36 KiB
36 KiB
Self-Improving AI Capabilities
Adaptive Learning and Continuous Enhancement for Enhanced BMAD System
The Self-Improving AI module enables the BMAD system to continuously learn from its experiences, adapt its behavior, optimize its performance, and automatically enhance its capabilities based on outcomes, feedback, and changing requirements.
Self-Improvement Architecture
Comprehensive Learning and Adaptation Framework
self_improvement_architecture:
learning_mechanisms:
outcome_based_learning:
- success_pattern_extraction: "Learn from successful executions and outcomes"
- failure_analysis_learning: "Learn from failures and mistakes"
- performance_correlation_learning: "Correlate actions with performance outcomes"
- feedback_integration_learning: "Learn from user and system feedback"
- comparative_analysis_learning: "Learn by comparing different approaches"
experiential_learning:
- execution_pattern_learning: "Learn from repeated execution patterns"
- context_adaptation_learning: "Learn to adapt to different contexts"
- user_behavior_learning: "Learn from user interaction patterns"
- project_specific_learning: "Learn project-specific patterns and preferences"
- domain_expertise_learning: "Develop domain-specific expertise over time"
reinforcement_learning:
- reward_based_optimization: "Optimize based on reward signals"
- exploration_exploitation_balance: "Balance trying new approaches vs proven ones"
- policy_gradient_improvement: "Improve decision policies over time"
- multi_armed_bandit_optimization: "Optimize choices among alternatives"
- temporal_difference_learning: "Learn from prediction errors"
meta_learning:
- learning_to_learn: "Improve the learning process itself"
- transfer_learning: "Transfer knowledge across domains and projects"
- few_shot_learning: "Learn quickly from limited examples"
- continual_learning: "Learn continuously without forgetting"
- curriculum_learning: "Learn in progressively complex sequences"
adaptation_capabilities:
behavioral_adaptation:
- strategy_adaptation: "Adapt strategies based on effectiveness"
- communication_style_adaptation: "Adapt communication to user preferences"
- workflow_adaptation: "Adapt workflows to project characteristics"
- tool_usage_adaptation: "Adapt tool usage patterns for efficiency"
- collaboration_pattern_adaptation: "Adapt collaboration patterns to team dynamics"
performance_adaptation:
- speed_optimization_adaptation: "Adapt to optimize execution speed"
- quality_optimization_adaptation: "Adapt to optimize output quality"
- resource_usage_adaptation: "Adapt resource usage patterns"
- cost_efficiency_adaptation: "Adapt to optimize cost efficiency"
- accuracy_improvement_adaptation: "Adapt to improve accuracy over time"
contextual_adaptation:
- project_context_adaptation: "Adapt to different project types and sizes"
- team_context_adaptation: "Adapt to different team structures and cultures"
- domain_context_adaptation: "Adapt to different business domains"
- technology_context_adaptation: "Adapt to different technology stacks"
- temporal_context_adaptation: "Adapt to changing requirements over time"
capability_adaptation:
- skill_development: "Develop new skills based on requirements"
- knowledge_expansion: "Expand knowledge in relevant areas"
- tool_mastery_improvement: "Improve mastery of available tools"
- pattern_recognition_enhancement: "Enhance pattern recognition abilities"
- decision_making_refinement: "Refine decision-making processes"
improvement_processes:
automated_optimization:
- parameter_tuning: "Automatically tune system parameters"
- algorithm_selection: "Select optimal algorithms for tasks"
- workflow_optimization: "Optimize execution workflows"
- resource_allocation_optimization: "Optimize resource allocation"
- performance_bottleneck_elimination: "Identify and eliminate bottlenecks"
self_diagnosis:
- performance_monitoring: "Monitor own performance metrics"
- error_pattern_detection: "Detect patterns in errors and failures"
- capability_gap_identification: "Identify missing or weak capabilities"
- efficiency_analysis: "Analyze efficiency in different scenarios"
- quality_assessment: "Assess quality of outputs and decisions"
capability_enhancement:
- skill_acquisition: "Acquire new skills and capabilities"
- knowledge_base_expansion: "Expand knowledge base with new information"
- pattern_library_growth: "Grow library of recognized patterns"
- best_practice_accumulation: "Accumulate best practices over time"
- expertise_deepening: "Deepen expertise in specific domains"
validation_and_testing:
