# Orchestrator Mode Examples

## Complete aiGI Workflow Orchestration

### Example 1: Full Development Workflow Using CLI
```bash
# Initialize orchestration with comprehensive goals
python -m safla.goals --create \
  --name "complete_aiGI_development_cycle" \
  --description "Full development workflow from specification to deployment" \
  --priority high \
  --metrics completion_time:7200,quality_score:0.9,test_coverage:0.85

# Select optimal strategy for development workflow
python -m safla.strategy --select \
  --context "aiGI_development" \
  --constraints time_limit:7200,memory_limit:8GB,quality_threshold:0.9 \
  --objectives performance,quality,maintainability

# Create specialized agent team
python -m safla.agents --create \
  --type cognitive \
  --config focus:analysis_and_planning,depth:comprehensive,quality_threshold:0.9

python -m safla.agents --create \
  --type memory \
  --config focus:knowledge_management,vector_dims:512,similarity_threshold:0.8

python -m safla.agents --create \
  --type optimization \
  --config focus:performance_tuning,metrics:all,optimization_level:balanced

# Execute workflow phases with monitoring
python -m safla.orchestrate --execute \
  --phases prompt-generator,architect,code,tdd,critic,final-assembly \
  --agents cognitive_001,memory_001,optimization_001 \
  --monitor-progress \
  --adaptive-learning

# Monitor workflow progress
python -m safla.orchestrate --monitor \
  --workflow aiGI_001 \
  --real-time \
  --alerts \
  --adaptive-learning
```

### Example 2: Adaptive Learning Workflow Using MCP Tools
```bash
# Start with baseline strategy using MCP
use_mcp_tool safla select_optimal_strategy '{
  "context": "development",
  "constraints": {"learning_enabled": true, "adaptation_threshold": 0.1},
  "objectives": ["performance", "quality", "learning"]
}'

# Execute phases with learning integration
for phase in prompt-generator code tdd critic; do
  # Execute phase with current strategy
  python -m safla.orchestrate --execute \
    --phase $phase \
    --strategy adaptive_learning_001 \
    --monitor-learning
  
  # Analyze learning from phase execution
  use_mcp_tool safla get_learning_metrics '{
    "metric_type": "adaptation_rate",
    "time_range_hours": 1
  }'
  
  # Trigger learning cycle if needed
  use_mcp_tool safla trigger_learning_cycle '{
    "learning_type": "incremental",
    "data_sources": ["workflow_metrics", "error_patterns"],
    "focus_areas": ["optimization", "quality_improvement"]
  }'
  
  # Update strategy based on learning
  python -m safla.strategy --evaluate \
    --id adaptive_learning_001 \
    --period 1h \
    --adapt-if-needed
done
```

## Agent Coordination Examples

### Example 3: Multi-Agent Code Review Using CLI
```bash
# Create specialized review agents
python -m safla.agents --create \
  --type cognitive \
  --config focus:logic_analysis,depth:comprehensive \
  --timeout 1800

python -m safla.agents --create \
  --type cognitive \
  --config focus:security_analysis,depth:thorough \
  --timeout 1800

python -m safla.agents --create \
  --type optimization \
  --config focus:performance_analysis,metrics:all \
  --timeout 1800

# Coordinate agents for code review
python -m safla.agents --batch \
  --create-config code_review_agents.yaml \
  --coordinate \
  --monitor

# Execute parallel code review tasks
python -m safla.agents --interact \
  --session cognitive_001 \
  --command analyze_code_logic \
  --params file_path:src/main.ts,analysis_depth:comprehensive \
  --async-mode &

python -m safla.agents --interact \
  --session cognitive_002 \
  --command analyze_security \
  --params file_path:src/main.ts,security_depth:thorough \
  --async-mode &

python -m safla.agents --interact \
  --session optimization_001 \
  --command analyze_performance \
  --params file_path:src/main.ts,metrics:all \
  --async-mode &

# Wait for all reviews to complete and consolidate
wait
python -m safla.agents --consolidate-results \
  --sessions cognitive_001,cognitive_002,optimization_001 \
  --output consolidated_review.json
```

