BMAD-METHOD/bmad-system/autonomous-development/autonomous-development-engine.md

# Autonomous Development Engine

## Intelligent Autonomous Development for Enhanced BMAD System

The Autonomous Development Engine enables the BMAD system to independently complete complex development tasks, from requirements analysis to implementation, testing, and deployment, while learning and improving its capabilities over time.

### Autonomous Development Architecture

#### Self-Directed Development Framework
```yaml
autonomous_development_architecture:
  autonomy_levels:
    guided_autonomy:
      - task_decomposition: "Break complex tasks into manageable subtasks"
      - context_understanding: "Deep understanding of project context and goals"
      - decision_making: "Make informed decisions based on patterns and knowledge"
      - progress_tracking: "Monitor progress and adjust approach as needed"
      
    collaborative_autonomy:
      - human_ai_collaboration: "Seamless collaboration between human and AI"
      - clarification_seeking: "Ask for clarification when ambiguous"
      - expertise_consultation: "Consult specialized knowledge when needed"
      - review_integration: "Incorporate human feedback and reviews"
      
    supervised_autonomy:
      - autonomous_execution: "Execute tasks independently with oversight"
      - quality_validation: "Self-validate work quality before submission"
      - error_detection: "Detect and correct errors autonomously"
      - performance_optimization: "Optimize approach based on results"
      
    full_autonomy:
      - end_to_end_delivery: "Complete entire features or modules independently"
      - architectural_decisions: "Make architectural and design decisions"
      - cross_system_integration: "Integrate across multiple systems and services"
      - innovation_application: "Apply innovative solutions and patterns"
      
  autonomous_capabilities:
    requirement_analysis:
      - stakeholder_intent_understanding: "Understand true stakeholder needs"
      - requirement_refinement: "Refine and clarify ambiguous requirements"
      - constraint_identification: "Identify technical and business constraints"
      - acceptance_criteria_generation: "Generate comprehensive acceptance criteria"
      
    architecture_design:
      - system_architecture_design: "Design scalable system architectures"
      - pattern_application: "Apply appropriate architectural patterns"
      - technology_selection: "Select optimal technologies for requirements"
      - integration_strategy: "Design integration strategies across systems"
      
    implementation_execution:
      - code_generation: "Generate high-quality, maintainable code"
      - algorithm_implementation: "Implement complex algorithms efficiently"
      - api_development: "Design and implement robust APIs"
      - database_design: "Design optimal database schemas and queries"
      
    testing_automation:
      - test_strategy_design: "Design comprehensive testing strategies"
      - test_case_generation: "Generate thorough test cases automatically"
      - test_automation: "Implement automated testing frameworks"
      - quality_assurance: "Ensure code quality through automated checks"
      
    deployment_orchestration:
      - deployment_strategy: "Design deployment strategies and pipelines"
      - infrastructure_provisioning: "Provision and configure infrastructure"
      - monitoring_setup: "Set up monitoring and alerting systems"
      - performance_optimization: "Optimize for performance and scalability"
      
  learning_mechanisms:
    outcome_based_learning:
      - success_pattern_extraction: "Learn from successful implementations"
      - failure_analysis: "Analyze and learn from failures"
      - performance_correlation: "Correlate approaches with performance outcomes"
      - quality_feedback_integration: "Learn from quality feedback and reviews"
      
    adaptive_improvement:
      - approach_refinement: "Refine approaches based on experience"
      - efficiency_optimization: "Optimize for development efficiency"
      - quality_enhancement: "Continuously improve code quality"
      - innovation_integration: "Integrate new techniques and patterns"
      
    meta_learning:
      - learning_strategy_optimization: "Optimize how the system learns"
      - knowledge_transfer: "Transfer knowledge across domains and projects"
      - expertise_development: "Develop specialized expertise areas"
      - capability_expansion: "Expand capabilities through experience"
```

#### Autonomous Development Engine Implementation
```python
import asyncio
import networkx as nx
from typing import Dict, List, Any, Optional, Tuple, Union
from dataclasses import dataclass, field
from enum import Enum
import json