- improvement_validation: "Validate improvements before deployment"
- a_b_testing: "Test different approaches systematically"
- regression_testing: "Ensure improvements don't break existing functionality"
- performance_benchmarking: "Benchmark performance improvements"
- quality_assurance: "Ensure quality is maintained or improved"
Self-Improving AI Implementation
import numpy as np
import pandas as pd
from typing import Dict, List, Any, Optional, Tuple, Callable
from dataclasses import dataclass, field
from enum import Enum
import asyncio
from datetime import datetime, timedelta
import json
import pickle
from collections import defaultdict, deque
import statistics
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, accuracy_score
import joblib
import hashlib
class LearningType(Enum):
OUTCOME_BASED = "outcome_based"
EXPERIENTIAL = "experiential"
REINFORCEMENT = "reinforcement"
META_LEARNING = "meta_learning"
class ImprovementType(Enum):
PERFORMANCE = "performance"
QUALITY = "quality"
EFFICIENCY = "efficiency"
CAPABILITY = "capability"
KNOWLEDGE = "knowledge"
@dataclass
class LearningExperience:
"""
Represents a learning experience from system execution
"""
experience_id: str
timestamp: datetime
context: Dict[str, Any]
action_taken: Dict[str, Any]
outcome: Dict[str, Any]
performance_metrics: Dict[str, float]
success_indicators: Dict[str, bool]
learning_opportunities: List[str] = field(default_factory=list)
feedback: Optional[Dict[str, Any]] = None
@dataclass
class ImprovementCandidate:
"""
Represents a potential improvement to the system
"""
improvement_id: str
improvement_type: ImprovementType
description: str
expected_benefits: Dict[str, float]
implementation_complexity: float
validation_requirements: List[str]
dependencies: List[str] = field(default_factory=list)
risk_assessment: Dict[str, float] = field(default_factory=dict)
@dataclass
class CapabilityMetrics:
"""
Tracks metrics for system capabilities
"""
capability_name: str
usage_frequency: float
success_rate: float
average_performance: float
improvement_trend: float
user_satisfaction: float
efficiency_score: float
class SelfImprovingAI:
"""
Advanced self-improving AI system with continuous learning and adaptation
"""
def __init__(self, config=None):
self.config = config or {
'learning_rate': 0.01,
'experience_buffer_size': 10000,
'improvement_threshold': 0.05,
'validation_required': True,
'auto_apply_improvements': False,
'exploration_rate': 0.1,
'performance_baseline_window': 100
}
# Learning components
self.outcome_learner = OutcomeBasedLearner(self.config)
self.experiential_learner = ExperientialLearner(self.config)
self.reinforcement_learner = ReinforcementLearner(self.config)
self.meta_learner = MetaLearner(self.config)
# Adaptation components
self.behavioral_adapter = BehavioralAdapter(self.config)
self.performance_adapter = PerformanceAdapter(self.config)
self.contextual_adapter = ContextualAdapter(self.config)
self.capability_adapter = CapabilityAdapter(self.config)
# Improvement components
self.improvement_engine = ImprovementEngine(self.config)
self.self_diagnostics = SelfDiagnostics(self.config)
self.capability_enhancer = CapabilityEnhancer(self.config)
self.validation_engine = ValidationEngine(self.config)
# Knowledge and experience storage
self.experience_buffer = deque(maxlen=self.config['experience_buffer_size'])
self.capability_metrics = {}
self.performance_history = defaultdict(list)
self.improvement_history = []
# Learning models
self.performance_predictor = None
self.success_classifier = None
self.improvement_recommender = None
# Improvement state
self.pending_improvements = []
self.active_experiments = {}
self.validated_improvements = []
async def learn_from_experience(self, experience: LearningExperience):
"""
Learn from a system execution experience
"""
learning_session = {
'session_id': generate_uuid(),
'experience_id': experience.experience_id,
'start_time': datetime.utcnow(),
'learning_results': {},
'adaptations_made': [],
'improvements_identified': []
}
# Store experience in buffer
self.experience_buffer.append(experience)
# Apply different learning mechanisms
learning_tasks = [
self.outcome_learner.learn_from_outcome(experience),
self.experiential_learner.learn_from_experience(experience),
self.reinforcement_learner.update_from_experience(experience),
self.meta_learner.extract_meta_patterns(experience)
]
learning_results = await asyncio.gather(*learning_tasks)
# Integrate learning results
integrated_insights = await self.integrate_learning_insights(
learning_results,
experience
)
