"""Core workflow analysis engine for intelligent workflow processing and optimization.
This module provides comprehensive workflow analysis including pattern recognition,
performance prediction, quality assessment, and optimization recommendations.
Security: Enterprise-grade workflow analysis with privacy compliance and secure processing.
Performance: <500ms analysis time, optimized algorithms, efficient pattern matching.
Type Safety: Complete workflow analysis framework with contract-driven development.
"""
import logging
import statistics
import uuid
from collections import defaultdict
from dataclasses import dataclass
from datetime import UTC, datetime, timedelta
from typing import Any
from ..core.constants import DEFAULT_RETRY_COUNT
from ..core.contracts import ensure, require
from ..core.either import Either
from ..core.errors import ValidationError
from ..core.workflow_intelligence import (
IntelligenceLevel,
OptimizationGoal,
OptimizationImpact,
OptimizationRecommendation,
PatternType,
WorkflowAnalysisResult,
WorkflowComplexity,
WorkflowComponent,
WorkflowPattern,
calculate_workflow_complexity_score,
create_analysis_session_id,
create_optimization_id,
create_pattern_id,
create_recommendation_id,
estimate_workflow_execution_time,
)
@dataclass
class AnalysisMetrics:
"""Metrics collected during workflow analysis."""
total_components: int
unique_component_types: int
dependency_depth: int
cyclic_dependencies: bool
resource_conflicts: list[str]
performance_bottlenecks: list[str]
reliability_concerns: list[str]
class WorkflowAnalyzer:
"""Core workflow analysis engine with AI-powered insights.
Provides comprehensive workflow analysis including complexity assessment,
performance prediction, pattern recognition, and optimization recommendations.
"""
def __init__(self, intelligence_config: dict[str, Any] | None = None):
self.config = intelligence_config or {}
self.logger = logging.getLogger(__name__)
# Analysis history for pattern learning
self.analysis_history: dict[str, WorkflowAnalysisResult] = {}
self.pattern_library: dict[str, WorkflowPattern] = {}
self.optimization_templates: dict[OptimizationGoal, list[dict[str, Any]]] = (
defaultdict(list)
)
# Performance benchmarks
self.performance_benchmarks = {
"simple_workflow_ms": 100,
"intermediate_workflow_ms": 500,
"advanced_workflow_ms": 2000,
"expert_workflow_ms": 5000,
}
# Quality thresholds
self.quality_thresholds = {
"excellent": 0.9,
"good": 0.75,
"fair": 0.6,
"poor": 0.4,
}
# Analysis statistics
self.analysis_stats = {
"total_analyses": 0,
"patterns_identified": 0,
"optimizations_suggested": 0,
"avg_analysis_time_ms": 0.0,
"quality_score_average": 0.0,
}
# Initialize common patterns
self._initialize_pattern_library()
def _analyze_performance(self, workflow_data: dict[str, Any]) -> dict[str, Any]:
"""Analyze workflow performance and return performance metrics.
Args:
workflow_data: Workflow data containing steps and duration information
Returns:
Dictionary containing performance analysis results including efficiency metrics
"""
try:
# Extract performance data
steps = workflow_data.get("steps", [])
total_duration = workflow_data.get("total_duration", 0)
if not steps:
return {"efficiency": 0.0, "analysis": "No steps found in workflow"}
# Calculate performance metrics
step_count = len(steps)
avg_step_duration = total_duration / step_count if step_count > 0 else 0
# Analyze step efficiency
step_durations = []
for step in steps:
duration = step.get("duration", 0)
step_durations.append(duration)
# Calculate efficiency score (0.0 to 1.0)
if step_durations:
max_duration = max(step_durations)
min_duration = min(step_durations)
duration_variance = (
max_duration - min_duration if max_duration > 0 else 0
)
# Efficiency is higher when durations are more consistent and reasonable
if max_duration > 0:
consistency_score = 1.0 - (duration_variance / max_duration)
speed_score = (
min(1.0, 10.0 / avg_step_duration)
if avg_step_duration > 0
else 0.0
)
efficiency = (consistency_score + speed_score) / 2.0
else:
efficiency = 0.5 # Default for zero duration
else:
efficiency = 0.0
# Generate performance insights
insights = []
if efficiency > 0.8:
insights.append("Workflow shows excellent performance characteristics")
elif efficiency > 0.6:
insights.append(
"Workflow has good performance with minor optimization potential"
)
else:
insights.append("Workflow could benefit from performance optimization")
# Identify bottlenecks
bottlenecks = []
if step_durations:
avg_duration = statistics.mean(step_durations)
for i, step in enumerate(steps):
duration = step.get("duration", 0)
if duration > avg_duration * 1.5: # 50% above average
bottlenecks.append(
f"Step {i + 1}: {step.get('name', 'unnamed')} (duration: {duration})"
)
return {
"efficiency": efficiency,
"step_count": step_count,
"total_duration": total_duration,
"average_step_duration": avg_step_duration,
"bottlenecks": bottlenecks,
"insights": insights,
"performance_score": efficiency * 100, # Convert to percentage
"analysis": f"Analyzed {step_count} steps with {efficiency:.2f} efficiency rating",
}
except Exception as e:
self.logger.error(f"Error in performance analysis: {e}")
return {
"efficiency": 0.0,
"error": str(e),
"analysis": "Performance analysis failed",
}
def _initialize_pattern_library(self) -> None:
"""Initialize common workflow patterns for recognition."""
