"""Ecosystem orchestration type definitions and architecture.
This module provides comprehensive type definitions for orchestrating and managing
the complete 49-tool enterprise automation ecosystem with intelligent coordination,
performance optimization, and strategic automation planning.
Security: All ecosystem operations include safety validation and authorization checks
Performance: <2s orchestration planning, <5s workflow execution, <10s optimization cycles
Enterprise: Complete audit integration, resource optimization, and scalable coordination
"""
import uuid
from dataclasses import dataclass, field
from datetime import UTC, datetime, timedelta
from enum import Enum
from typing import Any
from .contracts import require
# Branded types for ecosystem orchestration
class OrchestrationId(str):
"""Unique identifier for orchestration sessions."""
class WorkflowId(str):
"""Unique identifier for workflow definitions."""
class ToolId(str):
"""Unique identifier for registered tools."""
class OptimizationScore(float):
"""Performance optimization score (0.0 to 1.0)."""
def __new__(cls, value: float):
if not 0.0 <= value <= 1.0:
raise ValueError(
f"OptimizationScore must be between 0.0 and 1.0, got {value}",
)
return super().__new__(cls, value)
class EfficiencyMetric(float):
"""System efficiency metric (0.0 to 1.0)."""
def __new__(cls, value: float):
if not 0.0 <= value <= 1.0:
raise ValueError(
f"EfficiencyMetric must be between 0.0 and 1.0, got {value}",
)
return super().__new__(cls, value)
class ResourceUtilization(float):
"""Resource utilization percentage (0.0 to 100.0)."""
def __new__(cls, value: float):
if not 0.0 <= value <= 100.0:
raise ValueError(
f"ResourceUtilization must be between 0.0 and 100.0, got {value}",
)
return super().__new__(cls, value)
class OrchestrationMode(Enum):
"""Orchestration execution modes."""
SEQUENTIAL = "sequential" # Execute tools in sequence
PARALLEL = "parallel" # Execute compatible tools in parallel
ADAPTIVE = "adaptive" # Adapt execution based on performance
PIPELINE = "pipeline" # Pipeline execution with data flow
INTELLIGENT = "intelligent" # AI-driven intelligent orchestration
class OptimizationTarget(Enum):
"""System optimization targets."""
PERFORMANCE = "performance" # Optimize for speed and throughput
EFFICIENCY = "efficiency" # Optimize for resource efficiency
RELIABILITY = "reliability" # Optimize for stability and error reduction
COST = "cost" # Optimize for cost minimization
USER_EXPERIENCE = "user_experience" # Optimize for user satisfaction
BALANCED = "balanced" # Balanced optimization across all metrics
class ResourceStrategy(Enum):
"""Resource allocation strategies."""
CONSERVATIVE = "conservative" # Conservative resource usage
BALANCED = "balanced" # Balanced resource allocation
AGGRESSIVE = "aggressive" # Aggressive resource utilization
UNLIMITED = "unlimited" # No resource limits (enterprise only)
class MonitoringLevel(Enum):
"""System monitoring levels."""
MINIMAL = "minimal" # Basic health monitoring
STANDARD = "standard" # Standard performance monitoring
DETAILED = "detailed" # Detailed metrics and analytics
COMPREHENSIVE = "comprehensive" # Full ecosystem monitoring
class CacheStrategy(Enum):
"""Caching strategies for performance optimization."""
NONE = "none" # No caching
BASIC = "basic" # Basic result caching
INTELLIGENT = "intelligent" # AI-driven cache optimization
PREDICTIVE = "predictive" # Predictive pre-caching
class ErrorHandling(Enum):
"""Error handling strategies."""
FAIL_FAST = "fail_fast" # Stop on first error
RESILIENT = "resilient" # Continue with error isolation
RECOVERY = "recovery" # Attempt automatic recovery
ADAPTIVE = "adaptive" # Learn from errors and adapt
@dataclass(frozen=True)
class ToolCapability:
"""Capability definition for ecosystem tools."""
tool_id: ToolId
name: str
category: str
operations: list[str]
input_types: list[str]
output_types: list[str]
dependencies: set[ToolId]
resource_requirements: dict[str, float]
performance_metrics: dict[str, float]
compatibility_matrix: dict[ToolId, bool]
@require(lambda self: len(self.tool_id) > 0)
@require(lambda self: len(self.name) > 0)
@require(lambda self: len(self.operations) > 0)
def __post_init__(self):
pass
def is_compatible_with(self, other_tool: ToolId) -> bool:
"""Check compatibility with another tool."""
return self.compatibility_matrix.get(other_tool, True)
def get_resource_score(self) -> float:
"""Calculate resource usage score."""
return sum(self.resource_requirements.values()) / len(
self.resource_requirements,
)
def can_run_parallel_with(self, other: "ToolCapability") -> bool:
"""Check if tool can run in parallel with another tool."""
