataclass
class ResourceRequirement:
"""Resource requirements for workload"""
cpu: float # in cores
memory: float # in GB
storage: float # in GB
gpu: Optional[int] = None
network_bandwidth: Optional[float] = None # in Gbps
@dataclass
class ScalingDecision:
"""Scaling decision details"""
resource_type: str
action: str # scale_up, scale_down, migrate
current_value: int
target_value: int
reason: str
estimated_cost_impact: float
estimated_performance_impact: float
class InfrastructureAgent:
"""Autonomous infrastructure management agent"""
def __init__(self, config: Dict[str, Any]):
self.config = config
self.k8s_client = None
self.cloud_clients = {}
self.resource_inventory = {}
self.scaling_history = []
# Metrics
self.scaling_operations = Counter('infra_scaling_operations_total')
self.resource_utilization = Gauge('infra_resource_utilization_percent')
self.cost_saved = Counter('infra_cost_saved_dollars_total')
self.healing_actions = Counter('infra_healing_actions_total')
async def initialize(self):
"""Initialize infrastructure agent"""
logger.info("Initializing Infrastructure Agent")
# Initialize Kubernetes client
try:
config.load_incluster_config()
except:
config.load_kube_config()
self.k8s_client = client.ApiClient()
# Initialize cloud clients
await self._initialize_cloud_clients()
# Start monitoring loops
asyncio.create_task(self.resource_monitoring_loop())
asyncio.create_task(self.auto_scaling_loop())
asyncio.create_task(self.cost_optimization_loop())
async def _initialize_cloud_clients(self):
"""Initialize cloud provider clients"""
# AWS
if self.config.get('aws_enabled'):
self.cloud_clients['aws'] = {
'ec2': boto3.client('ec2'),
'ecs': boto3.client('ecs'),
'cloudwatch': boto3.client('cloudwatch')
}
# Azure
if self.config.get('azure_enabled'):
from azure.identity import DefaultAzureCredential
credential = DefaultAzureCredential()
self.cloud_clients['azure'] = {
'compute': azure.mgmt.compute.ComputeManagementClient(
credential,
self.config['azure_subscription_id']
)
}
# GCP
if self.config.get('gcp_enabled'):
self.cloud_clients['gcp'] = {
'compute': compute_v1.InstancesClient()
}
async def resource_monitoring_loop(self):
"""Monitor resource utilization continuously"""
while True:
try:
# Monitor Kubernetes resources
k8s_metrics = await self.collect_k8s_metrics()
# Monitor cloud resources
cloud_metrics = await self.collect_cloud_metrics()
# Update resource inventory
self.resource_inventory = {
'kubernetes': k8s_metrics,
'cloud': cloud_metrics,
'timestamp': datetime.now()
}
# Update utilization metrics
overall_cpu = k8s_metrics.get('cpu_utilization', 0)
overall_memory = k8s_metrics.get('memory_utilization', 0)
self.resource_utilization.labels(resource='cpu').set(overall_cpu)
self.resource_utilization.labels(resource='memory').set(overall_memory)
await asyncio.sleep(60) # Monitor every minute
except Exception as e:
logger.error(f"Resource monitoring error: {e}")
async def collect_k8s_metrics(self) -> Dict[str, Any]:
"""Collect Kubernetes cluster metrics"""
v1 = client.CoreV1Api(self.k8s_client)
metrics_api = client.CustomObjectsApi(self.k8s_client)
# Get nodes
nodes = v1.list_node()
total_cpu = 0
total_memory = 0
used_cpu = 0
used_memory = 0
for node in nodes.items:
# Get node capacity
total_cpu += self._parse_cpu(node.status.capacity['cpu'])
total_memory += self._parse_memory(node.status.capacity['memory'])
# Get node metrics
try:
metrics = metrics_api.get_cluster_custom_object(
group="metrics.k8s.io",
version="v1beta1",
plural="nodes",
name=node.metadata.name
)
used_cpu += self._parse_cpu(metrics['usage']['cpu'])
used_memory += self._parse_memory(metrics['usage']['memory'])
except Exception as e:
logger.warning(f"Failed to get metrics for node {node.metadata.name}: {e}")
# Get pod metrics
pods = v1.list_pod_for_all_namespaces()
pod_count = len([p for p in pods.items if p.status.phase == 'Running'])
return {
'nodes': len(nodes.items),
'pods': pod_count,
'total_cpu': total_cpu,
'total_memory': total_memory,
'used_cpu': used_cpu,
'used_memory': used_memory,
'cpu_utilization': (used_cpu / total_cpu * 100) if total_cpu > 0 else 0,
'memory_utilization': (used_memory / total_memory * 100) if total_memory > 0 else 0
}
async def auto_scaling_loop(self):
"""Automatic scaling based on metrics"""
while True:
try:
# Analyze current state
analysis = await self.analyze_scaling_needs()
# Make scaling decisions
decisions = await self.make_scaling_decisions(analysis)
# Execute scaling actions
for decision in decisions:
await self.execute_scaling_decision(decision)
await asyncio.sleep(300) # Check every 5 minutes
except Exception as e:
logger.error(f"Auto-scaling error: {e}")
async def analyze_scaling_needs(self) -> Dict[str, Any]:
"""Analyze infrastructure scaling needs"""
analysis = {
'timestamp': datetime.now(),
'recommendations': []
}
# Check Kubernetes workloads
v1 = client.AppsV1Api(self.k8s_client)
deployments = v1.list_deployment_for_all_namespaces()
for deployment in deployments.items:
# Get deployment metrics
metrics = await self.get_deployment_metrics(deployment)
if metrics:
# Check if scaling needed
if metrics['cpu_utilization'] > 80:
analysis['recommendations'].append({
'type': 'scale_up',
'resource': f"deployment/{deployment.metadata.name}",
'namespace': deployment.metadata.namespace,
'current_replicas': deployment.spec.replicas,
'recommended_replicas': deployment.spec.replicas + 1,
'reason': f"High CPU utilization: {metrics['cpu_utilization']}%"
})
elif metrics['cpu_utilization'] < 20 and deployment.spec.replicas > 1:
analysis['recommendations'].append({
'type': 'scale_down',
'resource': f"deployment/{deployment.metadata.name}",
'namespace': deployment.metadata.namespace,
'current_replicas': deployment.spec.replicas,
'recommended_replicas': max(1, deployment.spec.replicas - 1),
'reason': f"Low CPU utilization: {metrics['cpu_utilization']}%"
})
# Check cloud resources
cloud_recommendations = await self.analyze_cloud_scaling_needs()
analysis['recommendations'].extend(cloud_recommendations)
return analysis
async def make_scaling_decisions(self, analysis: Dict[str, Any]) -> List[ScalingDecision]:
"""Make intelligent scaling decisions"""
decisions = []
for recommendation in analysis['recommendations']:
# Evaluate recommendation
evaluation = await self.evaluate_scaling_recommendation(recommendation)
if evaluation['should_scale']:
decision = ScalingDecision(
resource_type=recommendation['type'],
action=recommendation['type'],
current_value=recommendation['current_replicas'],
target_value=recommendation['recommended_replicas'],
reason=recommendation['reason'],
estimated_cost_impact=evaluation['cost_impact'],
estimated_performance_impact=evaluation['performance_impact']
)
decisions.append(decision)
# Prioritize decisions
decisions.sort(key=lambda d: d.estimated_performance_impact, reverse=True)
return decisions
async def execute_scaling_decision(self, decision: ScalingDecision):
"""Execute a scaling decision"""
logger.info(f"Executing scaling decision: {decision.action} for {decision.resource_type}")
try:
if decision.resource_type.startswith('deployment/'):
# Scale Kubernetes deployment
await self.scale_k8s_deployment(decision)
elif decision.resource_type.startswith('vm/'):
# Scale cloud VM
await self.scale_cloud_vm(decision)
elif decision.resource_type.startswith('container/'):
# Scale container instance
await self.scale_container_instance(decision)
# Record scaling action
self.scaling_operations.inc()
self.scaling_history.append({
'timestamp': datetime.now(),
'decision': decision,
'status': 'executed'
})
# Calculate cost savings
if decision.action == 'scale_down':
self.cost_saved.inc(abs(decision.estimated_cost_impact))
except Exception as e:
logger.error(f"Failed to execute scaling decision: {e}")
self.scaling_history.append({
'timestamp': datetime.now(),
'decision': decision,