# Sequential processing pattern
sequential_pattern = WorkflowPattern(
pattern_id=create_pattern_id(PatternType.EFFICIENCY),
pattern_type=PatternType.EFFICIENCY,
name="Sequential Processing",
description="Linear sequence of operations without parallelization opportunities",
components=[],
usage_frequency=0.7,
effectiveness_score=0.6,
complexity_reduction=0.2,
reusability_score=0.8,
detected_in_workflows=[],
template_generated=False,
confidence_score=0.9,
)
self.pattern_library["sequential_processing"] = sequential_pattern
# Error handling pattern
error_handling_pattern = WorkflowPattern(
pattern_id=create_pattern_id(PatternType.BEST_PRACTICE),
pattern_type=PatternType.BEST_PRACTICE,
name="Comprehensive Error Handling",
description="Well-structured error handling with fallback mechanisms",
components=[],
usage_frequency=0.3,
effectiveness_score=0.95,
complexity_reduction=0.0,
reusability_score=0.9,
detected_in_workflows=[],
template_generated=True,
confidence_score=0.95,
)
self.pattern_library["error_handling"] = error_handling_pattern
@require(lambda __self, workflow_data: workflow_data is not None)
@ensure(lambda result: isinstance(result, Either))
async def analyze_workflow(
self,
workflow_data: dict[str, Any],
analysis_depth: IntelligenceLevel = IntelligenceLevel.ML_ENHANCED,
optimization_goals: list[OptimizationGoal] | None = None,
) -> Either[ValidationError, WorkflowAnalysisResult]:
"""Perform comprehensive workflow analysis."""
start_time = datetime.now(UTC)
analysis_id = create_analysis_session_id()
try:
# Extract workflow components
components = self._extract_workflow_components(workflow_data)
if not components:
return Either.left(
ValidationError("workflow_data", "no valid components found"),
)
# Collect analysis metrics
metrics = await self._collect_analysis_metrics(components)
# Perform quality assessment
quality_score = await self._assess_workflow_quality(components, metrics)
# Analyze complexity
complexity_analysis = await self._analyze_complexity(components, metrics)
# Predict performance
performance_prediction = await self._predict_performance(
components,
metrics,
)
# Identify patterns
identified_patterns = await self._identify_patterns(
components,
workflow_data,
)
# Detect anti-patterns
anti_patterns = await self._detect_anti_patterns(components, metrics)
# Generate optimization recommendations
optimizations = await self._generate_optimizations(
components,
metrics,
optimization_goals or [OptimizationGoal.EFFICIENCY],
)
# Analyze cross-tool dependencies
cross_tool_deps = self._analyze_cross_tool_dependencies(components)
# Assess resource requirements
resource_requirements = await self._assess_resource_requirements(components)
# Calculate reliability assessment
reliability_assessment = self._assess_reliability(components, metrics)
# Calculate maintainability score
maintainability_score = self._calculate_maintainability(components, metrics)
# Generate improvement suggestions
improvement_suggestions = await self._generate_improvement_suggestions(
components,
metrics,
identified_patterns,
anti_patterns,
)
# Generate alternative designs
alternative_designs = await self._generate_alternative_designs(
components,
optimization_goals or [OptimizationGoal.EFFICIENCY],
)
# Create analysis result
result = WorkflowAnalysisResult(
analysis_id=analysis_id,
workflow_id=workflow_data.get(
"workflow_id",
f"workflow_{uuid.uuid4().hex[:8]}",
),
analysis_depth=analysis_depth,
quality_score=quality_score,
complexity_analysis=complexity_analysis,
performance_prediction=performance_prediction,
optimization_opportunities=optimizations,
identified_patterns=identified_patterns,
anti_patterns_detected=anti_patterns,
cross_tool_dependencies=cross_tool_deps,
resource_requirements=resource_requirements,
reliability_assessment=reliability_assessment,
maintainability_score=maintainability_score,
improvement_suggestions=improvement_suggestions,
alternative_designs=alternative_designs,
)
# Store in analysis history
self.analysis_history[analysis_id] = result
# Update statistics
analysis_time = (datetime.now(UTC) - start_time).total_seconds() * 1000
self._update_analysis_stats(
analysis_time,
quality_score,
len(identified_patterns),
len(optimizations),
)
self.logger.info(
"Workflow analysis completed",
extra={
"analysis_id": analysis_id,
"quality_score": quality_score,
"patterns_found": len(identified_patterns),
"optimizations": len(optimizations),
"analysis_time_ms": analysis_time,
},
)
return Either.right(result)
except Exception as e:
self.logger.error(f"Workflow analysis failed: {e}")
return Either.left(
ValidationError("workflow_analysis", str(e), "analysis failed"),
)
def _extract_workflow_components(
self,
workflow_data: dict[str, Any],
) -> list[WorkflowComponent]:
"""Extract workflow components from workflow data."""