### Example 4: Dynamic Agent Scaling Using MCP Tools
```bash
# Analyze current workload
use_mcp_tool safla analyze_performance_bottlenecks '{
  "duration_seconds": 300,
  "include_memory_profile": true
}'

# Get current agent sessions
use_mcp_tool safla list_agent_sessions '{
  "include_inactive": false
}'

# Scale agents based on workload analysis
python -m safla.agents --auto-scale \
  --workload-analysis bottleneck_analysis.json \
  --target-performance 0.8 \
  --max-agents 10

# Monitor scaling effectiveness
python -m safla.monitor --agents \
  --scaling-metrics \
  --real-time \
  --duration 600
```

## Performance Optimization Examples

### Example 5: Real-Time Performance Optimization Using MCP Resources
```bash
# Set up continuous performance monitoring
python -m safla.monitor --performance \
  --real-time \
  --optimization-triggers \
  --auto-apply-safe-optimizations &

# Access real-time performance metrics via MCP resource
access_mcp_resource safla "safla://performance-metrics"

# Get AI-generated optimization recommendations
access_mcp_resource safla "safla://optimization-recommendations"

# Apply memory optimization if needed
use_mcp_tool safla optimize_memory_usage '{
  "optimization_level": "balanced",
  "target_memory_mb": 4096
}'

# Optimize vector operations for better performance
use_mcp_tool safla optimize_vector_operations '{
  "batch_size": 100,
  "use_gpu": true
}'

# Monitor optimization effectiveness
python -m safla.monitor --optimization-effectiveness \
  --baseline performance_baseline.json \
  --duration 300
```

### Example 6: Memory Usage Optimization Using CLI
```bash
# Analyze current memory bottlenecks
python -m safla.debug --bottlenecks \
  --focus memory \
  --duration 120 \
  --detailed

# Optimize memory usage with specific targets
use_mcp_tool safla optimize_memory_usage '{
  "optimization_level": "aggressive",
  "target_memory_mb": 6144
}'

# Validate memory operations after optimization
use_mcp_tool safla validate_memory_operations '{
  "test_data_size": 100,
  "include_stress_test": true
}'

# Benchmark memory performance improvements
use_mcp_tool safla benchmark_memory_performance '{
  "test_duration": 180,
  "memory_patterns": ["sequential", "random", "mixed"]
}'
```

## System Monitoring and Health Examples

### Example 7: Comprehensive System Health Monitoring
```bash
# Start comprehensive health monitoring
python -m safla.monitor --health \
  --interval 15 \
  --thresholds cpu_usage:0.8,memory_usage:0.85,error_rate:0.05 \
  --alerts \
  --auto-recovery &

# Get real-time system status
access_mcp_resource safla "safla://status"

# Monitor system logs for issues
access_mcp_resource safla "safla://system-logs"

# Check deployment status
access_mcp_resource safla "safla://deployments"

# Get system awareness state
use_mcp_tool safla get_system_awareness '{}'

# Update awareness parameters based on monitoring
use_mcp_tool safla update_awareness_state '{
  "awareness_level": 0.9,
  "focus_areas": ["performance", "quality", "security"],
  "introspection_depth": "deep"
}'
```

### Example 8: Predictive Analysis and Proactive Optimization
```bash
# Analyze adaptation patterns for predictions
use_mcp_tool safla analyze_adaptation_patterns '{
  "pattern_type": "all",
  "analysis_depth": "comprehensive",
  "time_window_days": 7
}'

# Access learning metrics for trend analysis
access_mcp_resource safla "safla://learning-metrics"

# Access adaptation patterns for prediction
access_mcp_resource safla "safla://adaptation-patterns"

# Trigger proactive learning based on patterns
use_mcp_tool safla trigger_learning_cycle '{
  "learning_type": "meta",
  "data_sources": ["adaptation_patterns", "performance_trends"],
  "focus_areas": ["predictive_optimization", "proactive_adaptation"]
}'

# Update learning parameters for better prediction
use_mcp_tool safla update_learning_parameters '{
  "learning_rate": 0.12,
  "adaptation_threshold": 0.08,
  "memory_retention": 0.95,
  "exploration_factor": 0.15
}'
```

## Goal Management and Strategy Examples

### Example 9: Advanced Goal Management Workflow
```bash
# Create hierarchical goals
python -m safla.goals --create \
  --name "system_optimization" \
  --description "Comprehensive system optimization initiative" \
  --priority critical \
  --metrics performance_improvement:0.3,resource_efficiency:0.25

python -m safla.goals --create \
  --name "performance_optimization" \
  --description "Optimize system performance metrics" \
  --priority high \
  --dependencies system_optimization \
  --metrics response_time:2000ms,throughput:1000rps

# Monitor goal progress
python -m safla.goals --evaluate \
  --id system_optimization_001 \
  --recommendations \
  --adaptive-targets

# Access goals via MCP resource
access_mcp_resource safla "safla://goals"

# Update goal progress based on metrics
python -m safla.goals --update \
  --id performance_optimization_001 \
  --progress 0.65 \
  --status active \
  --notes "Performance improvements showing positive trends"
```

