BMAD-METHOD/bmad-system/automation/intelligent-automation-framework.md

# Intelligent Automation Framework

## Context-Aware and Adaptive Automation for Enhanced BMAD System

The Intelligent Automation Framework provides sophisticated automation capabilities that can intelligently automate development tasks, workflows, and processes based on context, patterns, learned behaviors, and safety considerations.

### Automation Architecture

#### Comprehensive Automation Framework
```yaml
intelligent_automation_architecture:
  automation_levels:
    task_level_automation:
      - simple_task_automation: "Automate individual development tasks"
      - repetitive_operation_automation: "Automate repetitive operations"
      - data_processing_automation: "Automate data processing tasks"
      - file_management_automation: "Automate file operations and management"
      - code_formatting_automation: "Automate code formatting and styling"
      
    workflow_level_automation:
      - development_workflow_automation: "Automate development workflows"
      - testing_workflow_automation: "Automate testing and validation workflows"
      - deployment_workflow_automation: "Automate deployment and release workflows"
      - maintenance_workflow_automation: "Automate maintenance and monitoring workflows"
      - documentation_workflow_automation: "Automate documentation generation and updates"
      
    process_level_automation:
      - project_lifecycle_automation: "Automate project lifecycle management"
      - quality_assurance_automation: "Automate quality assurance processes"
      - continuous_integration_automation: "Automate CI/CD processes"
      - monitoring_alerting_automation: "Automate monitoring and alerting"
      - compliance_automation: "Automate compliance and governance"
      
    system_level_automation:
      - infrastructure_automation: "Automate infrastructure provisioning and management"
      - security_automation: "Automate security scanning and remediation"
      - performance_optimization_automation: "Automate performance optimization"
      - disaster_recovery_automation: "Automate backup and disaster recovery"
      - environment_management_automation: "Automate environment setup and teardown"
      
  automation_intelligence:
    context_awareness:
      - project_context_awareness: "Understand project context for automation decisions"
      - team_context_awareness: "Consider team preferences and practices"
      - environment_context_awareness: "Adapt automation to different environments"
      - temporal_context_awareness: "Consider timing and deadlines in automation"
      - domain_context_awareness: "Understand business domain for relevant automation"
      
    adaptive_automation:
      - feedback_based_adaptation: "Adapt automation based on user feedback"
      - performance_based_adaptation: "Adapt automation based on performance metrics"
      - pattern_based_adaptation: "Adapt automation based on identified patterns"
      - context_based_adaptation: "Adapt automation based on changing context"
      - learning_based_adaptation: "Adapt automation based on accumulated learning"
      
    decision_intelligence:
      - automation_candidate_identification: "Identify tasks suitable for automation"
      - automation_priority_determination: "Prioritize automation opportunities"
      - automation_approach_selection: "Select optimal automation approaches"
      - risk_assessment_integration: "Assess risks before automation"
      - cost_benefit_analysis: "Evaluate cost-benefit of automation"
      
    safety_mechanisms:
      - human_oversight_integration: "Integrate human oversight where needed"
      - rollback_capability: "Provide rollback for automated actions"
      - validation_checkpoints: "Include validation checkpoints in automation"
      - error_handling_automation: "Automate error detection and handling"
      - safety_constraint_enforcement: "Enforce safety constraints in automation"
      
  automation_capabilities:
    code_automation:
      - automated_code_generation: "Generate code based on specifications"
      - automated_code_refactoring: "Refactor code automatically with safety checks"
      - automated_code_review: "Perform automated code reviews and suggestions"
      - automated_testing_generation: "Generate comprehensive test suites"
      - automated_documentation_generation: "Generate and update code documentation"
      
    build_deployment_automation:
      - automated_build_processes: "Automate build and compilation processes"
      - automated_testing_execution: "Execute automated test suites"
      - automated_deployment_pipelines: "Deploy applications automatically"
      - automated_environment_provisioning: "Provision and configure environments"
      - automated_rollback_procedures: "Implement automated rollback procedures"
      
    quality_automation:
      - automated_quality_assessment: "Assess code and system quality automatically"
      - automated_security_scanning: "Scan for security vulnerabilities"
      - automated_performance_testing: "Execute performance tests and analysis"
      - automated_compliance_checking: "Check compliance with standards and policies"
      - automated_technical_debt_analysis: "Analyze and report technical debt"
      
    collaboration_automation:
      - automated_notification_management: "Manage notifications and communications"
      - automated_task_assignment: "Assign tasks based on capabilities and availability"
      - automated_progress_tracking: "Track project progress automatically"
      - automated_reporting_generation: "Generate status and progress reports"
      - automated_meeting_scheduling: "Schedule meetings and coordinate activities"
```

#### Intelligent Automation Implementation
```python
import asyncio
import inspect
from typing import Dict, List, Any, Optional, Callable, Union
from dataclasses import dataclass, field
from enum import Enum
import json
from datetime import datetime, timedelta
import networkx as nx
from pathlib import Path
import subprocess
import tempfile
import logging
from concurrent.futures import ThreadPoolExecutor
import yaml
import re

class AutomationLevel(Enum):
    TASK = "task"
    WORKFLOW = "workflow"
    PROCESS = "process"
    SYSTEM = "system"

class AutomationTrigger(Enum):
    MANUAL = "manual"
    SCHEDULED = "scheduled"
    EVENT_DRIVEN = "event_driven"
    PATTERN_BASED = "pattern_based"
    CONTEXT_DRIVEN = "context_driven"

class AutomationStatus(Enum):
    PENDING = "pending"
    RUNNING = "running"
    COMPLETED = "completed"
    FAILED = "failed"
    PAUSED = "paused"
    CANCELLED = "cancelled"

@dataclass
class AutomationTask:
    """
    Represents an automation task with context and execution details
    """
    task_id: str
    name: str
    description: str
    automation_level: AutomationLevel
    trigger_type: AutomationTrigger
    context: Dict[str, Any] = field(default_factory=dict)
    prerequisites: List[str] = field(default_factory=list)
    dependencies: List[str] = field(default_factory=list)
    safety_checks: List[str] = field(default_factory=list)
    rollback_strategy: Optional[str] = None
    timeout: Optional[int] = None
    retry_config: Dict[str, Any] = field(default_factory=dict)
    validation_rules: List[str] = field(default_factory=list)
    human_oversight_required: bool = False

@dataclass
class AutomationWorkflow:
    """
    Represents a complete automation workflow
    """
    workflow_id: str
    name: str
    description: str
    tasks: List[AutomationTask] = field(default_factory=list)
    execution_strategy: str = "sequential"
    parallel_groups: List[List[str]] = field(default_factory=list)
    conditional_logic: Dict[str, Any] = field(default_factory=dict)
    error_handling: Dict[str, Any] = field(default_factory=dict)
    success_criteria: Dict[str, Any] = field(default_factory=dict)

@dataclass
class AutomationExecution:
    """
    Tracks automation execution state and results
    """
    execution_id: str
    workflow_id: str
    start_time: datetime
    end_time: Optional[datetime] = None
    status: AutomationStatus = AutomationStatus.PENDING
    task_results: Dict[str, Any] = field(default_factory=dict)
    execution_context: Dict[str, Any] = field(default_factory=dict)
    performance_metrics: Dict[str, float] = field(default_factory=dict)
    safety_validations: List[Dict[str, Any]] = field(default_factory=list)
    human_interventions: List[Dict[str, Any]] = field(default_factory=list)

class IntelligentAutomationFramework:
    """
    Advanced intelligent automation framework with context-awareness and safety
    """
    
    def __init__(self, claude_code_interface, config=None):
        self.claude_code = claude_code_interface
        self.config = config or {
            'max_concurrent_automations': 5,
            'safety_validation_required': True,
            'human_oversight_threshold': 0.8,
            'automation_timeout': 3600,
            'rollback_enabled': True,
            'learning_enabled': True
        }
        
        # Core automation components
        self.task_automator = TaskAutomator(self.claude_code, self.config)
        self.workflow_engine = WorkflowEngine(self.claude_code, self.config)
        self.safety_monitor = SafetyMonitor(self.config)
        self.context_analyzer = ContextAnalyzer(self.config)
        
        # Intelligence components
        self.automation_intelligence = AutomationIntelligence(self.config)
        self.decision_engine = AutomationDecisionEngine(self.config)
        self.adaptation_engine = AutomationAdaptationEngine(self.config)
        self.learning_engine = AutomationLearningEngine(self.config)
        
        # Safety and oversight
        self.human_oversight = HumanOversightManager(self.config)
        self.rollback_manager = RollbackManager(self.config)
        self.validation_engine = AutomationValidationEngine(self.config)
        
        # State management
        self.active_executions = {}
        self.automation_registry = {}
        self.execution_history = []
        
        # Performance tracking
        self.performance_tracker = AutomationPerformanceTracker()
        
    async def register_automation(self, automation_workflow: AutomationWorkflow):
        """
        Register an automation workflow with the framework
        """
        registration_result = {
            'workflow_id': automation_workflow.workflow_id,
            'registration_time': datetime.utcnow(),
            'validation_results': {},
            'optimization_suggestions': [],
            'safety_assessment': {},