learning_session['learning_results'] = integrated_insights
# Identify adaptation opportunities
adaptation_opportunities = await self.identify_adaptation_opportunities(
integrated_insights,
experience
)
# Apply immediate adaptations
immediate_adaptations = await self.apply_immediate_adaptations(
adaptation_opportunities
)
learning_session['adaptations_made'] = immediate_adaptations
# Identify improvement opportunities
improvement_opportunities = await self.identify_improvement_opportunities(
integrated_insights,
experience
)
learning_session['improvements_identified'] = improvement_opportunities
# Update capability metrics
await self.update_capability_metrics(experience)
# Update performance models
await self.update_performance_models()
learning_session['end_time'] = datetime.utcnow()
learning_session['learning_duration'] = (
learning_session['end_time'] - learning_session['start_time']
).total_seconds()
return learning_session
async def identify_improvement_opportunities(self, learning_insights, experience):
"""
Identify specific opportunities for system improvement
"""
improvement_opportunities = []
# Performance-based improvements
performance_improvements = await self.identify_performance_improvements(
learning_insights,
experience
)
improvement_opportunities.extend(performance_improvements)
# Quality-based improvements
quality_improvements = await self.identify_quality_improvements(
learning_insights,
experience
)
improvement_opportunities.extend(quality_improvements)
# Capability-based improvements
capability_improvements = await self.identify_capability_improvements(
learning_insights,
experience
)
improvement_opportunities.extend(capability_improvements)
# Efficiency-based improvements
efficiency_improvements = await self.identify_efficiency_improvements(
learning_insights,
experience
)
improvement_opportunities.extend(efficiency_improvements)
# Knowledge-based improvements
knowledge_improvements = await self.identify_knowledge_improvements(
learning_insights,
experience
)
improvement_opportunities.extend(knowledge_improvements)
return improvement_opportunities
async def identify_performance_improvements(self, learning_insights, experience):
"""
Identify performance improvement opportunities
"""
performance_improvements = []
# Analyze performance metrics from experience
performance_metrics = experience.performance_metrics
# Compare with historical performance
for metric_name, metric_value in performance_metrics.items():
historical_values = self.performance_history[metric_name]
if len(historical_values) >= 10: # Need sufficient history
historical_mean = statistics.mean(historical_values[-50:]) # Last 50 values
historical_std = statistics.stdev(historical_values[-50:]) if len(historical_values) > 1 else 0
# Identify underperformance
if metric_value < historical_mean - 2 * historical_std:
performance_improvements.append({
'type': ImprovementType.PERFORMANCE,
'metric': metric_name,
'current_value': metric_value,
'expected_value': historical_mean,
'improvement_needed': historical_mean - metric_value,
'confidence': 0.8,
'suggested_actions': await self.suggest_performance_actions(
metric_name,
metric_value,
historical_mean,
experience
)
})
return performance_improvements
async def suggest_performance_actions(self, metric_name, current_value, expected_value, experience):
"""
Suggest specific actions to improve performance
"""
actions = []
if metric_name == 'execution_time':
actions.extend([
'Optimize algorithm selection for similar tasks',
'Implement caching for repeated operations',
'Parallelize independent operations',
'Use more efficient data structures'
])
elif metric_name == 'memory_usage':
actions.extend([
'Implement memory-efficient algorithms',
'Optimize data structure usage',
'Implement garbage collection optimizations',
'Use streaming processing for large datasets'
])
elif metric_name == 'accuracy':
actions.extend([
'Improve training data quality',
'Use ensemble methods for better accuracy',
'Implement cross-validation for model selection',
'Fine-tune model hyperparameters'
])
elif metric_name == 'cost_efficiency':
actions.extend([
'Optimize resource allocation',
'Implement cost-aware scheduling',
'Use cheaper alternatives when appropriate',
'Implement usage-based optimization'
])
return actions
async def apply_improvement(self, improvement_candidate: ImprovementCandidate):
"""
Apply a validated improvement to the system
"""
application_session = {
'session_id': generate_uuid(),
'improvement_id': improvement_candidate.improvement_id,
'start_time': datetime.utcnow(),
'application_steps': [],
'validation_results': {},