from datetime import datetime, timedelta
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
import torch
import torch.nn as nn

class AutonomyLevel(Enum):
    GUIDED = "guided"
    COLLABORATIVE = "collaborative" 
    SUPERVISED = "supervised"
    FULL = "full"

class TaskComplexity(Enum):
    SIMPLE = "simple"
    MODERATE = "moderate"
    COMPLEX = "complex"
    EXPERT = "expert"

@dataclass
class DevelopmentTask:
    """
    Represents a development task for autonomous execution
    """
    id: str
    title: str
    description: str
    requirements: List[str] = field(default_factory=list)
    constraints: List[str] = field(default_factory=list)
    acceptance_criteria: List[str] = field(default_factory=list)
    complexity: TaskComplexity = TaskComplexity.MODERATE
    estimated_effort: Optional[float] = None
    dependencies: List[str] = field(default_factory=list)
    context: Dict[str, Any] = field(default_factory=dict)
    
@dataclass
class AutonomousExecutionPlan:
    """
    Represents an execution plan for autonomous development
    """
    task_id: str
    autonomy_level: AutonomyLevel
    execution_steps: List[Dict[str, Any]] = field(default_factory=list)
    resource_requirements: Dict[str, Any] = field(default_factory=dict)
    quality_checkpoints: List[Dict[str, Any]] = field(default_factory=list)
    fallback_strategies: List[Dict[str, Any]] = field(default_factory=list)
    success_criteria: Dict[str, Any] = field(default_factory=dict)

class AutonomousDevelopmentEngine:
    """
    Advanced autonomous development engine for independent task execution
    """
    
    def __init__(self, claude_code_interface, config=None):
        self.claude_code = claude_code_interface
        self.config = config or {
            'default_autonomy_level': AutonomyLevel.COLLABORATIVE,
            'max_execution_time': 3600,  # 1 hour
            'quality_threshold': 0.85,
            'learning_rate': 0.01,
            'confidence_threshold': 0.8
        }
        
        # Core autonomous capabilities
        self.task_analyzer = AutonomousTaskAnalyzer(self.config)
        self.execution_planner = AutonomousExecutionPlanner(self.config)
        self.code_generator = AutonomousCodeGenerator(self.claude_code, self.config)
        self.quality_assessor = AutonomousQualityAssessor(self.config)
        self.learning_engine = AutonomousLearningEngine(self.config)
        
        # Specialized autonomous modules
        self.requirement_analyzer = AutonomousRequirementAnalyzer(self.config)
        self.architecture_designer = AutonomousArchitectureDesigner(self.config)
        self.test_generator = AutonomousTestGenerator(self.claude_code, self.config)
        self.deployment_orchestrator = AutonomousDeploymentOrchestrator(self.config)
        
        # Autonomous decision making
        self.decision_engine = AutonomousDecisionEngine(self.config)
        self.context_manager = AutonomousContextManager(self.config)
        
        # Performance tracking
        self.execution_history = []
        self.performance_metrics = AutonomousPerformanceTracker()
        
    async def execute_autonomous_development(self, task, execution_context=None):
        """
        Execute autonomous development task with full intelligence
        """
        execution_session = {
            'session_id': generate_uuid(),
            'task': task,
            'start_time': datetime.utcnow(),
            'execution_context': execution_context or {},
            'autonomy_decisions': [],
            'execution_steps': [],
            'quality_assessments': [],
            'learning_outcomes': {},
            'final_deliverables': {}
        }
        
        try:
            # Phase 1: Deep Task Analysis
            task_analysis = await self.task_analyzer.analyze_task_comprehensively(
                task, 
                execution_context
            )
            execution_session['task_analysis'] = task_analysis
            
            # Phase 2: Determine Optimal Autonomy Level
            autonomy_decision = await self.determine_optimal_autonomy_level(
                task,
                task_analysis,
                execution_context
            )
            execution_session['autonomy_level'] = autonomy_decision['level']
            execution_session['autonomy_decisions'].append(autonomy_decision)
            
            # Phase 3: Create Autonomous Execution Plan
            execution_plan = await self.execution_planner.create_autonomous_plan(
                task,
                task_analysis,
                autonomy_decision['level'],
                execution_context
            )
            execution_session['execution_plan'] = execution_plan
            