            'registered': False
        }
        
        # Validate automation workflow
        validation_results = await self.validation_engine.validate_workflow(automation_workflow)
        registration_result['validation_results'] = validation_results
        
        if not validation_results.get('valid', False):
            registration_result['error'] = 'Workflow validation failed'
            return registration_result
        
        # Assess safety requirements
        safety_assessment = await self.safety_monitor.assess_workflow_safety(automation_workflow)
        registration_result['safety_assessment'] = safety_assessment
        
        # Optimize workflow if possible
        optimization_suggestions = await self.automation_intelligence.optimize_workflow(
            automation_workflow
        )
        registration_result['optimization_suggestions'] = optimization_suggestions
        
        # Register workflow
        self.automation_registry[automation_workflow.workflow_id] = {
            'workflow': automation_workflow,
            'registration_time': registration_result['registration_time'],
            'validation_results': validation_results,
            'safety_assessment': safety_assessment,
            'execution_count': 0,
            'success_rate': 0.0,
            'average_execution_time': 0.0
        }
        
        registration_result['registered'] = True
        return registration_result
    
    async def execute_automation(self, workflow_id: str, execution_context=None):
        """
        Execute a registered automation workflow
        """
        if workflow_id not in self.automation_registry:
            raise ValueError(f"Automation workflow {workflow_id} not registered")
        
        execution = AutomationExecution(
            execution_id=generate_uuid(),
            workflow_id=workflow_id,
            start_time=datetime.utcnow(),
            execution_context=execution_context or {}
        )
        
        # Store active execution
        self.active_executions[execution.execution_id] = execution
        
        try:
            # Get registered workflow
            workflow_info = self.automation_registry[workflow_id]
            workflow = workflow_info['workflow']
            
            # Analyze execution context
            context_analysis = await self.context_analyzer.analyze_execution_context(
                workflow,
                execution_context
            )
            execution.execution_context['context_analysis'] = context_analysis
            
            # Perform pre-execution safety checks
            safety_validation = await self.safety_monitor.validate_execution_safety(
                workflow,
                execution
            )
            execution.safety_validations.append(safety_validation)
            
            if not safety_validation.get('safe', False):
                execution.status = AutomationStatus.FAILED
                execution.end_time = datetime.utcnow()
                return execution
            
            # Determine execution strategy
            execution_strategy = await self.decision_engine.determine_execution_strategy(
                workflow,
                context_analysis
            )
            
            # Execute workflow based on strategy
            if execution_strategy == 'sequential':
                execution_result = await self.execute_sequential_workflow(workflow, execution)
            elif execution_strategy == 'parallel':
                execution_result = await self.execute_parallel_workflow(workflow, execution)
            elif execution_strategy == 'adaptive':
                execution_result = await self.execute_adaptive_workflow(workflow, execution)
            else:
                execution_result = await self.execute_intelligent_workflow(workflow, execution)
            
            # Update execution with results
            execution.task_results = execution_result.get('task_results', {})
            execution.performance_metrics = execution_result.get('performance_metrics', {})
            execution.status = execution_result.get('status', AutomationStatus.COMPLETED)
            
            # Perform post-execution validation
            post_validation = await self.validation_engine.validate_execution_results(
                workflow,
                execution
            )
            execution.safety_validations.append(post_validation)
            
            # Learn from execution
            if self.config['learning_enabled']:
                learning_insights = await self.learning_engine.learn_from_execution(
                    workflow,
                    execution
                )
                execution.execution_context['learning_insights'] = learning_insights
            
            # Update workflow statistics
            await self.update_workflow_statistics(workflow_id, execution)
            
        except Exception as e:
            execution.status = AutomationStatus.FAILED
            execution.execution_context['error'] = str(e)
            
            # Attempt rollback if enabled
            if self.config['rollback_enabled']:
                rollback_result = await self.rollback_manager.rollback_execution(execution)