components = []
# Extract from different workflow formats
if "components" in workflow_data:
# Direct component format
for comp_data in workflow_data["components"]:
component = self._create_component_from_data(comp_data)
if component:
components.append(component)
elif "actions" in workflow_data:
# Action-based format
for i, action_data in enumerate(workflow_data["actions"]):
component = WorkflowComponent(
component_id=action_data.get("id", f"action_{i}"),
component_type="action",
name=action_data.get("name", f"Action {i + 1}"),
description=action_data.get("description", "Workflow action"),
parameters=action_data.get("parameters", {}),
dependencies=action_data.get("dependencies", []),
estimated_execution_time=timedelta(
milliseconds=action_data.get("execution_time_ms", 500),
),
reliability_score=action_data.get("reliability", 0.9),
complexity_score=action_data.get("complexity", 0.3),
)
components.append(component)
return components
def _create_component_from_data(
self,
comp_data: dict[str, Any],
) -> WorkflowComponent | None:
"""Create a WorkflowComponent from component data dictionary."""
try:
return WorkflowComponent(
component_id=comp_data.get(
"component_id",
f"comp_{uuid.uuid4().hex[:8]}",
),
component_type=comp_data.get("component_type", "action"),
name=comp_data.get("name", "Component"),
description=comp_data.get("description", "Workflow component"),
parameters=comp_data.get("parameters", {}),
dependencies=comp_data.get("dependencies", []),
estimated_execution_time=timedelta(
milliseconds=comp_data.get("execution_time_ms", 500),
),
reliability_score=comp_data.get("reliability_score", 0.9),
complexity_score=comp_data.get("complexity_score", 0.3),
)
except Exception as e:
self.logger.warning(f"Failed to create component from data: {e}")
return None
async def _collect_analysis_metrics(
self,
components: list[WorkflowComponent],
) -> AnalysisMetrics:
"""Collect metrics for workflow analysis."""
# Component analysis
total_components = len(components)
component_types = {comp.component_type for comp in components}
unique_component_types = len(component_types)
# Dependency analysis
dependency_graph = defaultdict(list)
for comp in components:
for dep in comp.dependencies:
dependency_graph[comp.component_id].append(dep)
dependency_depth = self._calculate_dependency_depth(dependency_graph)
cyclic_dependencies = self._detect_cyclic_dependencies(dependency_graph)
# Resource conflict analysis
resource_conflicts = self._detect_resource_conflicts(components)
# Performance bottleneck analysis
performance_bottlenecks = self._identify_performance_bottlenecks(components)
# Reliability concerns
reliability_concerns = self._identify_reliability_concerns(components)
return AnalysisMetrics(
total_components=total_components,
unique_component_types=unique_component_types,
dependency_depth=dependency_depth,
cyclic_dependencies=cyclic_dependencies,
resource_conflicts=resource_conflicts,
performance_bottlenecks=performance_bottlenecks,
reliability_concerns=reliability_concerns,
)
def _calculate_dependency_depth(
self,
dependency_graph: dict[str, list[str]],
) -> int:
"""Calculate the maximum dependency depth in the workflow."""
def dfs_depth(node: str, visited: set[str]) -> int:
if node in visited:
return 0 # Avoid infinite recursion
visited.add(node)
max_depth = 0
for dep in dependency_graph.get(node, []):
depth = dfs_depth(dep, visited.copy())
max_depth = max(max_depth, depth + 1)
return max_depth
max_depth = 0
for node in dependency_graph:
depth = dfs_depth(node, set())
max_depth = max(max_depth, depth)
return max_depth
def _detect_cyclic_dependencies(
self,
dependency_graph: dict[str, list[str]],
) -> bool:
"""Detect cyclic dependencies in the workflow."""
def has_cycle(node: str, visited: set[str], rec_stack: set[str]) -> bool:
visited.add(node)
rec_stack.add(node)
for neighbor in dependency_graph.get(node, []):
if neighbor not in visited:
if has_cycle(neighbor, visited, rec_stack):
return True
elif neighbor in rec_stack:
return True
rec_stack.remove(node)
return False
visited = set()
for node in dependency_graph:
if node not in visited and has_cycle(node, visited, set()):
return True
return False
def _detect_resource_conflicts(
self,
components: list[WorkflowComponent],
) -> list[str]:
"""Detect potential resource conflicts between components."""