            # Phase 4: Execute Plan with Autonomous Intelligence
            execution_result = await self.execute_autonomous_plan(
                execution_plan,
                execution_session
            )
            execution_session.update(execution_result)
            
            # Phase 5: Autonomous Quality Validation
            quality_validation = await self.quality_assessor.validate_autonomous_output(
                execution_session,
                task.acceptance_criteria
            )
            execution_session['quality_validation'] = quality_validation
            
            # Phase 6: Learn from Execution
            learning_outcomes = await self.learning_engine.learn_from_execution(
                execution_session
            )
            execution_session['learning_outcomes'] = learning_outcomes
            
            execution_session['status'] = 'completed'
            execution_session['success'] = quality_validation.get('passed', False)
            
        except Exception as e:
            execution_session['status'] = 'failed'
            execution_session['error'] = str(e)
            execution_session['success'] = False
            
            # Learn from failure
            failure_learning = await self.learning_engine.learn_from_failure(
                execution_session,
                str(e)
            )
            execution_session['failure_learning'] = failure_learning
            
        finally:
            execution_session['end_time'] = datetime.utcnow()
            execution_session['total_duration'] = (
                execution_session['end_time'] - execution_session['start_time']
            ).total_seconds()
            
            # Store execution history
            self.execution_history.append(execution_session)
            
            # Update performance metrics
            await self.performance_metrics.update_from_execution(execution_session)
        
        return execution_session
    
    async def determine_optimal_autonomy_level(self, task, task_analysis, context):
        """
        Determine the optimal autonomy level for task execution
        """
        autonomy_factors = {
            'task_complexity': task_analysis.get('complexity_score', 0.5),
            'context_clarity': task_analysis.get('clarity_score', 0.5),
            'domain_expertise': await self.assess_domain_expertise(task, context),
            'risk_level': task_analysis.get('risk_score', 0.5),
            'precedent_availability': await self.assess_precedent_availability(task),
            'stakeholder_preference': context.get('autonomy_preference', 0.5)
        }
        
        # Calculate autonomy score
        weights = {
            'task_complexity': -0.3,  # Higher complexity reduces autonomy
            'context_clarity': 0.25,   # Higher clarity increases autonomy
            'domain_expertise': 0.3,  # Higher expertise increases autonomy
            'risk_level': -0.25,       # Higher risk reduces autonomy
            'precedent_availability': 0.15,  # More precedents increase autonomy
            'stakeholder_preference': 0.05   # Stakeholder preference influence
        }
        
        autonomy_score = sum(
            autonomy_factors[factor] * weights[factor] 
            for factor in autonomy_factors
        )
        
        # Normalize to 0-1 range
        autonomy_score = max(0, min(1, autonomy_score + 0.5))
        
        # Determine autonomy level
        if autonomy_score >= 0.8:
            autonomy_level = AutonomyLevel.FULL
        elif autonomy_score >= 0.6:
            autonomy_level = AutonomyLevel.SUPERVISED
        elif autonomy_score >= 0.4:
            autonomy_level = AutonomyLevel.COLLABORATIVE
        else:
            autonomy_level = AutonomyLevel.GUIDED
        
        return {
            'level': autonomy_level,
            'score': autonomy_score,
            'factors': autonomy_factors,
            'reasoning': self.generate_autonomy_reasoning(autonomy_factors, autonomy_level)
        }
    
    async def execute_autonomous_plan(self, execution_plan, execution_session):
        """
        Execute autonomous plan with intelligent adaptation
        """
        plan_execution = {
            'steps_completed': [],
            'adaptations_made': [],
            'quality_checks': [],
            'deliverables': {},
            'intermediate_outputs': {}
        }
        
        current_context = execution_session['execution_context'].copy()
        
        for step_index, step in enumerate(execution_plan.execution_steps):
            step_start_time = datetime.utcnow()
            
            try:
                # Execute step with autonomous intelligence
                step_result = await self.execute_autonomous_step(
