"""Master ecosystem orchestrator coordinating all 46+ automation tools.
This module provides the central coordination hub that brings together:
- Tool registry and capability management
- Workflow orchestration and execution
- Performance monitoring and optimization
- Resource allocation and management
- Strategic planning and ecosystem evolution
Security: Enterprise-grade orchestration with comprehensive security validation.
Performance: <500ms orchestration decisions, distributed execution capabilities.
Type Safety: Complete type system with contracts and validation.
"""
import logging
from dataclasses import dataclass
from datetime import UTC, datetime, timedelta
from typing import Any
from ..core.contracts import require
from ..core.either import Either
from .ecosystem_architecture import (
EcosystemWorkflow,
ExecutionMode,
OptimizationTarget,
OrchestrationError,
OrchestrationId,
OrchestrationResult,
ToolCategory,
WorkflowStep,
create_orchestration_id,
create_step_id,
create_workflow_id,
)
from .performance_monitor import EcosystemPerformanceMonitor, get_performance_monitor
from .resource_manager import IntelligentResourceManager, get_resource_manager
from .strategic_planner import (
EcosystemStrategicPlanner,
EvolutionPhase,
get_strategic_planner,
)
from .tool_registry import ComprehensiveToolRegistry, get_tool_registry
from .workflow_engine import MasterWorkflowEngine, get_workflow_engine
@dataclass
class OrchestrationRequest:
"""Request for ecosystem orchestration operation."""
operation_type: str
parameters: dict[str, Any]
optimization_target: OptimizationTarget
priority: int = 5
timeout: float = 300.0
requesting_agent: str | None = None
@dataclass
class EcosystemStatus:
"""Current status of the entire ecosystem."""
timestamp: datetime
total_tools: int
active_workflows: int
system_health_score: float
performance_level: str
resource_utilization: dict[str, float]
maturity_phase: EvolutionPhase
active_alerts: int
optimization_opportunities: list[str]
strategic_recommendations: list[str]
class EcosystemOrchestrator:
"""Master orchestrator for the complete 46+ tool automation ecosystem."""
def __init__(
self,
tool_registry: ComprehensiveToolRegistry | None = None,
workflow_engine: MasterWorkflowEngine | None = None,
performance_monitor: EcosystemPerformanceMonitor | None = None,
resource_manager: IntelligentResourceManager | None = None,
strategic_planner: EcosystemStrategicPlanner | None = None,
):
# Core components
self.tool_registry = tool_registry or get_tool_registry()
self.workflow_engine = workflow_engine or get_workflow_engine()
self.performance_monitor = performance_monitor or get_performance_monitor()
self.resource_manager = resource_manager or get_resource_manager()
self.strategic_planner = strategic_planner or get_strategic_planner()
self.logger = logging.getLogger(__name__)
# Orchestration state
self.active_orchestrations: dict[OrchestrationId, dict[str, Any]] = {}
self.orchestration_history: list[OrchestrationResult] = []
# System state
self.is_initialized = False
self.is_running = False
self.initialization_time: datetime | None = None
async def initialize(self) -> Either[OrchestrationError, None]:
"""Initialize the complete ecosystem orchestrator."""
try:
self.logger.info("Initializing ecosystem orchestrator...")
# Start all subsystems
await self.performance_monitor.start_monitoring()
await self.resource_manager.start_management()
# Validate tool registry completeness
if len(self.tool_registry.tools) < 40: # Expect at least 40 tools
self.logger.warning(
f"Tool registry contains only {len(self.tool_registry.tools)} tools",
)
# Perform initial system health check
health_check = await self._perform_initial_health_check()
if health_check.is_left():
return health_check
self.is_initialized = True
self.initialization_time = datetime.now(UTC)
self.logger.info("Ecosystem orchestrator initialized successfully")
return Either.right(None)
except Exception as e:
error_msg = f"Ecosystem initialization failed: {e}"
self.logger.error(error_msg)
return Either.left(
OrchestrationError.ecosystem_initialization_failed(error_msg),
)
async def shutdown(self) -> None:
"""Gracefully shutdown the ecosystem orchestrator."""
try:
self.logger.info("Shutting down ecosystem orchestrator...")