conflicts = []
# Check for file system conflicts
file_resources = defaultdict(list)
for comp in components:
file_paths = comp.parameters.get(
"file_path",
comp.parameters.get("file_paths", []),
)
if isinstance(file_paths, str):
file_paths = [file_paths]
for path in file_paths:
file_resources[path].append(comp.component_id)
for path, comp_ids in file_resources.items():
if len(comp_ids) > 1:
conflicts.append(
f"File resource conflict: {path} used by {', '.join(comp_ids)}",
)
# Check for application conflicts
app_resources = defaultdict(list)
for comp in components:
apps = comp.parameters.get(
"application",
comp.parameters.get("applications", []),
)
if isinstance(apps, str):
apps = [apps]
for app in apps:
app_resources[app].append(comp.component_id)
for app, comp_ids in app_resources.items():
if len(comp_ids) > 1:
conflicts.append(
f"Application resource conflict: {app} used by {', '.join(comp_ids)}",
)
return conflicts
def _identify_performance_bottlenecks(
self,
components: list[WorkflowComponent],
) -> list[str]:
"""Identify potential performance bottlenecks."""
bottlenecks = []
# Find components with long execution times
execution_times = [
comp.estimated_execution_time.total_seconds() for comp in components
]
if execution_times:
avg_time = statistics.mean(execution_times)
for comp in components:
if comp.estimated_execution_time.total_seconds() > avg_time * 3:
bottlenecks.append(
f"Slow component: {comp.name} ({comp.estimated_execution_time.total_seconds():.2f}s)",
)
# Check for sequential operations that could be parallelized
sequential_actions = [
comp
for comp in components
if comp.component_type == "action" and not comp.dependencies
]
if len(sequential_actions) > DEFAULT_RETRY_COUNT:
bottlenecks.append(
f"Potential parallelization opportunity: {len(sequential_actions)} independent actions",
)
return bottlenecks
def _identify_reliability_concerns(
self,
components: list[WorkflowComponent],
) -> list[str]:
"""Identify reliability concerns in the workflow."""
concerns = []
# Find components with low reliability scores
for comp in components:
if comp.reliability_score < 0.8:
concerns.append(
f"Low reliability component: {comp.name} ({comp.reliability_score:.2f})",
)
# Check for missing error handling
condition_components = [
comp for comp in components if comp.component_type == "condition"
]
if len(condition_components) == 0 and len(components) > DEFAULT_RETRY_COUNT:
concerns.append("No error handling or conditional logic detected")
return concerns
async def _assess_workflow_quality(
self,
components: list[WorkflowComponent],
metrics: AnalysisMetrics,
) -> float:
"""Assess overall workflow quality score."""
quality_factors = []
# Component reliability factor
if components:
avg_reliability = statistics.mean(
[comp.reliability_score for comp in components],
)
quality_factors.append(avg_reliability)
# Complexity factor (lower complexity = higher quality for simple workflows)
avg_complexity = calculate_workflow_complexity_score(components)
complexity_factor = 1.0 - (
avg_complexity * 0.3
) # Penalize excessive complexity
quality_factors.append(max(0.0, complexity_factor))
# Dependency factor (clean dependencies = higher quality)
dependency_factor = 1.0
if metrics.cyclic_dependencies:
dependency_factor -= 0.3
if metrics.dependency_depth > 5:
dependency_factor -= 0.2
quality_factors.append(max(0.0, dependency_factor))
# Resource conflict factor
conflict_factor = 1.0 - (len(metrics.resource_conflicts) * 0.1)
quality_factors.append(max(0.0, conflict_factor))
# Performance factor
performance_factor = 1.0 - (len(metrics.performance_bottlenecks) * 0.15)
quality_factors.append(max(0.0, performance_factor))
# Calculate weighted average
weights = [0.3, 0.2, 0.2, 0.15, 0.15] # Reliability is most important
weighted_score = sum(
factor * weight
for factor, weight in zip(quality_factors, weights, strict=False)
)
return round(min(1.0, max(0.0, weighted_score)), 2)
async def _analyze_complexity(
self,
components: list[WorkflowComponent],
metrics: AnalysisMetrics,
) -> dict[str, Any]:
"""Analyze workflow complexity in detail."""
complexity_score = calculate_workflow_complexity_score(components)
# Categorize complexity
if complexity_score < 0.3:
complexity_level = WorkflowComplexity.SIMPLE
elif complexity_score < 0.6:
complexity_level = WorkflowComplexity.INTERMEDIATE
elif complexity_score < 0.8:
complexity_level = WorkflowComplexity.ADVANCED
else:
complexity_level = WorkflowComplexity.EXPERT
return {
"overall_score": complexity_score,
"complexity_level": complexity_level.value,
"component_count": metrics.total_components,
"component_types": metrics.unique_component_types,
"dependency_depth": metrics.dependency_depth,
"has_cycles": metrics.cyclic_dependencies,
"complexity_contributors": [
f"Component count: {metrics.total_components}",
f"Dependency depth: {metrics.dependency_depth}",
f"Component type diversity: {metrics.unique_component_types}",
],
}
async def _predict_performance(
self,
components: list[WorkflowComponent],
_metrics: AnalysisMetrics,
) -> dict[str, float]:
"""Predict workflow performance characteristics."""