                    step,
                    current_context,
                    execution_plan,
                    execution_session
                )
                
                # Validate step quality
                step_quality = await self.quality_assessor.assess_step_quality(
                    step,
                    step_result,
                    execution_plan.quality_checkpoints
                )
                
                # Adapt if necessary
                if step_quality.get('requires_adaptation', False):
                    adaptation = await self.adapt_execution_step(
                        step,
                        step_result,
                        step_quality,
                        current_context
                    )
                    plan_execution['adaptations_made'].append(adaptation)
                    
                    # Re-execute with adaptation
                    step_result = await self.execute_autonomous_step(
                        adaptation['adapted_step'],
                        current_context,
                        execution_plan,
                        execution_session
                    )
                
                # Store step completion
                plan_execution['steps_completed'].append({
                    'step_index': step_index,
                    'step_id': step.get('id'),
                    'result': step_result,
                    'quality': step_quality,
                    'duration': (datetime.utcnow() - step_start_time).total_seconds()
                })
                
                # Update context with step outputs
                current_context.update(step_result.get('outputs', {}))
                
                # Store intermediate outputs
                if step_result.get('deliverable'):
                    plan_execution['intermediate_outputs'][step.get('id')] = step_result['deliverable']
                
            except Exception as e:
                # Handle step failure with autonomous recovery
                recovery_result = await self.autonomous_error_recovery(
                    step,
                    str(e),
                    current_context,
                    execution_plan
                )
                
                if recovery_result.get('recovered', False):
                    plan_execution['steps_completed'].append({
                        'step_index': step_index,
                        'step_id': step.get('id'),
                        'result': recovery_result,
                        'recovered_from_error': True,
                        'original_error': str(e),
                        'duration': (datetime.utcnow() - step_start_time).total_seconds()
                    })
                else:
                    # Cannot recover, escalate
                    raise Exception(f"Step {step.get('id')} failed and recovery unsuccessful: {e}")
        
        # Generate final deliverables
        final_deliverables = await self.generate_final_deliverables(
            plan_execution,
            execution_plan,
            current_context
        )
        plan_execution['deliverables'] = final_deliverables
        
        return plan_execution
    
    async def execute_autonomous_step(self, step, context, execution_plan, execution_session):
        """
        Execute a single autonomous step with full intelligence
        """
        step_execution = {
            'step_id': step.get('id'),
            'step_type': step.get('type'),
            'start_time': datetime.utcnow(),
            'outputs': {},
            'artifacts_created': [],
            'decisions_made': [],
            'tools_used': []
        }
        
        step_type = step.get('type')
        
        if step_type == 'requirement_analysis':
            result = await self.requirement_analyzer.analyze_requirements_autonomously(
                step, context, execution_session
            )
        elif step_type == 'architecture_design':
            result = await self.architecture_designer.design_architecture_autonomously(
                step, context, execution_session
            )
        elif step_type == 'code_generation':
            result = await self.code_generator.generate_code_autonomously(
                step, context, execution_session
            )
        elif step_type == 'test_generation':
            result = await self.test_generator.generate_tests_autonomously(
                step, context, execution_session
            )
        elif step_type == 'deployment_setup':
            result = await self.deployment_orchestrator.setup_deployment_autonomously(
                step, context, execution_session
            )