'status': 'failed',
'error': str(e)
})
async def scale_k8s_deployment(self, decision: ScalingDecision):
"""Scale Kubernetes deployment"""
v1 = client.AppsV1Api(self.k8s_client)
# Parse resource name
parts = decision.resource_type.split('/')
name = parts[1]
namespace = 'default' # Should be passed in decision
# Update replica count
body = {'spec': {'replicas': decision.target_value}}
v1.patch_namespaced_deployment_scale(
name=name,
namespace=namespace,
body=body
)
logger.info(f"Scaled {name} from {decision.current_value} to {decision.target_value} replicas")
async def predict_resource_needs(self,
workload: Dict[str, Any],
timeframe: int = 3600) -> ResourceRequirement:
"""Predict resource needs for a workload"""
# Get historical data
history = await self.get_workload_history(workload['name'])
if len(history) < 10:
# Not enough data, use current + buffer
return ResourceRequirement(
cpu=workload.get('current_cpu', 1) * 1.5,
memory=workload.get('current_memory', 2) * 1.5,
storage=workload.get('current_storage', 10)
)
# Analyze patterns
cpu_trend = self._analyze_trend([h['cpu'] for h in history])
memory_trend = self._analyze_trend([h['memory'] for h in history])
# Predict future needs
predicted_cpu = cpu_trend['current'] + (cpu_trend['slope'] * timeframe / 3600)
predicted_memory = memory_trend['current'] + (memory_trend['slope'] * timeframe / 3600)
# Add safety margin
return ResourceRequirement(
cpu=predicted_cpu * 1.2,
memory=predicted_memory * 1.2,
storage=workload.get('current_storage', 10),
gpu=workload.get('gpu_required'),
network_bandwidth=workload.get('network_bandwidth')
)
def _analyze_trend(self, values: List[float]) -> Dict[str, float]:
"""Analyze trend in values"""
if not values:
return {'current': 0, 'slope': 0}
x = np.arange(len(values))
y = np.array(values)
# Simple linear regression
slope = np.polyfit(x, y, 1)[0]
return {
'current': values[-1],
'slope': slope,
'mean': np.mean(values),
'std': np.std(values)
}
async def optimize_placement(self, workload: Dict[str, Any]) -> Dict[str, Any]:
"""Optimize workload placement"""
# Get available nodes
nodes = await self.get_available_nodes()
# Score each node
scores = []
for node in nodes:
score = await self.score_node_for_workload(node, workload)
scores.append((node, score))
# Sort by score
scores.sort(key=lambda x: x[1], reverse=True)
if scores:
best_node = scores[0][0]
return {
'node': best_node['name'],
'score': scores[0][1],
'reason': self.explain_placement_decision(best_node, workload)
}
return {'node': None, 'score': 0, 'reason': 'No suitable nodes available'}
async def implement_self_healing(self, issue: Dict[str, Any]) -> Dict[str, Any]:
"""Implement self-healing for infrastructure issues"""
logger.info(f"Implementing self-healing for: {issue['type']}")
healing_result = {
'issue': issue,
'actions_taken': [],
'status': 'in_progress',
'timestamp': datetime.now()
}
if issue['type'] == 'node_not_ready':
# Attempt to heal node
actions = await self.heal_node(issue['node'])
healing_result['actions_taken'].extend(actions)
elif issue['type'] == 'pod_crash_loop':
# Attempt to fix pod
actions = await self.heal_pod(issue['pod'], issue['namespace'])
healing_result['actions_taken'].extend(actions)
elif issue['type'] == 'disk_pressure':
# Clear disk space
actions = await self.clear_disk_space(issue['node'])
healing_result['actions_taken'].extend(actions)
elif issue['type'] == 'memory_pressure':
# Optimize memory usage
actions = await self.optimize_memory(issue['node'])
healing_result['actions_taken'].extend(actions)
# Update metrics
self.healing_actions.inc()
healing_result['status'] = 'completed'
return healing_result
async def get_state(self) -> Dict[str, Any]:
"""Get current infrastructure state"""
return {
'resource_inventory': self.resource_inventory,
'scaling_history': self.scaling_history[-10:], # Last 10 scaling actions
'health': await self.assess_infrastructure_health(),
'capacity': await self.assess_capacity(),
'costs': await self.calculate_current_costs()
}
async def get_active_workloads(self) -> List[Dict[str, Any]]:
"""Get list of active workloads"""
workloads = []
# Kubernetes workloads
v1 = client.AppsV1Api(self.k8s_client)
deployments = v1.list_deployment_for_all_namespaces()
for deployment in deployments.items:
workloads.append({
'name': deployment.metadata.name,
'type': 'kubernetes_deployment',
'namespace': deployment.metadata.namespace,
'replicas': deployment.spec.replicas,
'available_replicas': deployment.status.available_replicas or 0,
'cpu_request': self._get_resource_request(deployment, 'cpu'),
'memory_request': self._get_resource_request(deployment, 'memory')
})
# Add cloud workloads
cloud_workloads = await self.get_cloud_workloads()
workloads.extend(cloud_workloads)
return workloads
def _parse_cpu(self, cpu_str: str) -> float:
"""Parse CPU string to cores"""
if cpu_str.endswith('m'):
return float(cpu_str[:-1]) / 1000
return float(cpu_str)
def _parse_memory(self, memory_str: str) -> float:
"""Parse memory string to bytes"""
units = {'Ki': 1024, 'Mi': 1024**2, 'Gi': 1024**3}
for unit, multiplier in units.items():
if memory_str.endswith(unit):
return float(memory_str[:-len(unit)]) * multiplier
return float(memory_str)Create agents/healing_agent.py:
import asyncio
from typing import Dict, List, Any, Optional
from datetime import datetime, timedelta
import structlog
from dataclasses import dataclass
from enum import Enum
import numpy as np
logger = structlog.get_logger()
class HealingAction(Enum):
RESTART = "restart"
SCALE = "scale"
MIGRATE = "migrate"
RECONFIGURE = "reconfigure"
REPLACE = "replace"
ROLLBACK = "rollback"
PATCH = "patch"
@dataclass
class HealingPlan:
"""Plan for healing an issue"""
issue_id: str
issue_type: str
affected_component: str
actions: List[HealingAction]
priority: int
estimated_time: int # seconds
risk_level: str
automated: bool
verification_steps: List[str]
class SelfHealingAgent:
"""Autonomous self-healing agent"""
def __init__(self, config: Dict[str, Any]):
self.config = config
self.healing_patterns = {}
self.healing_history = []
self.active_healings = {}
async def initialize(self):
"""Initialize self-healing agent"""
logger.info("Initializing Self-Healing Agent")
# Load healing patterns
await self.load_healing_patterns()
# Start healing loops
asyncio.create_task(self.detection_loop())
asyncio.create_task(self.healing_loop())
asyncio.create_task(self.learning_loop())
async def load_healing_patterns(self):
"""Load known healing patterns"""
self.healing_patterns = {
'pod_crash_loop': {
'detection': self.detect_pod_crash_loop,
'healing': self.heal_pod_crash_loop,
'verification': self.verify_pod_health
},
'memory_leak': {
'detection': self.detect_memory_leak,
'healing': self.heal_memory_leak,
'verification': self.verify_memory_stability
},
'disk_full': {
'detection': self.detect_disk_full,
'healing': self.heal_disk_full,
'verification': self.verify_disk_space
},
'connection_pool_exhausted': {
'detection': self.detect_connection_pool_exhaustion,
'healing': self.heal_connection_pool,
'verification': self.verify_connection_pool
},
'service_degradation': {
'detection': self.detect_service_degradation,
'healing': self.heal_service_degradation,
'verification': self.verify_service_performance
},
'certificate_expiry': {
'detection': self.detect_certificate_expiry,
'healing': self.heal_certificate_expiry,
'verification': self.verify_certificate_validity
}
}
async def detection_loop(self):
"""Continuous detection of issues"""
while True:
try:
# Run all detection patterns
detected_issues = []
for pattern_name, pattern in self.healing_patterns.items():
issues = await pattern['detection']()
for issue in issues:
issue['pattern'] = pattern_name
detected_issues.append(issue)
# Process detected issues
for issue in detected_issues:
await self.process_issue(issue)