# Stop background processes
await self.performance_monitor.stop_monitoring()
await self.resource_manager.stop_management()
# Cancel active orchestrations
for orchestration_id in list(self.active_orchestrations.keys()):
await self._cancel_orchestration(orchestration_id)
self.is_running = False
self.is_initialized = False
self.logger.info("Ecosystem orchestrator shutdown complete")
except Exception as e:
self.logger.error(f"Error during shutdown: {e}")
async def _perform_initial_health_check(self) -> Either[OrchestrationError, None]:
"""Perform initial health check of the ecosystem."""
try:
# Check tool registry
if not self.tool_registry.tools:
return Either.left(OrchestrationError.incomplete_tool_registry())
# Check core categories are represented
required_categories = [ToolCategory.FOUNDATION, ToolCategory.INTELLIGENCE]
for category in required_categories:
tools_in_category = self.tool_registry.find_tools_by_category(category)
if not tools_in_category:
return Either.left(
OrchestrationError.ecosystem_initialization_failed(
f"No tools found in required category: {category.value}",
),
)
# Check resource pools
resource_status = await self.resource_manager.get_resource_status()
if resource_status["system_health"] == "critical":
return Either.left(
OrchestrationError.ecosystem_initialization_failed(
"Critical resource issues detected",
),
)
return Either.right(None)
except Exception as e:
return Either.left(
OrchestrationError.ecosystem_initialization_failed(
f"Health check failed: {e}",
),
)
@require(lambda self: self.is_initialized)
async def orchestrate(
self,
workflow_definition: dict[str, Any] | None = None,
optimization_target: OptimizationTarget = OptimizationTarget.EFFICIENCY,
execution_mode: ExecutionMode = ExecutionMode.ADAPTIVE,
_tool_selection: str = "intelligent",
) -> Either[OrchestrationError, OrchestrationResult]:
"""Orchestrate complex workflow across multiple ecosystem tools."""
orchestration_id = create_orchestration_id()
try:
self.logger.info(f"Starting orchestration {orchestration_id}")
# Track active orchestration
self.active_orchestrations[orchestration_id] = {
"start_time": datetime.now(UTC),
"status": "initializing",
"workflow_definition": workflow_definition,
"optimization_target": optimization_target,
}
# Create or use provided workflow
if workflow_definition:
workflow = await self._create_workflow_from_definition(
workflow_definition,
execution_mode,
)
else:
# Create a default ecosystem health workflow
workflow = await self._create_default_workflow(
execution_mode,
optimization_target,
)
if workflow.is_left():
return workflow
ecosystem_workflow = workflow.right()
# Execute workflow
self.active_orchestrations[orchestration_id]["status"] = "executing"
execution_result = await self.workflow_engine.execute_workflow(
ecosystem_workflow,
optimization_target,
)
if execution_result.is_left():
self.active_orchestrations[orchestration_id]["status"] = "failed"
return execution_result
orchestration_result = execution_result.right()
# Record successful orchestration
self.active_orchestrations[orchestration_id]["status"] = "completed"
self.orchestration_history.append(orchestration_result)
# Clean up
del self.active_orchestrations[orchestration_id]
self.logger.info(f"Orchestration {orchestration_id} completed successfully")
return Either.right(orchestration_result)
except Exception as e:
if orchestration_id in self.active_orchestrations:
self.active_orchestrations[orchestration_id]["status"] = "error"
error_msg = f"Orchestration failed: {e}"
self.logger.error(error_msg)
return Either.left(OrchestrationError.workflow_execution_failed(error_msg))
async def _create_workflow_from_definition(
self,
definition: dict[str, Any],
execution_mode: ExecutionMode,
) -> Either[OrchestrationError, EcosystemWorkflow]:
"""Create workflow from user-provided definition."""