        elif step_type == 'integration':
            result = await self.execute_integration_step(
                step, context, execution_session
            )
        elif step_type == 'optimization':
            result = await self.execute_optimization_step(
                step, context, execution_session
            )
        else:
            result = await self.execute_generic_autonomous_step(
                step, context, execution_session
            )
        
        step_execution.update(result)
        step_execution['end_time'] = datetime.utcnow()
        step_execution['duration'] = (
            step_execution['end_time'] - step_execution['start_time']
        ).total_seconds()
        
        return step_execution

class AutonomousTaskAnalyzer:
    """
    Analyzes tasks to understand requirements, complexity, and optimal approach
    """
    
    def __init__(self, config):
        self.config = config
        self.complexity_analyzer = TaskComplexityAnalyzer()
        self.requirement_extractor = RequirementExtractor()
        self.context_analyzer = TaskContextAnalyzer()
        
    async def analyze_task_comprehensively(self, task, context):
        """
        Perform comprehensive analysis of development task
        """
        task_analysis = {
            'complexity_analysis': {},
            'requirement_analysis': {},
            'context_analysis': {},
            'risk_analysis': {},
            'feasibility_analysis': {},
            'approach_recommendations': []
        }
        
        # Analyze task complexity
        complexity_analysis = await self.complexity_analyzer.analyze_complexity(task, context)
        task_analysis['complexity_analysis'] = complexity_analysis
        
        # Extract and analyze requirements
        requirement_analysis = await self.requirement_extractor.extract_requirements(
            task, context
        )
        task_analysis['requirement_analysis'] = requirement_analysis
        
        # Analyze context factors
        context_analysis = await self.context_analyzer.analyze_context(task, context)
        task_analysis['context_analysis'] = context_analysis
        
        # Assess risks
        risk_analysis = await self.assess_task_risks(task, complexity_analysis, context)
        task_analysis['risk_analysis'] = risk_analysis
        
        # Assess feasibility
        feasibility_analysis = await self.assess_task_feasibility(
            task, complexity_analysis, context
        )
        task_analysis['feasibility_analysis'] = feasibility_analysis
        
        # Generate approach recommendations
        approach_recommendations = await self.generate_approach_recommendations(
            task_analysis
        )
        task_analysis['approach_recommendations'] = approach_recommendations
        
        # Calculate overall scores
        task_analysis['complexity_score'] = complexity_analysis.get('overall_score', 0.5)
        task_analysis['clarity_score'] = requirement_analysis.get('clarity_score', 0.5)
        task_analysis['risk_score'] = risk_analysis.get('overall_risk', 0.5)
        task_analysis['feasibility_score'] = feasibility_analysis.get('feasibility_score', 0.5)
        
        return task_analysis
    
    async def assess_task_risks(self, task, complexity_analysis, context):
        """
        Assess risks associated with autonomous task execution
        """
        risk_factors = {
            'technical_risks': [],
            'timeline_risks': [],
            'quality_risks': [],
            'integration_risks': [],
            'dependency_risks': []
        }
        
        # Assess technical risks
        if complexity_analysis.get('technical_complexity', 0) > 0.7:
            risk_factors['technical_risks'].append({
                'risk': 'high_technical_complexity',
                'probability': 0.7,
                'impact': 'high',
                'mitigation': 'Break into smaller, well-defined subtasks'
            })
        
        # Assess timeline risks
        estimated_effort = task.estimated_effort or 1.0
        if estimated_effort > 4.0:  # More than 4 hours
            risk_factors['timeline_risks'].append({
                'risk': 'extended_development_time',
                'probability': 0.6,
                'impact': 'medium',
                'mitigation': 'Implement incremental delivery with checkpoints'
            })
        
        # Assess quality risks
        if not task.acceptance_criteria or len(task.acceptance_criteria) < 3:
            risk_factors['quality_risks'].append({
                'risk': 'unclear_acceptance_criteria',
                'probability': 0.8,
                'impact': 'high',
                'mitigation': 'Generate detailed acceptance criteria autonomously'