# Check resource conflicts
for resource, usage in self.resource_requirements.items():
other_usage = other.resource_requirements.get(resource, 0)
if usage + other_usage > 100.0: # Exceeds 100% utilization
return False
# Check dependency conflicts
if other.tool_id in self.dependencies or self.tool_id in other.dependencies:
return False
return self.is_compatible_with(other.tool_id)
@dataclass(frozen=True)
class WorkflowStep:
"""Individual step in an orchestrated workflow."""
step_id: str
tool_id: ToolId
operation: str
parameters: dict[str, Any]
input_mapping: dict[str, str]
output_mapping: dict[str, str]
conditions: dict[str, Any]
timeout: timedelta
retry_count: int = 3
parallel_group: str | None = None
@require(lambda self: len(self.step_id) > 0)
@require(lambda self: len(self.operation) > 0)
@require(lambda self: self.retry_count >= 0)
def __post_init__(self):
pass
def can_execute_parallel_with(self, other: "WorkflowStep") -> bool:
"""Check if step can execute in parallel with another step."""
return (
self.parallel_group is not None
and self.parallel_group == other.parallel_group
)
@dataclass
class WorkflowDefinition:
"""Complete workflow definition for ecosystem orchestration."""
workflow_id: WorkflowId
name: str
description: str
steps: list[WorkflowStep]
global_timeout: timedelta
optimization_target: OptimizationTarget
resource_limits: dict[str, float]
success_criteria: dict[str, Any]
failure_handling: ErrorHandling
created_at: datetime = field(default_factory=lambda: datetime.now(UTC))
@require(lambda self: len(self.workflow_id) > 0)
@require(lambda self: len(self.name) > 0)
@require(lambda self: len(self.steps) > 0)
def __post_init__(self):
pass
def get_parallel_groups(self) -> dict[str, list[WorkflowStep]]:
"""Get steps grouped by parallel execution groups."""
groups = {}
for step in self.steps:
if step.parallel_group:
if step.parallel_group not in groups:
groups[step.parallel_group] = []
groups[step.parallel_group].append(step)
return groups
def estimate_execution_time(
self,
_tool_registry: dict[ToolId, ToolCapability],
) -> timedelta:
"""Estimate workflow execution time."""
sequential_time = 0.0
parallel_groups = self.get_parallel_groups()
for step in self.steps:
if step.parallel_group and step.parallel_group in parallel_groups:
# For parallel steps, take the maximum time in the group
group_steps = parallel_groups[step.parallel_group]
group_time = max(step.timeout.total_seconds() for step in group_steps)
sequential_time += group_time
# Remove processed group to avoid double counting
del parallel_groups[step.parallel_group]
elif not step.parallel_group:
# Sequential step
sequential_time += step.timeout.total_seconds()
return timedelta(seconds=sequential_time)
@dataclass
class OrchestrationContext:
"""Runtime context for ecosystem orchestration."""
orchestration_id: OrchestrationId
workflow_id: WorkflowId
execution_mode: OrchestrationMode
optimization_target: OptimizationTarget
resource_strategy: ResourceStrategy
monitoring_level: MonitoringLevel
cache_strategy: CacheStrategy
error_handling: ErrorHandling
available_resources: dict[str, float]
performance_requirements: dict[str, float]
security_constraints: dict[str, Any]
user_preferences: dict[str, Any]
started_at: datetime = field(default_factory=lambda: datetime.now(UTC))
@require(lambda self: len(self.orchestration_id) > 0)
@require(lambda self: len(self.workflow_id) > 0)
def __post_init__(self):
pass
def has_sufficient_resources(self, required_resources: dict[str, float]) -> bool:
"""Check if sufficient resources are available."""
for resource, required in required_resources.items():
available = self.available_resources.get(resource, 0.0)
if available < required:
return False
return True
def get_execution_priority(self, step: WorkflowStep) -> float:
"""Calculate execution priority for a workflow step."""
# Base priority from optimization target
if self.optimization_target == OptimizationTarget.PERFORMANCE:
return 1.0 - step.timeout.total_seconds() / 3600.0 # Favor faster steps
if self.optimization_target == OptimizationTarget.EFFICIENCY:
# Favor steps with lower resource requirements
return 1.0 - sum(step.parameters.get("resource_cost", {}).values()) / 100.0
if self.optimization_target == OptimizationTarget.RELIABILITY:
return 1.0 - (
step.retry_count / 10.0
) # Favor steps with fewer retries needed
return 0.5 # Balanced priority
@dataclass
class OrchestrationResult:
"""Result of ecosystem orchestration execution."""
orchestration_id: OrchestrationId
workflow_id: WorkflowId
success: bool
steps_executed: int
steps_failed: int
execution_time: timedelta
resource_utilization: dict[str, ResourceUtilization]
performance_metrics: dict[str, EfficiencyMetric]
optimization_score: OptimizationScore
error_details: list[dict[str, Any]]
step_results: dict[str, Any]
recommendations: list[str]
completed_at: datetime = field(default_factory=lambda: datetime.now(UTC))
@require(lambda self: len(self.orchestration_id) > 0)
@require(lambda self: self.steps_executed >= 0)
@require(lambda self: self.steps_failed >= 0)
def __post_init__(self):
pass
def get_success_rate(self) -> float:
"""Calculate overall success rate."""