# Estimate execution time
estimated_time = estimate_workflow_execution_time(components)
# Calculate throughput (workflows per hour)
if estimated_time.total_seconds() > 0:
throughput = 3600 / estimated_time.total_seconds()
else:
throughput = 3600 # Very fast workflow
# Estimate resource usage
cpu_estimate = (
sum(comp.complexity_score for comp in components) * 0.1
) # Simplified
memory_estimate = len(components) * 10 # MB estimate
# Predict success rate
if components:
avg_reliability = statistics.mean(
[comp.reliability_score for comp in components],
)
# Success rate decreases with more components
success_rate = avg_reliability * (0.99 ** len(components))
else:
success_rate = 1.0
return {
"estimated_execution_time_seconds": estimated_time.total_seconds(),
"estimated_throughput_per_hour": throughput,
"estimated_cpu_usage_percent": min(100.0, cpu_estimate),
"estimated_memory_usage_mb": memory_estimate,
"predicted_success_rate": success_rate,
"scalability_factor": max(0.1, 1.0 - (len(components) * 0.05)),
}
async def _identify_patterns(
self,
components: list[WorkflowComponent],
_workflow_data: dict[str, Any],
) -> list[WorkflowPattern]:
"""Identify workflow patterns using pattern recognition."""
identified_patterns = []
# Check for sequential processing pattern
if self._matches_sequential_pattern(components):
pattern = self.pattern_library["sequential_processing"]
identified_patterns.append(pattern)
# Check for error handling pattern
if self._matches_error_handling_pattern(components):
pattern = self.pattern_library["error_handling"]
identified_patterns.append(pattern)
# Identify custom patterns
custom_patterns = await self._discover_custom_patterns(components)
identified_patterns.extend(custom_patterns)
return identified_patterns
def _matches_sequential_pattern(self, components: list[WorkflowComponent]) -> bool:
"""Check if workflow matches sequential processing pattern."""
if len(components) < DEFAULT_RETRY_COUNT:
return False
# Check if most components have no dependencies (sequential)
independent_count = sum(1 for comp in components if not comp.dependencies)
return independent_count >= len(components) * 0.7
def _matches_error_handling_pattern(
self,
components: list[WorkflowComponent],
) -> bool:
"""Check if workflow has good error handling pattern."""
condition_count = sum(
1 for comp in components if comp.component_type == "condition"
)
action_count = sum(1 for comp in components if comp.component_type == "action")
# Good error handling has at least 1 condition per 3-4 actions
if action_count > 0:
condition_ratio = condition_count / action_count
return condition_ratio >= 0.25
return False
async def _discover_custom_patterns(
self,
components: list[WorkflowComponent],
) -> list[WorkflowPattern]:
"""Discover custom patterns in the workflow."""
custom_patterns = []
# Pattern: File processing workflow
file_operations = [
comp
for comp in components
if "file" in comp.name.lower() or "file_path" in comp.parameters
]
if len(file_operations) >= 2:
pattern = WorkflowPattern(
pattern_id=create_pattern_id(PatternType.EFFICIENCY),
pattern_type=PatternType.EFFICIENCY,
name="File Processing Workflow",
description="Workflow focused on file operations and processing",
components=file_operations,
usage_frequency=0.4,
effectiveness_score=0.8,
complexity_reduction=0.1,
reusability_score=0.7,
detected_in_workflows=[],
template_generated=False,
confidence_score=0.8,
)
custom_patterns.append(pattern)
return custom_patterns
async def _detect_anti_patterns(
self,
components: list[WorkflowComponent],
metrics: AnalysisMetrics,
) -> list[WorkflowPattern]:
"""Detect anti-patterns in the workflow."""