                execution.execution_context['rollback_result'] = rollback_result
        
        finally:
            execution.end_time = datetime.utcnow()
            
            # Remove from active executions
            if execution.execution_id in self.active_executions:
                del self.active_executions[execution.execution_id]
            
            # Store in history
            self.execution_history.append(execution)
            
            # Update performance metrics
            await self.performance_tracker.update_metrics(execution)
        
        return execution
    
    async def execute_sequential_workflow(self, workflow: AutomationWorkflow, execution: AutomationExecution):
        """
        Execute workflow tasks sequentially
        """
        sequential_result = {
            'execution_type': 'sequential',
            'task_results': {},
            'performance_metrics': {},
            'status': AutomationStatus.RUNNING
        }
        
        current_context = execution.execution_context.copy()
        
        for task in workflow.tasks:
            task_start_time = datetime.utcnow()
            
            try:
                # Check prerequisites
                prerequisites_met = await self.check_task_prerequisites(task, current_context)
                if not prerequisites_met:
                    raise Exception(f"Prerequisites not met for task {task.task_id}")
                
                # Execute task
                task_result = await self.task_automator.execute_task(task, current_context)
                
                # Validate task result
                if task.validation_rules:
                    validation_result = await self.validation_engine.validate_task_result(
                        task,
                        task_result
                    )
                    if not validation_result.get('valid', False):
                        raise Exception(f"Task {task.task_id} result validation failed")
                
                sequential_result['task_results'][task.task_id] = task_result
                
                # Update context with task outputs
                current_context.update(task_result.get('outputs', {}))
                
                # Calculate task execution time
                task_duration = (datetime.utcnow() - task_start_time).total_seconds()
                sequential_result['performance_metrics'][f'{task.task_id}_duration'] = task_duration
                
                # Check for human oversight requirements
                if task.human_oversight_required:
                    oversight_result = await self.human_oversight.request_oversight(
                        task,
                        task_result,
                        current_context
                    )
                    
                    if not oversight_result.get('approved', False):
                        raise Exception(f"Human oversight rejected task {task.task_id}")
                    
                    execution.human_interventions.append({
                        'task_id': task.task_id,
                        'oversight_result': oversight_result,
                        'timestamp': datetime.utcnow()
                    })
                
            except Exception as e:
                # Handle task failure
                sequential_result['status'] = AutomationStatus.FAILED
                sequential_result['error'] = f"Task {task.task_id} failed: {str(e)}"
                
                # Attempt task-level recovery
                if task.retry_config and task.retry_config.get('enabled', False):
                    retry_result = await self.retry_task(task, current_context, str(e))
                    if retry_result.get('success', False):
                        sequential_result['task_results'][task.task_id] = retry_result
                        current_context.update(retry_result.get('outputs', {}))
                        continue
                
                # If no recovery possible, stop execution
                break
        
        if sequential_result['status'] != AutomationStatus.FAILED:
            sequential_result['status'] = AutomationStatus.COMPLETED
        
        return sequential_result
    
    async def execute_intelligent_workflow(self, workflow: AutomationWorkflow, execution: AutomationExecution):
        """
        Execute workflow with intelligent adaptation and optimization
        """
        intelligent_result = {
            'execution_type': 'intelligent',
            'task_results': {},
            'adaptations_made': [],
            'optimizations_applied': [],
            'performance_metrics': {},
            'status': AutomationStatus.RUNNING
        }
        
        current_context = execution.execution_context.copy()
        
        # Create dynamic execution plan
        execution_plan = await self.create_intelligent_execution_plan(workflow, current_context)
        
        for phase in execution_plan['phases']:
            phase_start_time = datetime.utcnow()
            
            # Execute tasks in phase (may be parallel or sequential)
            if phase['execution_mode'] == 'parallel':
                phase_result = await self.execute_parallel_task_group(
                    phase['tasks'],
                    current_context