try:
workflow_id = create_workflow_id()
# Parse workflow steps
steps = []
for _i, step_def in enumerate(definition.get("steps", [])):
step_id = create_step_id()
tool_id = step_def.get("tool_id")
if not tool_id or tool_id not in self.tool_registry.tools:
return Either.left(
OrchestrationError.tool_not_found(tool_id or "unknown"),
)
step = WorkflowStep(
step_id=step_id,
tool_id=tool_id,
parameters=step_def.get("parameters", {}),
preconditions=step_def.get("preconditions", []),
postconditions=step_def.get("postconditions", []),
timeout=step_def.get("timeout", 300),
retry_count=step_def.get("retry_count", 3),
parallel_group=step_def.get("parallel_group"),
)
steps.append(step)
# Calculate resource requirements
resource_requirements = {}
for step in steps:
tool = self.tool_registry.tools[step.tool_id]
for resource, amount in tool.resource_requirements.items():
resource_requirements[resource] = (
resource_requirements.get(resource, 0) + amount
)
workflow = EcosystemWorkflow(
workflow_id=workflow_id,
name=definition.get("name", "Custom Workflow"),
description=definition.get("description", "User-defined workflow"),
steps=steps,
execution_mode=execution_mode,
optimization_target=OptimizationTarget.EFFICIENCY,
expected_duration=definition.get("expected_duration", 60.0),
resource_requirements=resource_requirements,
success_criteria=definition.get("success_criteria", []),
)
return Either.right(workflow)
except Exception as e:
return Either.left(
OrchestrationError.workflow_execution_failed(
f"Failed to create workflow: {e}",
),
)
async def _create_default_workflow(
self,
execution_mode: ExecutionMode,
optimization_target: OptimizationTarget,
) -> Either[OrchestrationError, EcosystemWorkflow]:
"""Create default ecosystem health and optimization workflow."""
try:
workflow_id = create_workflow_id()
# Select representative tools from different categories
steps = []
# Foundation tool step
foundation_tools = self.tool_registry.find_tools_by_category(
ToolCategory.FOUNDATION,
)
if foundation_tools:
step = WorkflowStep(
step_id=create_step_id(),
tool_id=foundation_tools[0].tool_id,
parameters={"operation": "health_check"},
postconditions=["foundation_verified"],
)
steps.append(step)
# Intelligence tool step (if available)
intelligence_tools = self.tool_registry.find_tools_by_category(
ToolCategory.INTELLIGENCE,
)
if intelligence_tools:
step = WorkflowStep(
step_id=create_step_id(),
tool_id=intelligence_tools[0].tool_id,
parameters={"operation": "analyze_system"},
preconditions=["foundation_verified"],
postconditions=["intelligence_analysis_complete"],
)
steps.append(step)
# Enterprise tool step (if available)
enterprise_tools = self.tool_registry.find_tools_by_category(
ToolCategory.ENTERPRISE,
)
if enterprise_tools:
step = WorkflowStep(
step_id=create_step_id(),
tool_id=enterprise_tools[0].tool_id,
parameters={"operation": "compliance_check"},
parallel_group="compliance",
)
steps.append(step)
# Calculate total resource requirements
resource_requirements = {}
for step in steps:
tool = self.tool_registry.tools[step.tool_id]
for resource, amount in tool.resource_requirements.items():
resource_requirements[resource] = (
resource_requirements.get(resource, 0) + amount
)
workflow = EcosystemWorkflow(
workflow_id=workflow_id,
name="Ecosystem Health Check",
description="Default workflow for ecosystem health verification and optimization",
steps=steps,
execution_mode=execution_mode,
optimization_target=optimization_target,
expected_duration=180.0, # 3 minutes
resource_requirements=resource_requirements,
success_criteria=["All tools responsive", "System health verified"],
)
return Either.right(workflow)
except Exception as e:
return Either.left(
OrchestrationError.workflow_execution_failed(
f"Failed to create default workflow: {e}",
),
)
async def optimize(
self,
target: OptimizationTarget = OptimizationTarget.EFFICIENCY,
) -> Either[OrchestrationError, dict[str, Any]]:
"""Optimize ecosystem performance across all dimensions."""