await asyncio.sleep(60) # Check every minute
except Exception as e:
logger.error(f"Detection loop error: {e}")
async def healing_loop(self):
"""Execute healing plans"""
while True:
try:
# Get pending healing plans
pending_plans = [p for p in self.active_healings.values()
if p['status'] == 'pending']
# Sort by priority
pending_plans.sort(key=lambda p: p['plan'].priority, reverse=True)
# Execute healings
for healing in pending_plans:
await self.execute_healing(healing)
await asyncio.sleep(30)
except Exception as e:
logger.error(f"Healing loop error: {e}")
async def process_issue(self, issue: Dict[str, Any]):
"""Process detected issue"""
issue_id = f"ISSUE-{datetime.now().timestamp()}"
logger.info(f"Processing issue: {issue_id} - {issue['pattern']}")
# Check if already being handled
if self.is_issue_being_handled(issue):
return
# Create healing plan
plan = await self.create_healing_plan(issue)
# Validate plan
if await self.validate_healing_plan(plan):
# Add to active healings
self.active_healings[issue_id] = {
'issue': issue,
'plan': plan,
'status': 'pending',
'created_at': datetime.now()
}
else:
logger.warning(f"Invalid healing plan for issue: {issue_id}")
async def create_healing_plan(self, issue: Dict[str, Any]) -> HealingPlan:
"""Create healing plan for an issue"""
pattern = self.healing_patterns.get(issue['pattern'])
if not pattern:
# Use AI to create plan
return await self.create_ai_healing_plan(issue)
# Determine actions based on issue severity
actions = []
if issue.get('severity') == 'critical':
actions.append(HealingAction.MIGRATE)
actions.append(HealingAction.RESTART)
elif issue.get('severity') == 'high':
actions.append(HealingAction.RESTART)
actions.append(HealingAction.RECONFIGURE)
else:
actions.append(HealingAction.RECONFIGURE)
return HealingPlan(
issue_id=issue.get('id', 'unknown'),
issue_type=issue['pattern'],
affected_component=issue.get('component', 'unknown'),
actions=actions,
priority=self.calculate_priority(issue),
estimated_time=self.estimate_healing_time(actions),
risk_level=self.assess_risk_level(issue, actions),
automated=issue.get('severity') != 'critical',
verification_steps=['check_health', 'verify_functionality', 'monitor_metrics']
)
async def execute_healing(self, healing: Dict[str, Any]):
"""Execute a healing plan"""
plan = healing['plan']
issue = healing['issue']
logger.info(f"Executing healing plan for: {plan.issue_type}")
healing['status'] = 'executing'
healing['start_time'] = datetime.now()
try:
# Execute each action
for action in plan.actions:
logger.info(f"Executing healing action: {action.value}")
success = await self.execute_healing_action(action, issue)
if not success:
logger.error(f"Healing action failed: {action.value}")
healing['status'] = 'failed'
break
# Verify after each action
if not await self.verify_healing_progress(issue, action):
logger.warning("Healing verification failed, continuing...")
# Final verification
if healing['status'] != 'failed':
pattern = self.healing_patterns.get(issue['pattern'])
if pattern and await pattern['verification'](issue):
healing['status'] = 'completed'
logger.info("Healing completed successfully")
else:
healing['status'] = 'partial'
logger.warning("Healing partially successful")
except Exception as e:
logger.error(f"Healing execution error: {e}")
healing['status'] = 'error'
healing['error'] = str(e)
finally:
healing['end_time'] = datetime.now()
healing['duration'] = (healing['end_time'] - healing['start_time']).total_seconds()
# Record in history
self.healing_history.append(healing)
async def execute_healing_action(self,
action: HealingAction,
issue: Dict[str, Any]) -> bool:
"""Execute specific healing action"""
try:
if action == HealingAction.RESTART:
return await self.restart_component(issue['component'])
elif action == HealingAction.SCALE:
return await self.scale_component(issue['component'], issue.get('scale_factor', 2))
elif action == HealingAction.MIGRATE:
return await self.migrate_workload(issue['component'])
elif action == HealingAction.RECONFIGURE:
return await self.reconfigure_component(issue['component'], issue.get('config'))
elif action == HealingAction.REPLACE:
return await self.replace_component(issue['component'])
elif action == HealingAction.ROLLBACK:
return await self.rollback_component(issue['component'])
elif action == HealingAction.PATCH:
return await self.patch_component(issue['component'])
return False
except Exception as e:
logger.error(f"Healing action execution failed: {e}")
return False
# Detection methods
async def detect_pod_crash_loop(self) -> List[Dict[str, Any]]:
"""Detect pods in crash loop"""
issues = []
# Check Kubernetes pods
pods = await self.get_pod_status()
for pod in pods:
if pod['restart_count'] > 5 and pod['age'] < 3600: # 5 restarts in 1 hour
issues.append({
'component': f"pod/{pod['name']}",
'namespace': pod['namespace'],
'severity': 'high',
'restart_count': pod['restart_count'],
'reason': pod.get('last_termination_reason', 'unknown')
})
return issues
async def detect_memory_leak(self) -> List[Dict[str, Any]]:
"""Detect potential memory leaks"""
issues = []
# Check memory trends
memory_trends = await self.get_memory_trends()
for component, trend in memory_trends.items():
if trend['slope'] > 0.1 and trend['duration'] > 3600: # Growing for 1 hour
issues.append({
'component': component,
'severity': 'medium',
'memory_growth_rate': trend['slope'],
'current_usage': trend['current'],
'predicted_oom': trend['time_to_limit']
})
return issues
async def detect_service_degradation(self) -> List[Dict[str, Any]]:
"""Detect service performance degradation"""
issues = []
# Check service metrics
services = await self.get_service_metrics()
for service in services:
# Check latency
if service['p99_latency'] > service['slo_latency'] * 1.5:
issues.append({
'component': f"service/{service['name']}",
'severity': 'high',
'metric': 'latency',
'current_value': service['p99_latency'],
'slo_value': service['slo_latency']
})
# Check error rate
if service['error_rate'] > 0.05: # 5% error rate
issues.append({
'component': f"service/{service['name']}",
'severity': 'critical',
'metric': 'error_rate',
'current_value': service['error_rate'],
'threshold': 0.05
})
return issues
# Healing methods
async def heal_pod_crash_loop(self, issue: Dict[str, Any]) -> bool:
"""Heal pod in crash loop"""
pod_name = issue['component'].split('/')[-1]
namespace = issue.get('namespace', 'default')
# Check crash reason
reason = issue.get('reason', '')
if 'OOMKilled' in reason:
# Increase memory limits
return await self.increase_pod_resources(pod_name, namespace, 'memory')
elif 'Error' in reason or 'CrashLoopBackOff' in reason:
# Check logs for common issues
logs = await self.get_pod_logs(pod_name, namespace)
if 'connection refused' in logs:
# Wait for dependencies
await asyncio.sleep(30)
return await self.restart_pod(pod_name, namespace)
elif 'permission denied' in logs:
# Fix permissions
return await self.fix_pod_permissions(pod_name, namespace)
# Default: restart with backoff
return await self.restart_pod_with_backoff(pod_name, namespace)
async def heal_memory_leak(self, issue: Dict[str, Any]) -> bool:
"""Heal memory leak"""
component = issue['component']
# Immediate: Restart to free memory
await self.restart_component(component)
# Long-term: Schedule periodic restarts
await self.schedule_periodic_restart(component, interval=3600*6) # Every 6 hours
# Alert for code fix
await self.create_memory_leak_ticket(component, issue)
return True
async def create_ai_healing_plan(self, issue: Dict[str, Any]) -> HealingPlan:
"""Use AI to create healing plan for unknown issues"""
prompt = f"""
Create a healing plan for this issue:
Issue: {json.dumps(issue, indent=2)}
Consider:
1. Root cause analysis
2. Immediate remediation steps
3. Long-term prevention
4. Risk assessment
5. Verification steps
Provide a structured healing plan.