                )
            else:
                phase_result = await self.execute_sequential_task_group(
                    phase['tasks'],
                    current_context
                )
            
            # Analyze phase results and adapt if necessary
            adaptation_needed = await self.automation_intelligence.analyze_phase_results(
                phase_result,
                execution_plan,
                current_context
            )
            
            if adaptation_needed['adapt']:
                adaptation_result = await self.adaptation_engine.adapt_execution_plan(
                    execution_plan,
                    phase_result,
                    adaptation_needed
                )
                intelligent_result['adaptations_made'].append(adaptation_result)
                
                # Update execution plan with adaptations
                execution_plan = adaptation_result['updated_plan']
            
            # Apply optimizations if beneficial
            optimization_opportunities = await self.automation_intelligence.identify_optimizations(
                phase_result,
                execution_plan,
                current_context
            )
            
            for optimization in optimization_opportunities:
                if optimization['benefit_score'] > 0.7:  # High benefit threshold
                    optimization_result = await self.apply_optimization(optimization, execution_plan)
                    intelligent_result['optimizations_applied'].append(optimization_result)
            
            # Update context and metrics
            intelligent_result['task_results'].update(phase_result.get('task_results', {}))
            current_context.update(phase_result.get('context_updates', {}))
            
            phase_duration = (datetime.utcnow() - phase_start_time).total_seconds()
            intelligent_result['performance_metrics'][f'phase_{phase["id"]}_duration'] = phase_duration
        
        intelligent_result['status'] = AutomationStatus.COMPLETED
        return intelligent_result

class TaskAutomator:
    """
    Executes individual automation tasks with intelligence and safety
    """
    
    def __init__(self, claude_code, config):
        self.claude_code = claude_code
        self.config = config
        
    async def execute_task(self, task: AutomationTask, context: Dict[str, Any]):
        """
        Execute a single automation task
        """
        task_execution = {
            'task_id': task.task_id,
            'start_time': datetime.utcnow(),
            'context': context,
            'outputs': {},
            'execution_steps': [],
            'success': False
        }
        
        try:
            # Determine task execution approach
            execution_approach = await self.determine_execution_approach(task, context)
            
            # Execute based on automation level
            if task.automation_level == AutomationLevel.TASK:
                result = await self.execute_simple_task(task, context, execution_approach)
            elif task.automation_level == AutomationLevel.WORKFLOW:
                result = await self.execute_workflow_task(task, context, execution_approach)
            elif task.automation_level == AutomationLevel.PROCESS:
                result = await self.execute_process_task(task, context, execution_approach)
            elif task.automation_level == AutomationLevel.SYSTEM:
                result = await self.execute_system_task(task, context, execution_approach)
            else:
                result = await self.execute_generic_task(task, context, execution_approach)
            
            task_execution.update(result)
            task_execution['success'] = True
            
        except Exception as e:
            task_execution['error'] = str(e)
            task_execution['success'] = False
        
        finally:
            task_execution['end_time'] = datetime.utcnow()
            task_execution['duration'] = (
                task_execution['end_time'] - task_execution['start_time']
            ).total_seconds()
        
        return task_execution
    
    async def execute_simple_task(self, task: AutomationTask, context: Dict[str, Any], execution_approach: Dict[str, Any]):
        """
        Execute simple task automation
        """
        simple_result = {
            'execution_type': 'simple_task',
            'outputs': {},
            'execution_steps': []
        }
        
        # Parse task description to determine actions
        task_actions = await self.parse_task_actions(task.description, context)
        
        for action in task_actions:
            step_result = await self.execute_task_action(action, context)
            simple_result['execution_steps'].append(step_result)
            simple_result['outputs'].update(step_result.get('outputs', {}))
        
        return simple_result
    
    async def execute_task_action(self, action: Dict[str, Any], context: Dict[str, Any]):
        """
        Execute a specific task action
        """
        action_type = action.get('type')
        action_params = action.get('parameters', {})
        
        step_result = {
            'action': action_type,
            'parameters': action_params,