try:
self.logger.info(f"Starting ecosystem optimization for {target.value}")
optimization_result = {
"timestamp": datetime.now(UTC).isoformat(),
"optimization_target": target.value,
"actions_performed": [],
"improvements": {},
"recommendations": [],
}
# Resource optimization
resource_optimization = await self.resource_manager.optimize_allocation(
target,
)
if resource_optimization.is_right():
optimization_result["actions_performed"].append("resource_optimization")
optimization_result.update(resource_optimization.right())
# Performance optimization through monitoring
health_report = await self.performance_monitor.get_health_report()
optimization_result["improvements"]["performance"] = {
"health_score": health_report.overall_health_score,
"performance_level": health_report.performance_level.value,
"active_alerts": len(health_report.active_alerts),
}
# Strategic optimization recommendations
current_state = await self.strategic_planner.analyze_current_state()
optimization_result["improvements"]["strategic"] = current_state[
"maturity_assessment"
]
optimization_result["recommendations"].extend(
current_state.get("capability_analysis", {}).get(
"improvement_areas",
[],
),
)
# Tool registry optimization
ecosystem_stats = self.tool_registry.get_ecosystem_statistics()
optimization_result["improvements"]["ecosystem"] = {
"total_tools": ecosystem_stats["total_tools"],
"enterprise_ready": ecosystem_stats["enterprise_ready_tools"],
"ai_enhanced": ecosystem_stats["ai_enhanced_tools"],
}
self.logger.info("Ecosystem optimization completed")
return Either.right(optimization_result)
except Exception as e:
error_msg = f"Optimization failed: {e}"
self.logger.error(error_msg)
return Either.left(OrchestrationError.optimization_failed(error_msg))
async def monitor(self) -> Either[OrchestrationError, dict[str, Any]]:
"""Get comprehensive monitoring data for the ecosystem."""
try:
# Get current performance metrics
current_metrics = await self.performance_monitor.get_current_metrics()
health_report = await self.performance_monitor.get_health_report()
# Get resource status
resource_status = await self.resource_manager.get_resource_status()
# Get ecosystem statistics
ecosystem_stats = self.tool_registry.get_ecosystem_statistics()
monitoring_data = {
"timestamp": datetime.now(UTC).isoformat(),
"system_health": {
"overall_score": current_metrics.get_health_score(),
"performance_level": health_report.performance_level.value,
"active_alerts": len(health_report.active_alerts),
},
"performance_metrics": {
"average_response_time": current_metrics.average_response_time,
"success_rate": current_metrics.success_rate,
"error_rate": current_metrics.error_rate,
"throughput": current_metrics.throughput,
"bottlenecks": current_metrics.bottlenecks,
},
"resource_utilization": resource_status["resource_pools"],
"ecosystem_overview": ecosystem_stats,
"active_workflows": len(self.active_orchestrations),
"orchestration_history_count": len(self.orchestration_history),
}
return Either.right(monitoring_data)
except Exception as e:
error_msg = f"Monitoring failed: {e}"
self.logger.error(error_msg)
return Either.left(OrchestrationError("monitoring_error", None, error_msg))
async def plan(
self,
target_phase: str | None = None,
timeline_months: int = 12,
focus_areas: list[str] | None = None,
) -> Either[OrchestrationError, dict[str, Any]]:
"""Create strategic plan for ecosystem evolution."""
try:
# Convert string parameters to enums
evolution_phase = EvolutionPhase.OPTIMIZATION # Default
if target_phase:
try:
evolution_phase = EvolutionPhase(target_phase.lower())
except ValueError:
return Either.left(
OrchestrationError.strategic_planning_failed(
f"Invalid target phase: {target_phase}",
),
)
# Convert focus areas to categories
focus_categories = []
if focus_areas:
for area in focus_areas:
try:
category = ToolCategory(area.lower())
focus_categories.append(category)
except ValueError:
self.logger.warning(f"Invalid focus area: {area}")
if not focus_categories:
focus_categories = [ToolCategory.INTELLIGENCE, ToolCategory.ENTERPRISE]
# Create strategic roadmap
timeline = timedelta(days=timeline_months * 30)
roadmap_result = await self.strategic_planner.create_strategic_roadmap(
evolution_phase,
timeline,
focus_categories,
)
if roadmap_result.is_left():
return roadmap_result
roadmap = roadmap_result.right()
# Analyze current state
current_state = await self.strategic_planner.analyze_current_state()
# Identify capability gaps
capability_gaps = await self.strategic_planner.identify_capability_gaps()
planning_result = {
"timestamp": datetime.now(UTC).isoformat(),
"current_state": current_state,
"strategic_roadmap": {
"roadmap_id": roadmap.roadmap_id,
"name": roadmap.name,
"current_phase": roadmap.current_phase.value,
"target_phase": roadmap.target_phase.value,
"timeline_days": roadmap.timeline.days,
"initiatives_count": len(roadmap.initiatives),
"milestones_count": len(roadmap.milestones),
"resource_requirements": roadmap.resource_requirements,
"expected_outcomes": roadmap.expected_outcomes,
},
"capability_gaps": [
{
"gap_id": gap.gap_id,
"category": gap.category.value,
"missing_capability": gap.missing_capability,
"priority": gap.priority.value,
"estimated_effort": gap.estimated_effort,
}
for gap in capability_gaps[:10] # Top 10 gaps
],
"recommendations": [
"Focus on high-priority capability gaps",
"Align initiatives with technology trends",
"Monitor progress against milestones",
"Adjust resource allocation as needed",
],
}
return Either.right(planning_result)
except Exception as e:
error_msg = f"Strategic planning failed: {e}"
self.logger.error(error_msg)
return Either.left(OrchestrationError.strategic_planning_failed(error_msg))
async def coordinate(
self,
tools: list[str],
operation: str = "sync",
parameters: dict[str, Any] | None = None,
) -> Either[OrchestrationError, dict[str, Any]]:
"""Coordinate operation across multiple specific tools."""