### Example 10: Strategy Selection and Evaluation
```bash
# List available strategies with effectiveness metrics
python -m safla.strategy --list \
  --context development \
  --effectiveness 0.7

# Access strategies via MCP resource
access_mcp_resource safla "safla://strategies"

# Create custom strategy for specific context
use_mcp_tool safla create_custom_strategy '{
  "strategy_name": "rapid_iteration_with_quality",
  "description": "Fast development with quality gates",
  "context": "agile_development",
  "steps": ["quick_prototype", "quality_check", "iterate", "validate"],
  "expected_outcomes": ["faster_delivery", "maintained_quality"]
}'

# Evaluate strategy performance over time
use_mcp_tool safla evaluate_strategy_performance '{
  "strategy_id": "rapid_iteration_001",
  "evaluation_period_hours": 168,
  "metrics": ["delivery_speed", "quality_score", "resource_usage"]
}'
```

## Integration and Deployment Examples

### Example 11: MCP Integration Testing and Benchmarking
```bash
# Test MCP connectivity comprehensively
use_mcp_tool safla test_mcp_connectivity '{
  "test_depth": "comprehensive"
}'

# Benchmark MCP throughput
use_mcp_tool safla benchmark_mcp_throughput '{
  "request_count": 500,
  "concurrent_connections": 10,
  "payload_size": "large"
}'

# Run comprehensive integration tests
use_mcp_tool safla run_integration_tests '{
  "test_suite": "mcp_integration",
  "parallel": true,
  "verbose": true
}'

# Monitor MCP server health
python -m safla.monitor --mcp-servers \
  --health-checks \
  --performance-metrics \
  --real-time
```

### Example 12: Production Deployment and Monitoring
```bash
# Deploy SAFLA instance for production
use_mcp_tool safla deploy_safla_instance '{
  "instance_name": "production_aiGI",
  "environment": "production",
  "config_overrides": {
    "performance_mode": "high",
    "monitoring_level": "comprehensive",
    "backup_frequency": "hourly"
  }
}'

# Check deployment status
use_mcp_tool safla check_deployment_status '{
  "instance_name": "production_aiGI"
}'

# Scale deployment resources
use_mcp_tool safla scale_deployment '{
  "instance_name": "production_aiGI",
  "scale_factor": 1.5,
  "resource_type": "both"
}'

# Create comprehensive backup
use_mcp_tool safla backup_safla_data '{
  "backup_type": "full",
  "destination": "/backup/production",
  "compress": true
}'

# Monitor production system health
python -m safla.monitor --production \
  --real-time \
  --alerts \
  --performance-optimization \
  --predictive-analysis
```

## Troubleshooting and Recovery Examples

### Example 13: System Diagnostics and Recovery
```bash
# Comprehensive system diagnostics
python -m safla.debug --diagnostics \
  --comprehensive \
  --export-report diagnostics_$(date +%Y%m%d_%H%M%S).json

# Analyze error patterns
python -m safla.debug --errors \
  --pattern-analysis \
  --time-window 24h \
  --auto-categorize

# Validate system installation and configuration
use_mcp_tool safla validate_installation '{}'

# Get detailed system information
use_mcp_tool safla get_system_info '{}'

# Check GPU status if available
use_mcp_tool safla check_gpu_status '{}'

# Restore from backup if needed
use_mcp_tool safla restore_safla_data '{
  "backup_path": "/backup/production/full_backup.tar.gz",
  "restore_type": "full",
  "verify_integrity": true
}'
```

### Example 14: Automated Recovery and Resilience Testing
```bash
# Set up automated recovery monitoring
python -m safla.monitor --recovery \
  --auto-recovery-enabled \
  --recovery-strategies all \
  --test-resilience &

# Simulate failure scenarios for testing
python -m safla.test --resilience \
  --failure-scenarios network,memory,agent \
  --recovery-validation \
  --report-generation

# Monitor recovery effectiveness
python -m safla.monitor --recovery-metrics \
  --success-rate \
  --recovery-time \
  --system-stability