total_steps = self.steps_executed + self.steps_failed
if total_steps == 0:
return 0.0
return self.steps_executed / total_steps
def get_overall_efficiency(self) -> EfficiencyMetric:
"""Calculate overall system efficiency."""
if not self.performance_metrics:
return EfficiencyMetric(0.0)
efficiency_sum = sum(self.performance_metrics.values())
return EfficiencyMetric(efficiency_sum / len(self.performance_metrics))
@dataclass
class SystemHealth:
"""System-wide health and performance status."""
timestamp: datetime
overall_health: EfficiencyMetric
tool_availability: dict[ToolId, bool]
resource_status: dict[str, ResourceUtilization]
performance_trends: dict[str, list[float]]
active_orchestrations: int
error_rate: float
optimization_opportunities: list[str]
alerts: list[dict[str, Any]]
def is_healthy(self, threshold: float = 0.8) -> bool:
"""Check if system is in healthy state."""
return (
self.overall_health >= threshold
and self.error_rate < 0.05
and all(self.tool_availability.values())
)
def get_critical_issues(self) -> list[str]:
"""Get list of critical system issues."""
issues = []
if self.overall_health < 0.5:
issues.append("Critical: Overall system health below 50%")
if self.error_rate > 0.1:
issues.append(f"Critical: High error rate ({self.error_rate:.1%})")
unavailable_tools = [
tool_id
for tool_id, available in self.tool_availability.items()
if not available
]
if unavailable_tools:
issues.append(
f"Critical: Tools unavailable: {', '.join(unavailable_tools)}",
)
overloaded_resources = [
resource for resource, usage in self.resource_status.items() if usage > 95.0
]
if overloaded_resources:
issues.append(
f"Critical: Resources overloaded: {', '.join(overloaded_resources)}",
)
return issues
class EcosystemOrchestratorError(Exception):
"""Base exception for ecosystem orchestration errors."""
def __init__(self, message: str, error_code: str = "ORCHESTRATION_ERROR"):
self.message = message
self.error_code = error_code
super().__init__(message)
@classmethod
def workflow_not_found(
cls,
workflow_id: WorkflowId,
) -> "EcosystemOrchestratorError":
return cls(f"Workflow not found: {workflow_id}", "WORKFLOW_NOT_FOUND")
@classmethod
def tool_not_available(cls, tool_id: ToolId) -> "EcosystemOrchestratorError":
return cls(f"Tool not available: {tool_id}", "TOOL_NOT_AVAILABLE")
@classmethod
def resource_limit_exceeded(
cls,
resource: str,
required: float,
available: float,
) -> "EcosystemOrchestratorError":
return cls(
f"Resource limit exceeded for {resource}: required {required}, available {available}",
"RESOURCE_LIMIT_EXCEEDED",
)
@classmethod
def orchestration_timeout(cls, timeout: timedelta) -> "EcosystemOrchestratorError":
return cls(f"Orchestration timed out after {timeout}", "ORCHESTRATION_TIMEOUT")
@classmethod
def workflow_validation_failed(cls, details: str) -> "EcosystemOrchestratorError":
return cls(
f"Workflow validation failed: {details}",
"WORKFLOW_VALIDATION_FAILED",
)
@classmethod
def optimization_failed(cls, details: str) -> "EcosystemOrchestratorError":
return cls(f"System optimization failed: {details}", "OPTIMIZATION_FAILED")
def create_orchestration_id() -> OrchestrationId:
"""Create unique orchestration identifier."""
return OrchestrationId(
f"orch_{datetime.now(UTC).strftime('%Y%m%d_%H%M%S')}_{uuid.uuid4().hex[:8]}",
)
def create_workflow_id() -> WorkflowId:
"""Create unique workflow identifier."""
return WorkflowId(
f"wf_{datetime.now(UTC).strftime('%Y%m%d_%H%M%S')}_{uuid.uuid4().hex[:8]}",
)
def create_tool_id(tool_name: str) -> ToolId:
"""Create tool identifier from tool name."""
return ToolId(f"km_{tool_name.lower().replace(' ', '_')}")
# Default configurations
def get_default_orchestration_context(
workflow_id: WorkflowId,
execution_mode: OrchestrationMode = OrchestrationMode.INTELLIGENT,
) -> OrchestrationContext:
"""Get default orchestration context configuration."""
return OrchestrationContext(
orchestration_id=create_orchestration_id(),
workflow_id=workflow_id,
execution_mode=execution_mode,
optimization_target=OptimizationTarget.BALANCED,
resource_strategy=ResourceStrategy.BALANCED,
monitoring_level=MonitoringLevel.COMPREHENSIVE,
cache_strategy=CacheStrategy.INTELLIGENT,
error_handling=ErrorHandling.RESILIENT,
available_resources={
"cpu": 100.0,
"memory": 100.0,
"disk": 100.0,
"network": 100.0,
"api_calls": 1000.0,
"concurrent_operations": 50.0,
},
performance_requirements={
"response_time": 5.0,
"throughput": 100.0,
"reliability": 0.99,
},
security_constraints={
"require_authentication": True,
"audit_all_operations": True,
"encrypt_sensitive_data": True,
},
user_preferences={},
)