try:
coordination_id = create_orchestration_id()
self.logger.info(f"Coordinating {operation} across {len(tools)} tools")
# Validate all tools exist
valid_tools = []
for tool_id in tools:
if tool_id in self.tool_registry.tools:
valid_tools.append(tool_id)
else:
self.logger.warning(f"Tool not found: {tool_id}")
if not valid_tools:
return Either.left(
OrchestrationError.tool_not_found("No valid tools provided"),
)
# Create coordination workflow
steps = []
for tool_id in valid_tools:
step = WorkflowStep(
step_id=create_step_id(),
tool_id=tool_id,
parameters=parameters or {"operation": operation},
parallel_group="coordination_group",
)
steps.append(step)
# Calculate resource requirements
resource_requirements = {}
for tool_id in valid_tools:
tool = self.tool_registry.tools[tool_id]
for resource, amount in tool.resource_requirements.items():
resource_requirements[resource] = (
resource_requirements.get(resource, 0) + amount
)
# Create workflow
workflow = EcosystemWorkflow(
workflow_id=create_workflow_id(),
name=f"Tool Coordination: {operation}",
description=f"Coordinate {operation} across {len(valid_tools)} tools",
steps=steps,
execution_mode=ExecutionMode.PARALLEL,
optimization_target=OptimizationTarget.EFFICIENCY,
expected_duration=120.0,
resource_requirements=resource_requirements,
)
# Execute coordination workflow
execution_result = await self.workflow_engine.execute_workflow(workflow)
if execution_result.is_left():
return execution_result
orchestration_result = execution_result.right()
coordination_result = {
"coordination_id": coordination_id,
"operation": operation,
"tools_coordinated": valid_tools,
"execution_time": orchestration_result.execution_time,
"success": orchestration_result.success,
"performance_metrics": {
"average_response_time": orchestration_result.performance_metrics.average_response_time,
"success_rate": orchestration_result.performance_metrics.success_rate,
"throughput": orchestration_result.performance_metrics.throughput,
},
}
return Either.right(coordination_result)
except Exception as e:
error_msg = f"Coordination failed: {e}"
self.logger.error(error_msg)
return Either.left(OrchestrationError.workflow_execution_failed(error_msg))
async def analyze(self) -> Either[OrchestrationError, dict[str, Any]]:
"""Perform comprehensive ecosystem analysis."""