'rollback_info': {},
'success': False
}
try:
# Validate improvement before application
if self.config['validation_required']:
validation_results = await self.validation_engine.validate_improvement(
improvement_candidate
)
application_session['validation_results'] = validation_results
if not validation_results.get('passed', False):
application_session['success'] = False
application_session['error'] = 'Validation failed'
return application_session
# Create rollback information
rollback_info = await self.create_rollback_info(improvement_candidate)
application_session['rollback_info'] = rollback_info
# Apply improvement based on type
if improvement_candidate.improvement_type == ImprovementType.PERFORMANCE:
result = await self.apply_performance_improvement(improvement_candidate)
elif improvement_candidate.improvement_type == ImprovementType.QUALITY:
result = await self.apply_quality_improvement(improvement_candidate)
elif improvement_candidate.improvement_type == ImprovementType.EFFICIENCY:
result = await self.apply_efficiency_improvement(improvement_candidate)
elif improvement_candidate.improvement_type == ImprovementType.CAPABILITY:
result = await self.apply_capability_improvement(improvement_candidate)
elif improvement_candidate.improvement_type == ImprovementType.KNOWLEDGE:
result = await self.apply_knowledge_improvement(improvement_candidate)
else:
result = {'success': False, 'error': 'Unknown improvement type'}
application_session['application_steps'] = result.get('steps', [])
application_session['success'] = result.get('success', False)
if application_session['success']:
# Record successful improvement
self.improvement_history.append({
'improvement_id': improvement_candidate.improvement_id,
'type': improvement_candidate.improvement_type,
'applied_at': datetime.utcnow(),
'expected_benefits': improvement_candidate.expected_benefits,
'application_session': application_session['session_id']
})
# Schedule post-application monitoring
await self.schedule_improvement_monitoring(improvement_candidate)
except Exception as e:
application_session['success'] = False
application_session['error'] = str(e)
# Attempt rollback if needed
if 'rollback_info' in application_session:
rollback_result = await self.rollback_improvement(
application_session['rollback_info']
)
application_session['rollback_result'] = rollback_result
finally:
application_session['end_time'] = datetime.utcnow()
application_session['application_duration'] = (
application_session['end_time'] - application_session['start_time']
).total_seconds()
return application_session
async def continuous_self_improvement(self):
"""
Continuously monitor and improve system capabilities
"""
improvement_cycle = {
'cycle_id': generate_uuid(),
'start_time': datetime.utcnow(),
'improvements_considered': 0,
'improvements_applied': 0,
'performance_gains': {},
'new_capabilities': []
}
while True:
try:
# Perform self-diagnosis
diagnostic_results = await self.self_diagnostics.perform_comprehensive_diagnosis()
# Identify improvement opportunities
improvement_opportunities = await self.improvement_engine.identify_opportunities(
diagnostic_results,
self.performance_history,
self.capability_metrics
)
improvement_cycle['improvements_considered'] += len(improvement_opportunities)
# Prioritize improvements
prioritized_improvements = await self.prioritize_improvements(
improvement_opportunities
)
# Apply high-priority improvements
for improvement in prioritized_improvements[:3]: # Apply top 3
if self.config['auto_apply_improvements']:
application_result = await self.apply_improvement(improvement)
if application_result['success']:
improvement_cycle['improvements_applied'] += 1
else:
# Add to pending improvements for manual review
self.pending_improvements.append(improvement)
# Monitor existing improvements
await self.monitor_improvement_effectiveness()
# Update capability metrics
await self.update_all_capability_metrics()
# Sleep before next cycle
await asyncio.sleep(3600) # 1 hour cycle
except Exception as e:
# Log error but continue improvement cycle
print(f"Error in continuous improvement cycle: {e}")
await asyncio.sleep(1800) # 30 minutes before retry
async def monitor_improvement_effectiveness(self):
"""
Monitor the effectiveness of applied improvements
"""
monitoring_results = {
'monitoring_timestamp': datetime.utcnow(),
'improvements_monitored': 0,
'effective_improvements': 0,
'ineffective_improvements': 0,
'improvements_requiring_attention': []
}
# Monitor recent improvements (last 30 days)
recent_threshold = datetime.utcnow() - timedelta(days=30)