anti_patterns = []
# Anti-pattern: Overly complex single component
for comp in components:
if comp.complexity_score > 0.8 and len(comp.parameters) > 10:
anti_pattern = WorkflowPattern(
pattern_id=create_pattern_id(PatternType.ANTI_PATTERN),
pattern_type=PatternType.ANTI_PATTERN,
name="Overly Complex Component",
description=f"Component '{comp.name}' is overly complex and should be split",
components=[comp],
usage_frequency=0.1,
effectiveness_score=0.3,
complexity_reduction=-0.3,
reusability_score=0.2,
detected_in_workflows=[],
template_generated=False,
confidence_score=0.9,
)
anti_patterns.append(anti_pattern)
# Anti-pattern: No error handling
if len(metrics.reliability_concerns) > 0 and "error handling" in str(
metrics.reliability_concerns,
):
anti_pattern = WorkflowPattern(
pattern_id=create_pattern_id(PatternType.ANTI_PATTERN),
pattern_type=PatternType.ANTI_PATTERN,
name="Missing Error Handling",
description="Workflow lacks adequate error handling mechanisms",
components=[],
usage_frequency=0.3,
effectiveness_score=0.4,
complexity_reduction=0.0,
reusability_score=0.3,
detected_in_workflows=[],
template_generated=False,
confidence_score=0.85,
)
anti_patterns.append(anti_pattern)
return anti_patterns
async def _generate_optimizations(
self,
components: list[WorkflowComponent],
metrics: AnalysisMetrics,
optimization_goals: list[OptimizationGoal],
) -> list[OptimizationRecommendation]:
"""Generate optimization recommendations."""
optimizations = []
for goal in optimization_goals:
if goal == OptimizationGoal.PERFORMANCE:
perf_optimizations = await self._generate_performance_optimizations(
components,
metrics,
)
optimizations.extend(perf_optimizations)
elif goal == OptimizationGoal.EFFICIENCY:
efficiency_optimizations = (
await self._generate_efficiency_optimizations(components, metrics)
)
optimizations.extend(efficiency_optimizations)
elif goal == OptimizationGoal.RELIABILITY:
reliability_optimizations = (
await self._generate_reliability_optimizations(components, metrics)
)
optimizations.extend(reliability_optimizations)
return optimizations
async def _generate_performance_optimizations(
self,
components: list[WorkflowComponent],
_metrics: AnalysisMetrics,
) -> list[OptimizationRecommendation]:
"""Generate performance-focused optimizations."""
optimizations = []
# Parallelization opportunity
independent_actions = [
comp
for comp in components
if comp.component_type == "action" and not comp.dependencies
]
if len(independent_actions) > 2:
optimization_id = create_optimization_id(OptimizationGoal.PERFORMANCE)
recommendation = OptimizationRecommendation(
recommendation_id=create_recommendation_id(optimization_id),
optimization_id=optimization_id,
title="Parallelize Independent Actions",
description=f"Execute {len(independent_actions)} independent actions in parallel",
optimization_goals=[OptimizationGoal.PERFORMANCE],
impact_level=OptimizationImpact.MEDIUM,
implementation_effort=WorkflowComplexity.INTERMEDIATE,
expected_improvement={"execution_time": -50.0, "throughput": 100.0},
before_components=independent_actions,
after_components=[], # Would be parallelized
implementation_steps=[
"Group independent actions into parallel execution block",
"Implement synchronization point after parallel execution",
"Test parallel execution for race conditions",
],
risks_and_considerations=[
"Potential resource contention",
"Increased complexity",
"Requires parallel execution support",
],
confidence_score=0.8,
)
optimizations.append(recommendation)
return optimizations
async def _generate_efficiency_optimizations(
self,
components: list[WorkflowComponent],
_metrics: AnalysisMetrics,
) -> list[OptimizationRecommendation]:
"""Generate efficiency-focused optimizations."""
optimizations = []
# Component consolidation
similar_components = self._find_similar_components(components)
if len(similar_components) > 1:
optimization_id = create_optimization_id(OptimizationGoal.EFFICIENCY)
recommendation = OptimizationRecommendation(
recommendation_id=create_recommendation_id(optimization_id),
optimization_id=optimization_id,
title="Consolidate Similar Components",
description=f"Merge {len(similar_components)} similar components to reduce redundancy",
optimization_goals=[OptimizationGoal.EFFICIENCY],
impact_level=OptimizationImpact.LOW,
implementation_effort=WorkflowComplexity.SIMPLE,
expected_improvement={
"component_count": -30.0,
"maintainability": 20.0,
},
before_components=similar_components,
after_components=[], # Would be consolidated
implementation_steps=[
"Identify common functionality",
"Create consolidated component",
"Update dependencies and references",
],
risks_and_considerations=[
"May reduce flexibility",
"Requires careful testing",
],
confidence_score=0.7,
)
optimizations.append(recommendation)
return optimizations
async def _generate_reliability_optimizations(
self,
_components: list[WorkflowComponent],
metrics: AnalysisMetrics,
) -> list[OptimizationRecommendation]:
"""Generate reliability-focused optimizations."""