"""
ai_plan = await self.call_ai_model(prompt)
# Parse AI response into HealingPlan
return HealingPlan(
issue_id=issue.get('id', 'ai-generated'),
issue_type='unknown',
affected_component=issue.get('component', 'unknown'),
actions=[HealingAction.RESTART], # Safe default
priority=5,
estimated_time=300,
risk_level='medium',
automated=False, # Require approval for AI plans
verification_steps=['monitor', 'verify']
)
async def learn_from_healing(self, healing: Dict[str, Any]):
"""Learn from healing outcomes"""
outcome = {
'pattern': healing['issue']['pattern'],
'actions': [a.value for a in healing['plan'].actions],
'duration': healing.get('duration', 0),
'success': healing['status'] == 'completed'
}
# Update pattern effectiveness
pattern_key = f"{outcome['pattern']}:{','.join(outcome['actions'])}"
if pattern_key not in self.healing_patterns:
self.healing_patterns[pattern_key] = {
'success_count': 0,
'failure_count': 0,
'avg_duration': 0
}
if outcome['success']:
self.healing_patterns[pattern_key]['success_count'] += 1
else:
self.healing_patterns[pattern_key]['failure_count'] += 1
# Update average duration
current_avg = self.healing_patterns[pattern_key]['avg_duration']
count = (self.healing_patterns[pattern_key]['success_count'] +
self.healing_patterns[pattern_key]['failure_count'])
self.healing_patterns[pattern_key]['avg_duration'] = (
(current_avg * (count - 1) + outcome['duration']) / count
)
async def get_state(self) -> Dict[str, Any]:
"""Get self-healing agent state"""
return {
'active_healings': len(self.active_healings),
'healing_history': len(self.healing_history),
'pattern_effectiveness': self.calculate_pattern_effectiveness(),
'common_issues': self.get_common_issues(),
'healing_success_rate': self.calculate_success_rate()
}
def calculate_pattern_effectiveness(self) -> Dict[str, float]:
"""Calculate effectiveness of healing patterns"""
effectiveness = {}
for pattern, stats in self.healing_patterns.items():
if isinstance(stats, dict) and 'success_count' in stats:
total = stats['success_count'] + stats['failure_count']
if total > 0:
effectiveness[pattern] = stats['success_count'] / total
return effectiveness
def calculate_success_rate(self) -> float:
"""Calculate overall healing success rate"""
if not self.healing_history:
return 1.0
successful = len([h for h in self.healing_history if h['status'] == 'completed'])
return successful / len(self.healing_history)Create platform/api.py:
from fastapi import FastAPI, WebSocket, HTTPException, BackgroundTasks
from fastapi.responses import HTMLResponse
from fastapi.staticfiles import StaticFiles
import uvicorn
from typing import Dict, Any, List
import json
import asyncio
from datetime import datetime
import structlog
from platform.intelligent_platform import IntelligentPlatformBrain
logger = structlog.get_logger()
class PlatformAPI:
"""API for the Intelligent Platform"""
def __init__(self, platform_brain: IntelligentPlatformBrain):
self.brain = platform_brain
self.app = FastAPI(title="Intelligent Platform API")
self.setup_routes()
self.websocket_clients = []
def setup_routes(self):
"""Setup API routes"""
@self.app.get("/")
async def root():
return {"message": "Intelligent Platform API", "version": "1.0.0"}
@self.app.get("/health")
async def health():
"""Platform health check"""
if self.brain.state:
return {
"status": "healthy",
"health_score": self.brain.state.health_score,
"timestamp": self.brain.state.timestamp.isoformat()
}
return {"status": "initializing"}
@self.app.get("/state")
async def get_platform_state():
"""Get current platform state"""
if not self.brain.state:
raise HTTPException(status_code=503, detail="Platform state not available")
return {
"timestamp": self.brain.state.timestamp.isoformat(),
"health_score": self.brain.state.health_score,
"performance_metrics": self.brain.state.performance_metrics,
"security_posture": self.brain.state.security_posture,
"cost_metrics": self.brain.state.cost_metrics,
"compliance_status": self.brain.state.compliance_status,
"active_workloads": len(self.brain.state.active_workloads),
"resource_utilization": self.brain.state.resource_utilization,
"predictions": self.brain.state.predictions,
"anomalies": self.brain.state.anomalies
}
@self.app.get("/decisions")
async def get_recent_decisions():
"""Get recent platform decisions"""
recent = self.brain.decision_history[-20:]
return {
"total_decisions": len(self.brain.decision_history),
"recent_decisions": [
{
"id": d.id,
"timestamp": d.timestamp.isoformat(),
"type": d.decision_type,
"action": d.action,
"confidence": d.confidence,
"expected_impact": d.expected_impact
}
for d in recent
]
}
@self.app.get("/insights")
async def get_platform_insights():
"""Get AI-generated platform insights"""
insights = await self.brain.generate_platform_insights()
return insights
@self.app.get("/agents")
async def get_agent_status():
"""Get status of all agents"""
agent_status = {}
for name, agent in self.brain.agents.items():
if hasattr(agent, 'get_state'):
agent_status[name] = await agent.get_state()
else:
agent_status[name] = {"status": "active"}
return agent_status
@self.app.post("/objectives")
async def update_objectives(objectives: Dict[str, float]):
"""Update platform objectives"""
# Validate objectives
total_weight = sum(objectives.values())
if abs(total_weight - 1.0) > 0.01:
raise HTTPException(status_code=400, detail="Objective weights must sum to 1.0")
# Update objectives
for obj_name, weight in objectives.items():
if obj_name in [o.value for o in self.brain.objectives.keys()]:
# Update weight
pass
return {"status": "objectives updated"}
@self.app.post("/crisis")
async def trigger_crisis_response(crisis: Dict[str, Any]):
"""Trigger crisis response"""
await self.brain.handle_crisis(crisis['type'], crisis['data'])
return {"status": "crisis response initiated"}
@self.app.get("/dashboard")
async def get_dashboard():
"""Get executive dashboard data"""
return await self.brain.generate_executive_dashboard()
@self.app.websocket("/ws")
async def websocket_endpoint(websocket: WebSocket):
"""WebSocket for real-time updates"""
await websocket.accept()
self.websocket_clients.append(websocket)
try:
while True:
# Send platform updates
update = await self.generate_real_time_update()
await websocket.send_json(update)
await asyncio.sleep(5)
except Exception as e:
logger.error(f"WebSocket error: {e}")
finally:
self.websocket_clients.remove(websocket)
