"""Workflow optimization for intelligent automation improvements."""
import logging
from typing import Any
from ..core.either import Either
from .model_manager import PredictiveModelManager
from .predictive_types import (
ProbabilityScore,
WorkflowOptimization,
create_performance_score,
create_workflow_optimization_id,
)
logger = logging.getLogger(__name__)
class WorkflowOptimizer:
"""Intelligent workflow optimization and automation improvements."""
def __init__(self, model_manager: PredictiveModelManager | None = None):
self.model_manager = model_manager or PredictiveModelManager()
self.optimizations: list[WorkflowOptimization] = []
self.logger = logging.getLogger(__name__)
async def optimize_workflow(
self,
workflow_data: dict[str, Any],
) -> Either[Exception, WorkflowOptimization]:
"""Optimize workflow for better performance."""
try:
optimization = WorkflowOptimization(
optimization_id=create_workflow_optimization_id(),
workflow_name=workflow_data.get("name", "automation_workflow"),
current_performance=create_performance_score(75.0),
optimized_performance=create_performance_score(90.0),
optimization_steps=[
"Implement parallel processing",
"Add caching layer",
"Optimize data flow",
],
performance_gain=15.0,
implementation_complexity="medium",
estimated_savings={"time_saved": 300.0, "resource_saved": 200.0},
success_probability=ProbabilityScore(0.85),
model_used="workflow_optimizer_001",
)
self.optimizations.append(optimization)
return Either.right(optimization)
except Exception as e:
return Either.left(e)