for improvement_record in self.improvement_history:
if improvement_record['applied_at'] > recent_threshold:
monitoring_results['improvements_monitored'] += 1
# Assess improvement effectiveness
effectiveness_assessment = await self.assess_improvement_effectiveness(
improvement_record
)
if effectiveness_assessment['effective']:
monitoring_results['effective_improvements'] += 1
else:
monitoring_results['ineffective_improvements'] += 1
# Mark for attention if significantly ineffective
if effectiveness_assessment['effectiveness_score'] < 0.3:
monitoring_results['improvements_requiring_attention'].append({
'improvement_id': improvement_record['improvement_id'],
'reason': 'Low effectiveness score',
'score': effectiveness_assessment['effectiveness_score'],
'recommended_action': 'Consider rollback or modification'
})
return monitoring_results
async def assess_improvement_effectiveness(self, improvement_record):
"""
Assess the effectiveness of an applied improvement
"""
effectiveness_assessment = {
'improvement_id': improvement_record['improvement_id'],
'effective': False,
'effectiveness_score': 0.0,
'actual_benefits': {},
'benefit_realization': {},
'side_effects': []
}
# Compare expected vs actual benefits
expected_benefits = improvement_record['expected_benefits']
for benefit_metric, expected_value in expected_benefits.items():
# Get performance data since improvement was applied
performance_data = self.get_performance_data_since(
benefit_metric,
improvement_record['applied_at']
)
if performance_data:
actual_improvement = np.mean(performance_data) - self.get_baseline_performance(
benefit_metric,
improvement_record['applied_at']
)
effectiveness_assessment['actual_benefits'][benefit_metric] = actual_improvement
# Calculate realization percentage
if expected_value > 0:
realization_percentage = actual_improvement / expected_value
else:
realization_percentage = 1.0 if actual_improvement >= expected_value else 0.0
effectiveness_assessment['benefit_realization'][benefit_metric] = realization_percentage
# Calculate overall effectiveness score
if effectiveness_assessment['benefit_realization']:
effectiveness_assessment['effectiveness_score'] = np.mean(
list(effectiveness_assessment['benefit_realization'].values())
)
effectiveness_assessment['effective'] = effectiveness_assessment['effectiveness_score'] >= 0.7
return effectiveness_assessment
def get_performance_data_since(self, metric_name, since_timestamp):
"""
Get performance data for a metric since a specific timestamp
"""
# This would integrate with actual performance monitoring
# For now, return simulated data
return self.performance_history.get(metric_name, [])[-10:] # Last 10 values
def get_baseline_performance(self, metric_name, before_timestamp):
"""
Get baseline performance for a metric before a specific timestamp
"""
# This would get historical data before the timestamp
# For now, return simulated baseline
historical_data = self.performance_history.get(metric_name, [])
if len(historical_data) >= 20:
return np.mean(historical_data[-20:-10]) # Average of 10 values before last 10
return 0.0
class OutcomeBasedLearner:
"""
Learns from execution outcomes and results
"""
def __init__(self, config):
self.config = config
self.success_patterns = {}
self.failure_patterns = {}
async def learn_from_outcome(self, experience: LearningExperience):
"""
Learn from the outcome of an execution
"""
outcome_learning = {
'learning_type': LearningType.OUTCOME_BASED,
'patterns_identified': [],
'correlations_found': [],
'insights_extracted': []
}
# Determine if outcome was successful
overall_success = self.determine_overall_success(experience)
if overall_success:
# Learn from success
success_insights = await self.extract_success_patterns(experience)
outcome_learning['patterns_identified'].extend(success_insights)
else:
# Learn from failure
failure_insights = await self.extract_failure_patterns(experience)
outcome_learning['patterns_identified'].extend(failure_insights)
# Find correlations between context and outcome
correlations = await self.find_context_outcome_correlations(experience)
outcome_learning['correlations_found'] = correlations
return outcome_learning
def determine_overall_success(self, experience: LearningExperience):
"""
Determine if the overall outcome was successful
"""
success_indicators = experience.success_indicators
if not success_indicators:
return False
# Calculate success rate
success_count = sum(1 for success in success_indicators.values() if success)
success_rate = success_count / len(success_indicators)
return success_rate >= 0.7 # 70% success threshold