async def generate_real_time_update(self) -> Dict[str, Any]:
"""Generate real-time platform update"""
return {
"timestamp": datetime.now().isoformat(),
"health_score": self.brain.state.health_score if self.brain.state else 0,
"active_decisions": len(self.brain.active_tasks) if hasattr(self.brain, 'active_tasks') else 0,
"recent_events": await self.get_recent_events(),
"metrics": await self.get_real_time_metrics()
}
async def get_recent_events(self) -> List[Dict[str, Any]]:
"""Get recent platform events"""
# Implementation depends on event storage
return []
async def get_real_time_metrics(self) -> Dict[str, Any]:
"""Get real-time platform metrics"""
if not self.brain.state:
return {}
return {
"performance": {
"latency_ms": self.brain.state.performance_metrics.get('avg_latency_ms', 0),
"throughput_rps": self.brain.state.performance_metrics.get('throughput_rps', 0),
"error_rate": self.brain.state.performance_metrics.get('error_rate', 0)
},
"resources": {
"cpu_utilization": self.brain.state.resource_utilization.get('cpu', 0),
"memory_utilization": self.brain.state.resource_utilization.get('memory', 0),
"storage_utilization": self.brain.state.resource_utilization.get('storage', 0)
},
"cost": {
"current_spend": self.brain.state.cost_metrics.get('current_spend', 0),
"projected_monthly": self.brain.state.cost_metrics.get('projected_monthly', 0)
}
}
async def broadcast_update(self, update: Dict[str, Any]):
"""Broadcast update to all WebSocket clients"""
for client in self.websocket_clients:
try:
await client.send_json(update)
except:
# Client disconnected
passCreate dashboard/intelligent_platform.html:
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Intelligent Platform Dashboard</title>
<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
<script src="https://cdn.tailwindcss.com"></script>
<script src="https://unpkg.com/lucide@latest"></script>
<style>
@keyframes pulse {
0%, 100% { opacity: 1; }
50% { opacity: 0.5; }
}
.pulse { animation: pulse 2s infinite; }
@keyframes slide-in {
from { transform: translateX(100%); opacity: 0; }
to { transform: translateX(0); opacity: 1; }
}
.slide-in { animation: slide-in 0.5s ease-out; }
.health-ring {
stroke-dasharray: 440;
stroke-dashoffset: 440;
animation: fillRing 2s ease-out forwards;
}
@keyframes fillRing {
to { stroke-dashoffset: calc(440 - (440 * var(--health-score) / 100)); }
}
.decision-card {
backdrop-filter: blur(10px);
background: rgba(30, 41, 59, 0.8);
}
</style>
</head>
<body class="bg-gray-900 text-white font-sans">
<div class="min-h-screen">
<!-- Header -->
<header class="bg-gray-800 shadow-lg">
<div class="container mx-auto px-4 py-4">
<div class="flex items-center justify-between">
<div class="flex items-center space-x-4">
<h1 class="text-3xl font-bold bg-gradient-to-r from-blue-500 to-purple-600 bg-clip-text text-transparent">
Intelligent Platform
</h1>
<div id="platform-status" class="flex items-center">
<div class="w-3 h-3 bg-green-500 rounded-full pulse mr-2"></div>
<span class="text-sm text-gray-400">Autonomous Operations Active</span>
</div>
</div>
<div class="text-sm text-gray-400">
<span id="current-time"></span>
</div>
</div>
</div>
</header>
<!-- Main Dashboard -->
<main class="container mx-auto px-4 py-8">
<!-- Platform Health Overview -->
<section class="mb-8">
<div class="grid grid-cols-1 lg:grid-cols-4 gap-6">
<!-- Health Score -->
<div class="bg-gray-800 rounded-lg p-6 relative overflow-hidden">
<div class="absolute top-0 right-0 w-32 h-32 bg-gradient-to-br from-blue-500/20 to-purple-600/20 rounded-full blur-2xl"></div>
<h3 class="text-lg font-semibold mb-4">Platform Health</h3>
<div class="relative">
<svg class="w-32 h-32 mx-auto">
<circle cx="64" cy="64" r="56" stroke="#374151" stroke-width="8" fill="none"/>
<circle cx="64" cy="64" r="56" stroke="url(#health-gradient)" stroke-width="8" fill="none"
class="health-ring" style="--health-score: 95"/>
</svg>
<div class="absolute inset-0 flex items-center justify-center">
<span id="health-score" class="text-3xl font-bold">95%</span>
</div>
<defs>
<linearGradient id="health-gradient" x1="0%" y1="0%" x2="100%" y2="100%">
<stop offset="0%" style="stop-color:#3B82F6"/>
<stop offset="100%" style="stop-color:#8B5CF6"/>
</linearGradient>
</defs>
</div>
</div>
<!-- Active Decisions -->
<div class="bg-gray-800 rounded-lg p-6">
<h3 class="text-lg font-semibold mb-4">Autonomous Decisions</h3>
<div class="space-y-2">
<div class="flex justify-between items-center">
<span class="text-gray-400">Today</span>
<span id="decisions-today" class="text-2xl font-bold">142</span>
</div>
<div class="flex justify-between items-center">
<span class="text-gray-400">Success Rate</span>
<span id="decision-success" class="text-xl text-green-500">98.6%</span>
</div>
<div class="flex justify-between items-center">
<span class="text-gray-400">Avg Time</span>
<span id="decision-time" class="text-xl">1.2s</span>
</div>
</div>
</div>
<!-- Cost Optimization -->
<div class="bg-gray-800 rounded-lg p-6">
<h3 class="text-lg font-semibold mb-4">Cost Optimization</h3>
<div class="space-y-2">
<div class="flex justify-between items-center">
<span class="text-gray-400">Monthly Savings</span>
<span id="monthly-savings" class="text-2xl font-bold text-green-500">$12.4K</span>
</div>
<div class="flex justify-between items-center">
<span class="text-gray-400">Efficiency</span>
<span id="cost-efficiency" class="text-xl">87%</span>
</div>
<div class="flex justify-between items-center">
<span class="text-gray-400">Utilization</span>
<span id="resource-util" class="text-xl">76%</span>
</div>
</div>
</div>
<!-- Security Status -->
<div class="bg-gray-800 rounded-lg p-6">
<h3 class="text-lg font-semibold mb-4">Security Posture</h3>
<div class="space-y-2">
<div class="flex justify-between items-center">
<span class="text-gray-400">Threat Level</span>
<span id="threat-level" class="text-xl text-green-500">Low</span>
</div>
<div class="flex justify-between items-center">
<span class="text-gray-400">Blocked Threats</span>
<span id="blocked-threats" class="text-2xl font-bold">1,247</span>
</div>
<div class="flex justify-between items-center">
<span class="text-gray-400">Compliance</span>
<span id="compliance-score" class="text-xl">99.2%</span>
</div>
</div>
</div>
</div>
</section>
<!-- Real-time Metrics -->
<section class="mb-8">
<h2 class="text-2xl font-bold mb-4">Real-time Performance</h2>
<div class="grid grid-cols-1 lg:grid-cols-2 gap-6">