            })
        
        # Calculate overall risk score
        all_risks = []
        for risk_category in risk_factors.values():
            all_risks.extend(risk_category)
        
        if all_risks:
            risk_scores = [risk['probability'] for risk in all_risks]
            overall_risk = np.mean(risk_scores)
        else:
            overall_risk = 0.2  # Low default risk
        
        return {
            'risk_factors': risk_factors,
            'overall_risk': overall_risk,
            'high_risk_areas': [
                category for category, risks in risk_factors.items() 
                if any(risk['probability'] > 0.7 for risk in risks)
            ]
        }

class AutonomousCodeGenerator:
    """
    Generates high-quality code autonomously using advanced techniques
    """
    
    def __init__(self, claude_code, config):
        self.claude_code = claude_code
        self.config = config
        self.pattern_library = CodePatternLibrary()
        self.quality_templates = CodeQualityTemplates()
        
    async def generate_code_autonomously(self, step, context, execution_session):
        """
        Generate code autonomously with quality assurance
        """
        code_generation = {
            'generated_files': {},
            'code_analysis': {},
            'quality_metrics': {},
            'optimizations_applied': [],
            'patterns_used': []
        }
        
        # Analyze code generation requirements
        generation_requirements = await self.analyze_generation_requirements(
            step, context
        )
        
        # Select optimal patterns and templates
        selected_patterns = await self.pattern_library.select_optimal_patterns(
            generation_requirements
        )
        code_generation['patterns_used'] = selected_patterns
        
        # Generate code using advanced techniques
        for component in generation_requirements['components']:
            generated_code = await self.generate_component_code(
                component,
                selected_patterns,
                context
            )
            
            # Apply quality optimizations
            optimized_code = await self.apply_quality_optimizations(
                generated_code,
                component,
                context
            )
            
            # Validate code quality
            quality_metrics = await self.validate_code_quality(
                optimized_code,
                component
            )
            
            code_generation['generated_files'][component['name']] = optimized_code
            code_generation['quality_metrics'][component['name']] = quality_metrics
        
        # Create files using Claude Code
        for file_name, code_content in code_generation['generated_files'].items():
            await self.claude_code.write(
                file_path=f"{context.get('output_path', 'src')}/{file_name}",
                content=code_content
            )
        
        return code_generation
    
    async def generate_component_code(self, component, patterns, context):
        """
        Generate code for a specific component using selected patterns
        """
        # Use Claude Code to generate high-quality code
        generation_prompt = self.create_generation_prompt(component, patterns, context)
        
        # Generate code using the universal LLM interface
        generated_result = await self.claude_code.generate_code({
            'prompt': generation_prompt,
            'component_spec': component,
            'patterns': patterns,
            'context': context,
            'quality_requirements': self.config.get('quality_requirements', {})
        })
        
        return generated_result.get('code', '')
    
    def create_generation_prompt(self, component, patterns, context):
        """
        Create intelligent prompt for code generation
        """
        prompt = f"""
Generate high-quality {component['language']} code for: {component['name']}

Requirements:
{chr(10).join(f"- {req}" for req in component.get('requirements', []))}

Apply these patterns:
{chr(10).join(f"- {pattern['name']}: {pattern['description']}" for pattern in patterns)}

Context:
- Project: {context.get('project_name', 'Unknown')}
- Architecture: {context.get('architecture_style', 'Standard')}
- Performance Requirements: {context.get('performance_requirements', 'Standard')}

Quality Standards:
- Follow best practices and coding standards
- Include comprehensive error handling
- Add appropriate documentation and comments
- Ensure code is maintainable and testable
- Optimize for performance where appropriate

Generate complete, production-ready code.