async def extract_success_patterns(self, experience: LearningExperience):
"""
Extract patterns from successful executions
"""
success_patterns = []
# Analyze context that led to success
context_factors = experience.context
action_factors = experience.action_taken
# Look for recurring patterns in successful contexts
context_pattern = {
'pattern_type': 'success_context',
'context_factors': context_factors,
'action_factors': action_factors,
'outcome_quality': experience.outcome,
'confidence': 0.8
}
success_patterns.append(context_pattern)
return success_patterns
class ValidationEngine:
"""
Validates improvements before they are applied
"""
def __init__(self, config):
self.config = config
async def validate_improvement(self, improvement_candidate: ImprovementCandidate):
"""
Validate an improvement candidate before application
"""
validation_results = {
'improvement_id': improvement_candidate.improvement_id,
'validation_timestamp': datetime.utcnow(),
'validation_tests': {},
'passed': False,
'confidence_score': 0.0,
'risks_identified': [],
'recommendations': []
}
# Run validation tests based on improvement type
if improvement_candidate.improvement_type == ImprovementType.PERFORMANCE:
validation_tests = await self.validate_performance_improvement(improvement_candidate)
elif improvement_candidate.improvement_type == ImprovementType.QUALITY:
validation_tests = await self.validate_quality_improvement(improvement_candidate)
elif improvement_candidate.improvement_type == ImprovementType.CAPABILITY:
validation_tests = await self.validate_capability_improvement(improvement_candidate)
else:
validation_tests = await self.validate_generic_improvement(improvement_candidate)
validation_results['validation_tests'] = validation_tests
# Determine overall validation result
test_results = [test['passed'] for test in validation_tests.values()]
if test_results:
pass_rate = sum(test_results) / len(test_results)
validation_results['passed'] = pass_rate >= 0.8 # 80% pass threshold
validation_results['confidence_score'] = pass_rate
return validation_results
async def validate_performance_improvement(self, improvement_candidate):
"""
Validate performance improvements
"""
validation_tests = {}
# Test 1: Backward compatibility
validation_tests['backward_compatibility'] = {
'test_name': 'Backward Compatibility',
'description': 'Ensure improvement maintains backward compatibility',
'passed': True, # Simulated
'details': 'All existing interfaces remain functional'
}
# Test 2: Performance regression
validation_tests['performance_regression'] = {
'test_name': 'Performance Regression',
'description': 'Ensure no performance degradation in other areas',
'passed': True, # Simulated
'details': 'No significant performance regression detected'
}
# Test 3: Resource usage
validation_tests['resource_usage'] = {
'test_name': 'Resource Usage',
'description': 'Validate resource usage is within acceptable limits',
'passed': True, # Simulated
'details': 'Memory and CPU usage within expected ranges'
}
return validation_tests
Self-Improvement Commands
# Learning and adaptation
bmad learn --from-experience --session-id "uuid" --extract-patterns
bmad adapt --to-context --project-type "web-app" --optimize-for "performance"
bmad improve --capability "code-generation" --based-on-feedback
# Performance monitoring and optimization
bmad monitor --self-performance --real-time --alerts
bmad optimize --self-performance --target-metrics "speed,accuracy,cost"
bmad diagnose --self-capabilities --identify-weaknesses
# Improvement management
bmad improvements --list-opportunities --prioritize --by-impact
bmad improvements --apply --improvement-id "uuid" --validate-first
bmad improvements --monitor --effectiveness --since "7d"
# Knowledge and capability enhancement
bmad knowledge --expand --domain "frontend-development" --learn-patterns
bmad capabilities --assess --identify-gaps --suggest-enhancements
bmad expertise --develop --area "security" --based-on-projects
# Experimentation and validation
bmad experiment --a-b-test --approach1 "current" --approach2 "optimized"
bmad validate --improvement "performance-boost" --before-applying
bmad rollback --improvement "uuid" --if-ineffective
This Self-Improving AI module enables the BMAD system to continuously learn, adapt, and enhance its capabilities based on experience, feedback, and performance data, creating a truly intelligent and evolving development assistant.
<function_calls> [{"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": "in_progress", "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"}]