<!-- Performance Chart -->
<div class="bg-gray-800 rounded-lg p-6">
<h3 class="text-lg font-semibold mb-4">System Performance</h3>
<canvas id="performance-chart"></canvas>
</div>
<!-- Resource Utilization -->
<div class="bg-gray-800 rounded-lg p-6">
<h3 class="text-lg font-semibold mb-4">Resource Utilization</h3>
<canvas id="resource-chart"></canvas>
</div>
</div>
</section>
<!-- Autonomous Decisions -->
<section class="mb-8">
<h2 class="text-2xl font-bold mb-4">Recent Autonomous Decisions</h2>
<div id="decisions-container" class="space-y-4">
<!-- Decision cards will be inserted here -->
</div>
</section>
<!-- Agent Status -->
<section class="mb-8">
<h2 class="text-2xl font-bold mb-4">Agent Ecosystem</h2>
<div class="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-4 gap-4">
<!-- CI/CD Agent -->
<div class="bg-gray-800 rounded-lg p-4">
<div class="flex items-center justify-between mb-2">
<h4 class="font-semibold">CI/CD Agent</h4>
<span class="w-2 h-2 bg-green-500 rounded-full"></span>
</div>
<div class="text-sm text-gray-400">
<p>Deployments: <span class="text-white">24</span></p>
<p>Success Rate: <span class="text-green-500">100%</span></p>
</div>
</div>
<!-- Security Agent -->
<div class="bg-gray-800 rounded-lg p-4">
<div class="flex items-center justify-between mb-2">
<h4 class="font-semibold">Security Agent</h4>
<span class="w-2 h-2 bg-green-500 rounded-full"></span>
</div>
<div class="text-sm text-gray-400">
<p>Threats Blocked: <span class="text-white">142</span></p>
<p>Active Hunts: <span class="text-yellow-500">3</span></p>
</div>
</div>
<!-- Infrastructure Agent -->
<div class="bg-gray-800 rounded-lg p-4">
<div class="flex items-center justify-between mb-2">
<h4 class="font-semibold">Infrastructure Agent</h4>
<span class="w-2 h-2 bg-green-500 rounded-full"></span>
</div>
<div class="text-sm text-gray-400">
<p>Auto-Scaled: <span class="text-white">18</span></p>
<p>Cost Saved: <span class="text-green-500">$1.2K</span></p>
</div>
</div>
<!-- Self-Healing Agent -->
<div class="bg-gray-800 rounded-lg p-4">
<div class="flex items-center justify-between mb-2">
<h4 class="font-semibold">Self-Healing Agent</h4>
<span class="w-2 h-2 bg-green-500 rounded-full"></span>
</div>
<div class="text-sm text-gray-400">
<p>Issues Fixed: <span class="text-white">67</span></p>
<p>MTTR: <span class="text-green-500">42s</span></p>
</div>
</div>
</div>
</section>
<!-- Predictions -->
<section>
<h2 class="text-2xl font-bold mb-4">AI Predictions</h2>
<div class="grid grid-cols-1 lg:grid-cols-3 gap-6">
<div class="bg-gray-800 rounded-lg p-6">
<h3 class="text-lg font-semibold mb-2">Performance Forecast</h3>
<canvas id="prediction-chart"></canvas>
</div>
<div class="bg-gray-800 rounded-lg p-6">
<h3 class="text-lg font-semibold mb-2">Capacity Planning</h3>
<div class="space-y-2 mt-4">
<p class="text-sm text-gray-400">Next scaling event: <span class="text-white">2h 14m</span></p>
<p class="text-sm text-gray-400">Predicted load: <span class="text-yellow-500">+34%</span></p>
<p class="text-sm text-gray-400">Resource need: <span class="text-white">+4 nodes</span></p>
</div>
</div>
<div class="bg-gray-800 rounded-lg p-6">
<h3 class="text-lg font-semibold mb-2">Cost Projection</h3>
<div class="space-y-2 mt-4">
<p class="text-sm text-gray-400">Monthly estimate: <span class="text-white">$24,500</span></p>
<p class="text-sm text-gray-400">Optimization potential: <span class="text-green-500">$3,200</span></p>
<p class="text-sm text-gray-400">Recommended actions: <span class="text-white">3</span></p>
</div>
</div>
</div>
</section>
</main>
</div>
<script>
// WebSocket connection
const ws = new WebSocket('ws://localhost:8002/ws');
// Charts
let performanceChart, resourceChart, predictionChart;
// Initialize
document.addEventListener('DOMContentLoaded', function() {
initializeCharts();
updateTime();
setInterval(updateTime, 1000);
});
// Update current time
function updateTime() {
document.getElementById('current-time').textContent = new Date().toLocaleString();
}
// Initialize charts
function initializeCharts() {
// Performance Chart
const perfCtx = document.getElementById('performance-chart').getContext('2d');
performanceChart = new Chart(perfCtx, {
type: 'line',
data: {
labels: [],
datasets: [{
label: 'Latency (ms)',
data: [],
borderColor: 'rgb(59, 130, 246)',
tension: 0.1,
yAxisID: 'y-latency'
}, {
label: 'Throughput (rps)',
data: [],
borderColor: 'rgb(34, 197, 94)',
tension: 0.1,
yAxisID: 'y-throughput'
}]
},
options: {
responsive: true,
interaction: {
mode: 'index',
intersect: false,
},
scales: {
'y-latency': {
type: 'linear',
display: true,
position: 'left'
},
'y-throughput': {
type: 'linear',
display: true,
position: 'right',
grid: {
drawOnChartArea: false
}
}
}
}
});
// Resource Chart
const resourceCtx = document.getElementById('resource-chart').getContext('2d');
resourceChart = new Chart(resourceCtx, {
type: 'bar',
data: {
labels: ['CPU', 'Memory', 'Storage', 'Network'],
datasets: [{
label: 'Current Usage %',
data: [65, 72, 45, 58],
backgroundColor: [
'rgba(59, 130, 246, 0.8)',
'rgba(34, 197, 94, 0.8)',
'rgba(251, 191, 36, 0.8)',
'rgba(139, 92, 246, 0.8)'
]
}]
},
options: {
responsive: true,
scales: {
y: {
beginAtZero: true,
max: 100
}
}
}
});
// Prediction Chart
const predCtx = document.getElementById('prediction-chart').getContext('2d');
predictionChart = new Chart(predCtx, {
type: 'line',
data: {
labels: ['Now', '+1h', '+2h', '+3h', '+4h', '+6h', '+12h', '+24h'],
datasets: [{
label: 'Predicted Load',
data: [100, 115, 134, 125, 118, 105, 95, 110],
borderColor: 'rgb(251, 191, 36)',
backgroundColor: 'rgba(251, 191, 36, 0.1)',
fill: true,
tension: 0.4
}]
},
options: {
responsive: true,
scales: {
y: {
beginAtZero: true
}
}
}
});
}
// WebSocket message handler
ws.onmessage = function(event) {
const data = JSON.parse(event.data);
updateDashboard(data);
};
// Update dashboard with real-time data
function updateDashboard(data) {
// Update health score
const healthScore = data.health_score || 95;
document.getElementById('health-score').textContent = healthScore + '%';
document.querySelector('.health-ring').style.setProperty('--health-score', healthScore);
// Update metrics
if (data.metrics) {
updateMetrics(data.metrics);
}
// Update decisions
if (data.recent_events) {
updateDecisions(data.recent_events);
}
// Update charts
updateCharts(data);
}
function updateMetrics(metrics) {
// Update performance metrics