try:
analysis_result = {
"timestamp": datetime.now(UTC).isoformat(),
"ecosystem_analysis": {},
"performance_analysis": {},
"strategic_analysis": {},
"recommendations": [],
}
# Ecosystem analysis
ecosystem_stats = self.tool_registry.get_ecosystem_statistics()
analysis_result["ecosystem_analysis"] = ecosystem_stats
# Performance analysis
health_report = await self.performance_monitor.get_health_report()
analysis_result["performance_analysis"] = {
"overall_health_score": health_report.overall_health_score,
"performance_level": health_report.performance_level.value,
"active_tools": health_report.active_tools,
"total_executions": health_report.total_executions,
"optimization_recommendations": health_report.optimization_recommendations,
}
# Strategic analysis
current_state = await self.strategic_planner.analyze_current_state()
analysis_result["strategic_analysis"] = current_state
# Generate comprehensive recommendations
recommendations = []
# Performance recommendations
if health_report.overall_health_score < 0.8:
recommendations.append(
"Improve system health - current score below optimal",
)
# Ecosystem recommendations
if ecosystem_stats["enterprise_ready_tools"] < 20:
recommendations.append(
"Increase enterprise-ready tools for production deployment",
)
if ecosystem_stats["ai_enhanced_tools"] < 5:
recommendations.append("Enhance AI integration across more tools")
# Strategic recommendations
maturity = current_state.get("maturity_assessment", {})
if maturity.get("overall_maturity", 0) < 0.7:
recommendations.append("Focus on ecosystem maturity development")
analysis_result["recommendations"] = recommendations
return Either.right(analysis_result)
except Exception as e:
error_msg = f"Analysis failed: {e}"
self.logger.error(error_msg)
return Either.left(OrchestrationError("analysis_error", None, error_msg))
async def get_status(self) -> EcosystemStatus:
"""Get current status of the entire ecosystem."""
try:
# Get performance metrics
current_metrics = await self.performance_monitor.get_current_metrics()
health_report = await self.performance_monitor.get_health_report()
# Get ecosystem statistics
ecosystem_stats = self.tool_registry.get_ecosystem_statistics()
# Get strategic analysis
current_state = await self.strategic_planner.analyze_current_state()
maturity = current_state.get("maturity_assessment", {})
# Determine maturity phase
overall_maturity = maturity.get("overall_maturity", 0.0)
if overall_maturity < 0.3:
maturity_phase = EvolutionPhase.FOUNDATION
elif overall_maturity < 0.5:
maturity_phase = EvolutionPhase.EXPANSION
elif overall_maturity < 0.7:
maturity_phase = EvolutionPhase.INTELLIGENCE
elif overall_maturity < 0.85:
maturity_phase = EvolutionPhase.OPTIMIZATION
else:
maturity_phase = EvolutionPhase.INNOVATION
# Get resource utilization
resource_status = await self.resource_manager.get_resource_status()
resource_utilization = {}
for resource_type, pool_info in resource_status["resource_pools"].items():
resource_utilization[resource_type] = pool_info["utilization_rate"]
status = EcosystemStatus(
timestamp=datetime.now(UTC),
total_tools=ecosystem_stats["total_tools"],
active_workflows=len(self.active_orchestrations),
system_health_score=current_metrics.get_health_score(),
performance_level=health_report.performance_level.value,
resource_utilization=resource_utilization,
maturity_phase=maturity_phase,
active_alerts=len(health_report.active_alerts),
optimization_opportunities=current_metrics.optimization_opportunities,
strategic_recommendations=health_report.optimization_recommendations,
)
return status
except Exception as e:
self.logger.error(f"Failed to get ecosystem status: {e}")
# Return a basic status in case of error
return EcosystemStatus(
timestamp=datetime.now(UTC),
total_tools=len(self.tool_registry.tools),
active_workflows=len(self.active_orchestrations),
system_health_score=0.5,
performance_level="unknown",
resource_utilization={},
maturity_phase=EvolutionPhase.FOUNDATION,
active_alerts=0,
optimization_opportunities=[],
strategic_recommendations=[],
)
async def _cancel_orchestration(self, orchestration_id: OrchestrationId) -> None:
"""Cancel an active orchestration."""
if orchestration_id in self.active_orchestrations:
self.active_orchestrations[orchestration_id]["status"] = "cancelled"
del self.active_orchestrations[orchestration_id]
self.logger.info(f"Cancelled orchestration {orchestration_id}")
# Global ecosystem orchestrator instance
_global_orchestrator: EcosystemOrchestrator | None = None
def get_ecosystem_orchestrator() -> EcosystemOrchestrator:
"""Get or create the global ecosystem orchestrator instance."""
global _global_orchestrator
if _global_orchestrator is None:
_global_orchestrator = EcosystemOrchestrator()
return _global_orchestrator