optimizations = []
# Add error handling
if "No error handling" in str(metrics.reliability_concerns):
optimization_id = create_optimization_id(OptimizationGoal.RELIABILITY)
recommendation = OptimizationRecommendation(
recommendation_id=create_recommendation_id(optimization_id),
optimization_id=optimization_id,
title="Add Comprehensive Error Handling",
description="Implement error handling and fallback mechanisms",
optimization_goals=[OptimizationGoal.RELIABILITY],
impact_level=OptimizationImpact.HIGH,
implementation_effort=WorkflowComplexity.INTERMEDIATE,
expected_improvement={"reliability": 40.0, "success_rate": 25.0},
before_components=[],
after_components=[], # Would add new error handling components
implementation_steps=[
"Add try-catch blocks around critical operations",
"Implement fallback mechanisms",
"Add validation for inputs and outputs",
"Create error notification system",
],
risks_and_considerations=[
"Increased complexity",
"Potential performance overhead",
"May mask underlying issues",
],
confidence_score=0.9,
)
optimizations.append(recommendation)
return optimizations
def _find_similar_components(
self,
components: list[WorkflowComponent],
) -> list[WorkflowComponent]:
"""Find similar components that could be consolidated."""
similar_components = []
# Group by component type and name similarity
component_groups = defaultdict(list)
for comp in components:
key = f"{comp.component_type}_{comp.name.lower()[:10]}"
component_groups[key].append(comp)
# Find groups with multiple similar components
for group in component_groups.values():
if len(group) > 1:
similar_components.extend(group)
break # Return first group found
return similar_components
def _analyze_cross_tool_dependencies(
self,
components: list[WorkflowComponent],
) -> dict[str, list[str]]:
"""Analyze dependencies across different tools."""
tool_dependencies = defaultdict(set)
for comp in components:
tool_name = comp.parameters.get("tool", "unknown")
for dep in comp.dependencies:
dep_tool = next(
(
c.parameters.get("tool", "unknown")
for c in components
if c.component_id == dep
),
"unknown",
)
if dep_tool != tool_name:
tool_dependencies[tool_name].add(dep_tool)
return {tool: list(deps) for tool, deps in tool_dependencies.items()}
async def _assess_resource_requirements(
self,
components: list[WorkflowComponent],
) -> dict[str, Any]:
"""Assess resource requirements for the workflow."""
# Estimate computational requirements
cpu_requirement = (
sum(comp.complexity_score for comp in components) * 10
) # Percentage
memory_requirement = len(components) * 5 # MB per component
# Estimate network requirements
network_components = [
comp
for comp in components
if "url" in comp.parameters or "api" in comp.name.lower()
]
network_requirement = len(network_components) * 1 # MB per network operation
# Estimate storage requirements
file_components = [
comp for comp in components if "file_path" in comp.parameters
]
storage_requirement = len(file_components) * 10 # MB per file operation
return {
"cpu_percentage": min(100.0, cpu_requirement),
"memory_mb": memory_requirement,
"network_mb": network_requirement,
"storage_mb": storage_requirement,
"parallel_execution_capable": len(
[c for c in components if not c.dependencies],
)
> 1,
"external_dependencies": len(
[c for c in components if "url" in str(c.parameters)],
),
}
def _assess_reliability(
self,
components: list[WorkflowComponent],
metrics: AnalysisMetrics,
) -> dict[str, float]:
"""Assess workflow reliability characteristics."""
if not components:
return {"overall": 0.0}
# Overall reliability based on component reliability
component_reliability = statistics.mean(
[comp.reliability_score for comp in components],
)
# Adjust for workflow structure
structure_penalty = 0.0
if metrics.cyclic_dependencies:
structure_penalty += 0.2
if len(metrics.resource_conflicts) > 0:
structure_penalty += 0.1
if len(metrics.reliability_concerns) > 0:
structure_penalty += 0.15
overall_reliability = max(0.0, component_reliability - structure_penalty)
return {
"overall": overall_reliability,
"component_average": component_reliability,
"structure_penalty": structure_penalty,
"error_handling_score": 1.0
if "error handling" not in str(metrics.reliability_concerns)
else 0.5,
}
def _calculate_maintainability(
self,
components: list[WorkflowComponent],
metrics: AnalysisMetrics,
) -> float:
"""Calculate workflow maintainability score."""
maintainability_factors = []
# Complexity factor (lower complexity = higher maintainability)
complexity_score = calculate_workflow_complexity_score(components)
complexity_factor = 1.0 - complexity_score
maintainability_factors.append(complexity_factor)
# Component organization factor
organization_factor = 1.0
if metrics.dependency_depth > DEFAULT_RETRY_COUNT:
organization_factor -= 0.2
if metrics.cyclic_dependencies:
organization_factor -= 0.3
maintainability_factors.append(max(0.0, organization_factor))
# Documentation factor (based on description quality)
doc_factor = (
0.8 if any(len(comp.description) > 20 for comp in components) else 0.5
)
maintainability_factors.append(doc_factor)
# Modularity factor
modularity_factor = min(
1.0,
metrics.unique_component_types / max(1, metrics.total_components),
)
maintainability_factors.append(modularity_factor)
# Calculate weighted average
weights = [0.3, 0.3, 0.2, 0.2]
weighted_score = sum(
factor * weight
for factor, weight in zip(maintainability_factors, weights, strict=False)
)
return round(min(1.0, max(0.0, weighted_score)), 2)
async def _generate_improvement_suggestions(
self,
components: list[WorkflowComponent],
metrics: AnalysisMetrics,
_patterns: list[WorkflowPattern],
anti_patterns: list[WorkflowPattern],
) -> list[str]:
"""Generate improvement suggestions based on analysis."""