if (metrics.performance) {
const latency = metrics.performance.latency_ms;
const throughput = metrics.performance.throughput_rps;
// Add to chart
const time = new Date().toLocaleTimeString();
performanceChart.data.labels.push(time);
performanceChart.data.datasets[0].data.push(latency);
performanceChart.data.datasets[1].data.push(throughput);
// Keep last 20 points
if (performanceChart.data.labels.length > 20) {
performanceChart.data.labels.shift();
performanceChart.data.datasets[0].data.shift();
performanceChart.data.datasets[1].data.shift();
}
performanceChart.update();
}
// Update resource utilization
if (metrics.resources) {
resourceChart.data.datasets[0].data = [
metrics.resources.cpu_utilization,
metrics.resources.memory_utilization,
metrics.resources.storage_utilization,
58 // Network placeholder
];
resourceChart.update();
}
}
function updateDecisions(events) {
const container = document.getElementById('decisions-container');
// Add new decisions
events.forEach(event => {
const card = createDecisionCard(event);
container.insertBefore(card, container.firstChild);
// Remove old cards
while (container.children.length > 5) {
container.removeChild(container.lastChild);
}
});
}
function createDecisionCard(decision) {
const card = document.createElement('div');
card.className = 'decision-card rounded-lg p-4 slide-in';
const typeColors = {
'scaling': 'text-blue-500',
'deployment': 'text-green-500',
'security': 'text-red-500',
'cost': 'text-yellow-500',
'healing': 'text-purple-500'
};
const color = typeColors[decision.type] || 'text-gray-500';
card.innerHTML = `
<div class="flex items-center justify-between">
<div>
<h4 class="font-semibold ${color}">${decision.type.toUpperCase()}</h4>
<p class="text-sm text-gray-400">${decision.action}</p>
<p class="text-xs text-gray-500">${new Date(decision.timestamp).toLocaleTimeString()}</p>
</div>
<div class="text-right">
<p class="text-sm">Confidence: <span class="font-semibold">${(decision.confidence * 100).toFixed(1)}%</span></p>
<p class="text-xs text-gray-500">Impact: ${decision.impact}</p>
</div>
</div>
`;
return card;
}
function updateCharts(data) {
// Update performance chart with real data
if (data.metrics && data.metrics.performance) {
const time = new Date().toLocaleTimeString();
performanceChart.data.labels.push(time);
performanceChart.data.datasets[0].data.push(data.metrics.performance.latency_ms);
performanceChart.data.datasets[1].data.push(data.metrics.performance.throughput_rps);
if (performanceChart.data.labels.length > 20) {
performanceChart.data.labels.shift();
performanceChart.data.datasets.forEach(dataset => dataset.data.shift());
}
performanceChart.update();
}
}
// Connection status
ws.onopen = function() {
document.getElementById('platform-status').innerHTML = `
<div class="w-3 h-3 bg-green-500 rounded-full pulse mr-2"></div>
<span class="text-sm text-gray-400">Autonomous Operations Active</span>
`;
};
ws.onclose = function() {
document.getElementById('platform-status').innerHTML = `
<div class="w-3 h-3 bg-red-500 rounded-full mr-2"></div>
<span class="text-sm text-gray-400">Connection Lost</span>
`;
};
</script>
</body>
</html>Create main_platform.py:
#!/usr/bin/env python3
"""
Intelligent Platform - Main Application
"""
import asyncio
import argparse
import sys
import os
from pathlib import Path
import json
import signal
import logging
from datetime import datetime
import uvicorn
from platform.intelligent_platform import IntelligentPlatformBrain
from platform.api import PlatformAPI
import structlog
# Configure structured logging
structlog.configure(
processors=[
structlog.stdlib.filter_by_level,
structlog.stdlib.add_logger_name,
structlog.stdlib.add_log_level,
structlog.stdlib.PositionalArgumentsFormatter(),
structlog.processors.TimeStamper(fmt="iso"),
structlog.processors.StackInfoRenderer(),
structlog.processors.format_exc_info,
structlog.dev.ConsoleRenderer()
],
context_class=dict,
logger_factory=structlog.stdlib.LoggerFactory(),
cache_logger_on_first_use=True,
)
logger = structlog.get_logger()
class IntelligentPlatform:
"""Main Intelligent Platform Application"""
def __init__(self, config: Dict[str, Any]):
self.config = config
self.brain = IntelligentPlatformBrain(config)
self.api = PlatformAPI(self.brain)
self.running = False
async def start(self):
"""Start the Intelligent Platform"""
logger.info("🚀 Starting Intelligent Platform...")
self.running = True
# Initialize platform brain
await self.brain.initialize()
logger.info("✅ Platform Brain initialized")
# Start simulation if in demo mode
if self.config.get('demo_mode'):
asyncio.create_task(self.run_demo_simulation())
# Start API server
config = uvicorn.Config(
self.api.app,
host=self.config.get('api_host', '0.0.0.0'),
port=self.config.get('api_port', 8002),
log_level="info"
)
server = uvicorn.Server(config)
logger.info(f"🌐 API server starting on {config.host}:{config.port}")
# Run server
await server.serve()
async def run_demo_simulation(self):
"""Run demo simulation for the platform"""
logger.info("🎮 Running in demo mode - simulating platform events")
while self.running:
try:
# Simulate various events
await self.simulate_workload_changes()
await self.simulate_security_events()
await self.simulate_performance_issues()
await self.simulate_cost_events()
await asyncio.sleep(30) # Run simulation every 30 seconds
except Exception as e:
logger.error(f"Demo simulation error: {e}")
async def simulate_workload_changes(self):
"""Simulate workload changes"""
import random
# Simulate traffic spike
if random.random() < 0.1: # 10% chance
logger.info("📈 Simulating traffic spike")
# This would trigger scaling decisions
async def simulate_security_events(self):
"""Simulate security events"""
import random
if random.random() < 0.05: # 5% chance
logger.info("🔒 Simulating security threat")
# This would trigger security response
async def simulate_performance_issues(self):
"""Simulate performance degradation"""
import random
if random.random() < 0.08: # 8% chance
logger.info("⚡ Simulating performance issue")
# This would trigger healing actions
async def simulate_cost_events(self):
"""Simulate cost optimization opportunities"""
import random
if random.random() < 0.15: # 15% chance
logger.info("💰 Simulating cost optimization opportunity")
# This would trigger cost optimization
async def stop(self):
"""Stop the Intelligent Platform"""
logger.info("🛑 Stopping Intelligent Platform...")