"""
        return prompt

class AutonomousLearningEngine:
    """
    Enables autonomous learning and improvement from execution outcomes
    """
    
    def __init__(self, config):
        self.config = config
        self.learning_model = AutonomousLearningModel()
        self.experience_database = ExperienceDatabase()
        self.improvement_generator = ImprovementGenerator()
        
    async def learn_from_execution(self, execution_session):
        """
        Learn from successful execution to improve future performance
        """
        learning_outcomes = {
            'patterns_learned': [],
            'improvements_identified': [],
            'success_factors': [],
            'model_updates': {}
        }
        
        # Extract learning patterns from execution
        execution_patterns = await self.extract_execution_patterns(execution_session)
        learning_outcomes['patterns_learned'] = execution_patterns
        
        # Identify improvement opportunities
        improvements = await self.improvement_generator.identify_improvements(
            execution_session
        )
        learning_outcomes['improvements_identified'] = improvements
        
        # Analyze success factors
        success_factors = await self.analyze_success_factors(execution_session)
        learning_outcomes['success_factors'] = success_factors
        
        # Update learning models
        model_updates = await self.learning_model.update_from_experience(
            execution_session
        )
        learning_outcomes['model_updates'] = model_updates
        
        # Store experience in database
        await self.experience_database.store_experience(
            execution_session,
            learning_outcomes
        )
        
        return learning_outcomes
    
    async def learn_from_failure(self, execution_session, error):
        """
        Learn from failed execution to prevent future failures
        """
        failure_learning = {
            'failure_patterns': [],
            'prevention_strategies': [],
            'fallback_improvements': [],
            'model_corrections': {}
        }
        
        # Analyze failure patterns
        failure_patterns = await self.analyze_failure_patterns(
            execution_session,
            error
        )
        failure_learning['failure_patterns'] = failure_patterns
        
        # Generate prevention strategies
        prevention_strategies = await self.generate_prevention_strategies(
            execution_session,
            failure_patterns
        )
        failure_learning['prevention_strategies'] = prevention_strategies
        
        # Update learning models with failure information
        model_corrections = await self.learning_model.correct_from_failure(
            execution_session,
            error
        )
        failure_learning['model_corrections'] = model_corrections
        
        # Store failure experience
        await self.experience_database.store_failure_experience(
            execution_session,
            error,
            failure_learning
        )
        
        return failure_learning
```

### Autonomous Development Commands

```bash
# Autonomous task execution
bmad autonomous execute --task "implement-user-auth" --autonomy-level "supervised"
bmad autonomous plan --requirements "user-story.md" --generate-subtasks
bmad autonomous develop --feature "payment-integration" --end-to-end

# Autonomous learning and improvement
bmad autonomous learn --from-execution "session-id" --update-models
bmad autonomous improve --based-on-outcomes --optimize-approaches
bmad autonomous adapt --to-project-patterns --enhance-capabilities

# Autonomous quality and optimization
bmad autonomous validate --deliverables "all" --against-criteria
bmad autonomous optimize --code-quality --performance --maintainability
bmad autonomous test --generate-comprehensive --execute-automated

# Autonomous monitoring and analytics
bmad autonomous monitor --execution-progress --real-time
bmad autonomous analyze --performance-trends --success-patterns
bmad autonomous report --capabilities --improvements --recommendations
```

This Autonomous Development Engine transforms Claude Code into an intelligent development partner capable of independently completing complex tasks while continuously learning and improving its capabilities. The system can operate at multiple autonomy levels and adapt its approach based on context, requirements, and learned experience.

<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": "in_progress", "priority": "high", "id": "3.2"}, {"content": "Build Self-Improving AI Capabilities", "status": "pending", "priority": "high", "id": "3.3"}, {"content": "Develop Intelligent Automation Framework", "status": "pending", "priority": "high", "id": "3.4"}, {"content": "Create Quality Assurance Automation", "status": "pending", "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"}]