35 KiB
35 KiB
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
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
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
# 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.
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