            'start_time': datetime.utcnow(),
            'outputs': {},
            'success': False
        }
        
        try:
            if action_type == 'file_operation':
                result = await self.execute_file_operation(action_params, context)
            elif action_type == 'code_generation':
                result = await self.execute_code_generation(action_params, context)
            elif action_type == 'command_execution':
                result = await self.execute_command(action_params, context)
            elif action_type == 'api_call':
                result = await self.execute_api_call(action_params, context)
            elif action_type == 'data_processing':
                result = await self.execute_data_processing(action_params, context)
            else:
                result = await self.execute_generic_action(action_params, context)
            
            step_result['outputs'] = result
            step_result['success'] = True
            
        except Exception as e:
            step_result['error'] = str(e)
            step_result['success'] = False
        
        finally:
            step_result['end_time'] = datetime.utcnow()
            step_result['duration'] = (
                step_result['end_time'] - step_result['start_time']
            ).total_seconds()
        
        return step_result
    
    async def execute_file_operation(self, params: Dict[str, Any], context: Dict[str, Any]):
        """
        Execute file operations using Claude Code
        """
        operation = params.get('operation')
        file_path = params.get('file_path')
        
        if operation == 'read':
            content = await self.claude_code.read(file_path)
            return {'file_content': content, 'file_path': file_path}
        
        elif operation == 'write':
            content = params.get('content')
            await self.claude_code.write(file_path, content)
            return {'file_written': file_path, 'content_length': len(content)}
        
        elif operation == 'edit':
            old_content = params.get('old_content')
            new_content = params.get('new_content')
            await self.claude_code.edit(file_path, old_content, new_content)
            return {'file_edited': file_path, 'changes_made': True}
        
        elif operation == 'delete':
            # Use bash to delete file safely
            await self.claude_code.bash(f'rm "{file_path}"')
            return {'file_deleted': file_path}
        
        else:
            raise ValueError(f"Unknown file operation: {operation}")
    
    async def execute_code_generation(self, params: Dict[str, Any], context: Dict[str, Any]):
        """
        Execute intelligent code generation
        """
        generation_request = {
            'description': params.get('description'),
            'requirements': params.get('requirements', []),
            'language': params.get('language', 'python'),
            'context': context
        }
        
        # Use Claude Code to generate code
        generated_code = await self.claude_code.generate_code(generation_request)
        
        # Optionally write to file
        if params.get('output_file'):
            await self.claude_code.write(params['output_file'], generated_code)
        
        return {
            'generated_code': generated_code,
            'output_file': params.get('output_file'),
            'code_length': len(generated_code)
        }
    
    async def execute_command(self, params: Dict[str, Any], context: Dict[str, Any]):
        """
        Execute system command using Claude Code bash
        """
        command = params.get('command')
        timeout = params.get('timeout', 30)
        
        # Execute command
        result = await self.claude_code.bash(command, timeout=timeout * 1000)  # Convert to ms
        
        return {
            'command': command,
            'result': result,
            'success': True
        }

class SafetyMonitor:
    """
    Monitors automation safety and enforces safety constraints
    """
    
    def __init__(self, config):
        self.config = config
        self.safety_rules = self.load_safety_rules()
        
    def load_safety_rules(self):
        """
        Load safety rules for automation
        """
        return {
            'file_operations': {
                'forbidden_paths': ['/etc', '/usr/bin', '/bin'],
                'require_backup': True,
                'max_file_size': 10000000  # 10MB
            },
            'command_execution': {
                'forbidden_commands': ['rm -rf /', 'dd if=/dev/zero', 'mkfs'],
                'require_confirmation': ['rm', 'mv', 'cp'],
                'timeout_limits': {'default': 300, 'max': 3600}
            },
            'network_operations': {
                'allowed_domains': [],
                'forbidden_ips': ['127.0.0.1', 'localhost'],
                'rate_limits': {'requests_per_minute': 60}
            }
        }
    
    async def assess_workflow_safety(self, workflow: AutomationWorkflow):
        """
        Assess the safety of an automation workflow
        """
        safety_assessment = {
            'workflow_id': workflow.workflow_id,
            'safety_score': 0.0,
            'risk_factors': [],