suggestions = []
# Suggestions based on quality issues
if len(metrics.resource_conflicts) > 0:
suggestions.append(
"Resolve resource conflicts to prevent workflow failures",
)
if len(metrics.performance_bottlenecks) > 0:
suggestions.append("Optimize identified performance bottlenecks")
if metrics.cyclic_dependencies:
suggestions.append(
"Resolve cyclic dependencies to improve workflow stability",
)
# Suggestions based on anti-patterns
for anti_pattern in anti_patterns:
if anti_pattern.pattern_type == PatternType.ANTI_PATTERN:
suggestions.append(f"Address anti-pattern: {anti_pattern.name}")
# Suggestions based on complexity
complexity_score = calculate_workflow_complexity_score(components)
if complexity_score > 0.8:
suggestions.append(
"Consider breaking down complex workflow into smaller, manageable parts",
)
# Suggestions based on reliability
low_reliability_components = [
comp for comp in components if comp.reliability_score < 0.8
]
if low_reliability_components:
suggestions.append(
f"Improve reliability of {len(low_reliability_components)} components",
)
# Default suggestion if no issues found
if not suggestions:
suggestions.append(
"Workflow appears well-structured. Consider adding monitoring and logging.",
)
return suggestions[:5] # Limit to top 5 suggestions
async def _generate_alternative_designs(
self,
components: list[WorkflowComponent],
optimization_goals: list[OptimizationGoal],
) -> list[dict[str, Any]]:
"""Generate alternative workflow designs."""
alternatives = []
# Alternative 1: Simplified version
essential_components = [
comp for comp in components if comp.complexity_score < 0.5
]
if len(essential_components) < len(components):
alternatives.append(
{
"name": "Simplified Workflow",
"description": "Streamlined version focusing on essential operations",
"component_count": len(essential_components),
"estimated_improvement": {
"complexity": -30.0,
"maintainability": 25.0,
},
"trade_offs": ["Reduced functionality", "Improved simplicity"],
},
)
# Alternative 2: Performance-optimized version
if OptimizationGoal.PERFORMANCE in optimization_goals:
alternatives.append(
{
"name": "Performance-Optimized Workflow",
"description": "Version optimized for maximum execution speed",
"component_count": len(components),
"estimated_improvement": {
"execution_time": -40.0,
"resource_usage": 20.0,
},
"trade_offs": ["Increased complexity", "Higher resource usage"],
},
)
# Alternative 3: Reliability-focused version
if OptimizationGoal.RELIABILITY in optimization_goals:
alternatives.append(
{
"name": "High-Reliability Workflow",
"description": "Version with comprehensive error handling and validation",
"component_count": len(components)
+ 2, # Add error handling components
"estimated_improvement": {
"reliability": 35.0,
"error_recovery": 50.0,
},
"trade_offs": ["Increased complexity", "Longer execution time"],
},
)
return alternatives
def _update_analysis_stats(
self,
analysis_time: float,
quality_score: float,
patterns_found: int,
optimizations_count: int,
) -> Any:
"""Update analysis performance statistics."""
self.analysis_stats["total_analyses"] += 1
self.analysis_stats["patterns_identified"] += patterns_found
self.analysis_stats["optimizations_suggested"] += optimizations_count
# Update average analysis time
current_avg = self.analysis_stats["avg_analysis_time_ms"]
total_analyses = self.analysis_stats["total_analyses"]
new_avg = (current_avg * (total_analyses - 1) + analysis_time) / total_analyses
self.analysis_stats["avg_analysis_time_ms"] = new_avg
# Update average quality score
current_avg_quality = self.analysis_stats["quality_score_average"]
new_avg_quality = (
current_avg_quality * (total_analyses - 1) + quality_score
) / total_analyses
self.analysis_stats["quality_score_average"] = new_avg_quality
async def get_analysis_statistics(self) -> dict[str, Any]:
"""Get workflow analysis performance statistics."""
return {
"total_analyses_performed": self.analysis_stats["total_analyses"],
"patterns_identified": self.analysis_stats["patterns_identified"],
"optimizations_suggested": self.analysis_stats["optimizations_suggested"],
"average_analysis_time_ms": self.analysis_stats["avg_analysis_time_ms"],
"average_quality_score": self.analysis_stats["quality_score_average"],
"pattern_library_size": len(self.pattern_library),
"analysis_history_size": len(self.analysis_history),
"last_updated": datetime.now(UTC).isoformat(),
}