self.running = False
# Cleanup
for agent in self.brain.agents.values():
if hasattr(agent, 'close'):
await agent.close()
logger.info("✅ Platform stopped")
async def main():
"""Main entry point"""
parser = argparse.ArgumentParser(
description="Intelligent Platform - Self-Managing Infrastructure",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
Examples:
# Start platform with default config
python main_platform.py
# Start with custom config
python main_platform.py --config platform-config.json
# Start in demo mode
python main_platform.py --demo
# Custom port
python main_platform.py --port 8080
"""
)
parser.add_argument('--config', default='platform-config.json',
help='Configuration file')
parser.add_argument('--demo', action='store_true',
help='Run in demo mode with simulated events')
parser.add_argument('--port', type=int, default=8002,
help='API port (default: 8002)')
parser.add_argument('--log-level', default='INFO',
choices=['DEBUG', 'INFO', 'WARNING', 'ERROR'],
help='Log level')
args = parser.parse_args()
# Set log level
logging.getLogger().setLevel(getattr(logging, args.log_level))
# Load configuration
config_path = Path(args.config)
if config_path.exists():
with open(config_path, 'r') as f:
config = json.load(f)
else:
# Default configuration
config = {
'openai_api_key': os.getenv('OPENAI_API_KEY'),
'anthropic_api_key': os.getenv('ANTHROPIC_API_KEY'),
'aws_enabled': os.getenv('AWS_ACCESS_KEY_ID') is not None,
'azure_enabled': os.getenv('AZURE_SUBSCRIPTION_ID') is not None,
'gcp_enabled': os.getenv('GOOGLE_APPLICATION_CREDENTIALS') is not None,
'elasticsearch_url': os.getenv('ELASTICSEARCH_URL', 'http://localhost:9200'),
'database_url': os.getenv('DATABASE_URL', 'postgresql://user:pass@localhost/platform'),
'redis_url': os.getenv('REDIS_URL', 'redis://localhost:6379'),
'api_host': '0.0.0.0',
'api_port': args.port,
'demo_mode': args.demo
}
# Validate configuration
if not config.get('openai_api_key'):
logger.error("❌ OpenAI API key not configured")
logger.info("Set OPENAI_API_KEY environment variable or add to config file")
sys.exit(1)
# Create platform
platform = IntelligentPlatform(config)
# Setup signal handlers
def signal_handler(sig, frame):
logger.info("Received shutdown signal")
asyncio.create_task(platform.stop())
sys.exit(0)
signal.signal(signal.SIGINT, signal_handler)
signal.signal(signal.SIGTERM, signal_handler)
try:
# ASCII art banner
print("""
╔═══════════════════════════════════════════════════════════╗
║ INTELLIGENT PLATFORM ║
║ Self-Managing • Self-Healing • Self-Evolving ║
╚═══════════════════════════════════════════════════════════╝
""")
# Start platform
await platform.start()
except Exception as e:
logger.error(f"Platform failed: {e}")
sys.exit(1)
if __name__ == "__main__":
asyncio.run(main())Create platform-config.json:
{
"platform_name": "Intelligent Platform Demo",
"version": "1.0.0",
"ai_services": {
"openai_api_key": "${OPENAI_API_KEY}",
"anthropic_api_key": "${ANTHROPIC_API_KEY}",
"azure_openai_endpoint": "${AZURE_OPENAI_ENDPOINT}",
"azure_openai_key": "${AZURE_OPENAI_KEY}"
},
"cloud_providers": {
"aws_enabled": true,
"aws_region": "us-east-1",
"azure_enabled": true,
"azure_subscription_id": "${AZURE_SUBSCRIPTION_ID}",
"gcp_enabled": false
},
"infrastructure": {
"kubernetes_enabled": true,
"kubernetes_config": "~/.kube/config",
"docker_enabled": true,
"terraform_enabled": true
},
"data_stores": {
"elasticsearch_url": "http://localhost:9200",
"database_url": "postgresql://platform:password@localhost/intelligent_platform",
"redis_url": "redis://localhost:6379",
"prometheus_url": "http://localhost:9090"
},
"objectives": {
"performance_weight": 0.25,
"cost_weight": 0.20,
"security_weight": 0.25,
"reliability_weight": 0.20,
"compliance_weight": 0.05,
"user_experience_weight": 0.05
},
"agents": {
"cicd": {
"enabled": true,
"auto_deploy": true,
"canary_enabled": true,
"rollback_threshold": 0.05
},
"security": {
"enabled": true,
"threat_detection": true,
"auto_response": true,
"compliance_frameworks": ["SOC2", "ISO27001", "GDPR"]
},
"infrastructure": {
"enabled": true,
"auto_scaling": true,
"cost_optimization": true,
"multi_cloud": true
},
"healing": {
"enabled": true,
"auto_heal": true,
"learning_enabled": true
}
},
"api": {
"host": "0.0.0.0",
"port": 8002,
"cors_enabled": true,
"auth_enabled": false
},
"monitoring": {
"metrics_interval": 60,
"log_level": "INFO",
"trace_enabled": true,
"dashboard_enabled": true
},
"demo_mode": true,
"learning_enabled": true,
"evolution_enabled": true
}- Set up the complete environment:
# Navigate to exercise directory
cd exercises/exercise3-intelligent-platform
# Create virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install all dependencies
pip install -r requirements.txtCreate requirements.txt:
# Core
asyncio==3.4.3
aiohttp==3.9.3
aioredis==2.0.1
fastapi==0.109.2
uvicorn==0.27.1
websockets==12.0
# AI/ML
openai==1.12.0
anthropic==0.18.1
langchain==0.1.9
tensorflow==2.15.0
scikit-learn==1.4.0
numpy==1.26.3
pandas==2.2.0
networkx==3.2.1
# Infrastructure
kubernetes==29.0.0
boto3==1.33.0
azure-mgmt-compute==30.4.0
google-cloud-compute==1.14.1
docker==7.0.0
# Observability
prometheus-client==0.19.0
elasticsearch==8.12.1
structlog==24.1.0
# Distributed Computing
ray[default]==2.9.1
ray[serve]==2.9.1
# Database
asyncpg==0.29.0
redis==5.0.1
# Security
cryptography==42.0.2
yara-python==4.3.1- Start required services:
# Start all services with Docker Compose
docker-compose up -dCreate docker-compose.yml:
version: '3.8'
services:
elasticsearch:
image: elasticsearch:8.12.1
environment:
- discovery.type=single-node
- xpack.security.enabled=false
ports:
- "9200:9200"
volumes:
- es_data:/usr/share/elasticsearch/data
postgres:
image: postgres:16
environment:
POSTGRES_DB: intelligent_platform
POSTGRES_USER: platform
POSTGRES_PASSWORD: password
ports:
- "5432:5432"
volumes:
- pg_data:/var/lib/postgresql/data
redis:
image: redis:7-alpine
ports:
- "6379:6379"
volumes:
- redis_data:/data
prometheus:
image: prom/prometheus
ports:
- "9090:9090"
volumes:
- ./prometheus.yml:/etc/prometheus/prometheus.yml
- prom_data:/prometheus
grafana:
image: grafana/grafana
ports:
- "3000:3000"
environment:
- GF_SECURITY_ADMIN_PASSWORD=admin
volumes:
- grafana_data:/var/lib/grafana
volumes:
es_data:
pg_data:
redis_data:
prom_data:
grafana_data:- Configure Kubernetes (if using):
# Create namespace
kubectl create namespace intelligent-platform
# Apply RBAC
kubectl apply -f k8s/rbac.yaml
# Deploy platform components
kubectl apply -f k8s/- Run the Intelligent Platform:
# Export required environment variables
export OPENAI_API_KEY="your-api-key"
export ANTHROPIC_API_KEY="your-api-key" # Optional
# Start the platform
python main_platform.py --demo
# In another terminal, open the dashboard
open dashboard/intelligent_platform.html- Test the platform:
# Check platform health
curl http://localhost:8002/health
# Get platform state
curl http://localhost:8002/state
# View agent status
curl http://localhost:8002/agents
# Get insights
curl http://localhost:8002/insights
# View dashboard
curl http://localhost:8002/dashboardYour Intelligent Platform should now:
- ✅ Coordinate multiple specialized agents autonomously
- ✅ Make intelligent decisions without human intervention
- ✅ Self-heal infrastructure issues automatically
- ✅ Optimize costs continuously
- ✅ Predict and prevent problems
- ✅ Learn from operational patterns
- ✅ Evolve and improve over time
- ✅ Provide comprehensive observability
- ✅ Handle crisis situations autonomously
- ✅ Scale resources based on predictions
-
Advanced AI Integration:
- Implement GPT-4 Vision for dashboard analysis
- Add voice control for platform operations
- Create natural language platform queries
- Implement AI-driven root cause analysis
-
Multi-Cloud Orchestration:
- Add support for AWS, Azure, and GCP
- Implement cross-cloud workload migration
- Optimize costs across clouds
- Handle multi-region deployments
-
Advanced Self-Evolution:
- Implement genetic algorithms for optimization
- Create self-modifying code capabilities
- Add reinforcement learning for decisions
- Implement automated A/B testing
-
Enterprise Features:
- Add multi-tenancy support
- Implement RBAC and SSO
- Create audit trails and compliance reports
- Add disaster recovery automation
You've built a complete Intelligent Platform that represents the pinnacle of Agentic DevOps! This self-managing system can:
- Think and make decisions autonomously
- Heal itself without human intervention
- Optimize continuously for multiple objectives
- Learn and evolve from experience
- Predict and prevent problems
- Coordinate complex multi-agent operations
You've mastered Advanced DevOps & Security with Agentic DevOps. You're now ready for:
- Module 29: Enterprise Architecture Review (.NET)
- Module 30: Ultimate Capstone Challenge
The future of infrastructure is autonomous, and you're now equipped to lead this transformation!