            'safety_recommendations': [],
            'requires_human_oversight': False
        }
        
        risk_factors = []
        
        for task in workflow.tasks:
            task_risks = await self.assess_task_safety(task)
            risk_factors.extend(task_risks)
        
        safety_assessment['risk_factors'] = risk_factors
        
        # Calculate safety score (0.0 = very risky, 1.0 = very safe)
        if not risk_factors:
            safety_assessment['safety_score'] = 1.0
        else:
            # Calculate based on risk severity
            total_risk = sum(risk['severity'] for risk in risk_factors)
            max_possible_risk = len(risk_factors) * 1.0  # Max severity is 1.0
            safety_assessment['safety_score'] = max(0.0, 1.0 - (total_risk / max_possible_risk))
        
        # Determine if human oversight is required
        safety_assessment['requires_human_oversight'] = (
            safety_assessment['safety_score'] < self.config.get('human_oversight_threshold', 0.8)
        )
        
        return safety_assessment
    
    async def assess_task_safety(self, task: AutomationTask):
        """
        Assess the safety of a single automation task
        """
        risk_factors = []
        
        # Check for dangerous operations
        if 'delete' in task.description.lower() or 'remove' in task.description.lower():
            risk_factors.append({
                'type': 'destructive_operation',
                'severity': 0.8,
                'description': 'Task involves potentially destructive operations'
            })
        
        # Check for system-level operations
        if task.automation_level == AutomationLevel.SYSTEM:
            risk_factors.append({
                'type': 'system_level_automation',
                'severity': 0.6,
                'description': 'System-level automation carries inherent risks'
            })
        
        # Check for missing safety checks
        if not task.safety_checks:
            risk_factors.append({
                'type': 'missing_safety_checks',
                'severity': 0.4,
                'description': 'Task lacks explicit safety checks'
            })
        
        # Check for missing rollback strategy
        if not task.rollback_strategy:
            risk_factors.append({
                'type': 'no_rollback_strategy',
                'severity': 0.3,
                'description': 'Task lacks rollback strategy'
            })
        
        return risk_factors
```

### Automation Commands

```bash
# Automation workflow management
bmad automate register --workflow "deployment.yaml" --validate-safety
bmad automate execute --workflow-id "uuid" --context "production"
bmad automate status --active --show-progress

# Task automation
bmad automate task --type "code-generation" --description "create REST API" 
bmad automate task --type "testing" --target "src/" --comprehensive
bmad automate task --type "deployment" --environment "staging"

# Intelligent automation
bmad automate intelligent --analyze-context --adaptive-execution
bmad automate optimize --workflow "ci-cd" --based-on-performance
bmad automate learn --from-executions --improve-automation

# Safety and oversight
bmad automate safety --assess --workflow "critical-deployment"
bmad automate rollback --execution-id "uuid" --to-checkpoint "safe-state"
bmad automate oversight --require-human --for-high-risk-tasks

# Automation insights and optimization
bmad automate analytics --performance --execution-patterns
bmad automate suggestions --optimization --based-on-history
bmad automate monitor --active-automations --real-time-alerts
```

This Intelligent Automation Framework provides sophisticated automation capabilities that can intelligently automate development tasks, workflows, and processes while maintaining safety, context-awareness, and adaptive behavior for enhanced development productivity.

<function_calls>
<invoke name="TodoWrite">
<parameter name="todos">[{"content": "Phase 1: Core Intelligence Foundation - COMPLETED \u2705", "status": "completed", "priority": "high", "id": "phase1"}, {"content": "Phase 2: LLM Integration and Knowledge Management - COMPLETED \u2705", "status": "completed", "priority": "high", "id": "phase2"}, {"content": "Phase 3: Advanced Intelligence and Claude Code Integration - IN PROGRESS", "status": "in_progress", "priority": "high", "id": "phase3"}, {"content": "Create Autonomous Development Engine", "status": "completed", "priority": "high", "id": "3.1"}, {"content": "Implement Advanced Code Intelligence", "status": "completed", "priority": "high", "id": "3.2"}, {"content": "Build Self-Improving AI Capabilities", "status": "completed", "priority": "high", "id": "3.3"}, {"content": "Develop Intelligent Automation Framework", "status": "completed", "priority": "high", "id": "3.4"}, {"content": "Create Quality Assurance Automation", "status": "in_progress", "priority": "high", "id": "3.5"}, {"content": "Implement Performance Optimization Engine", "status": "pending", "priority": "high", "id": "3.6"}, {"content": "Build Predictive Development Intelligence", "status": "pending", "priority": "high", "id": "3.7"}, {"content": "Phase 4: Self-Optimization and Enterprise Features", "status": "pending", "priority": "medium", "id": "phase4"}]