# Performance & Scalability Optimization

## Current Performance Bottlenecks

### 1. Sequential Agent Execution
**Problem**: Agents execute one-by-one, even when they could run in parallel
```yaml
# Current: Sequential (slow)
sequence:
  - step: 1 (analyst) -> 2 (pm) -> 3 (ux) -> 4 (architect) -> 5 (developer)
  
# Optimized: Parallel where possible  
execution_groups:
  group_1: [analyst]  # Must run first
  group_2: [pm]       # Depends on analyst
  group_3: [ux_expert, architect]  # Can run parallel after PM
  group_4: [developer]  # Depends on UX + architect  
  group_5: [qa]       # Final validation
```

### 2. Context Loading Inefficiencies
**Problem**: Each agent loads all previous outputs, even irrelevant ones
```yaml
# Current: Load everything (inefficient)
context_loading: "all_previous_outputs"

# Optimized: Selective loading
context_loading:
  analyst: []  # No dependencies
  pm: 
    - analyst.project_brief
    - analyst.structured_data.target_users
  architect:
    - pm.functional_requirements
    - pm.non_functional_requirements
    - analyst.technical_constraints
  # Only load what's needed
```

### 3. Template Processing Overhead
**Problem**: Full template parsing for simple variable substitution
```yaml
# Optimized template caching
template_optimization:
  pre_compile: true
  cache_duration: "session"
  variable_validation: "compile_time"
  partial_templates: true  # For repeated sections
```

## Scalability Improvements

### 1. Workflow Partitioning
```yaml
# For large projects, split workflows
workflow_partitioning:
  triggers:
    - feature_count > 20
    - complexity_score > 8
    - team_size > 5
    
  strategy:
    - split_by_domain: ["auth", "core_features", "reporting"]  
    - parallel_workflows: true
    - cross_domain_validation: "integration_checkpoints"
```

### 2. Agent Pool Management  
```yaml
# Support multiple concurrent projects
agent_pool:
  max_concurrent_sessions: 10
  agent_instances:
    analyst: 3
    pm: 3  
    architect: 2
    developer: 4
    qa: 2
    ux_expert: 2
  load_balancing: "round_robin"
```

### 3. Incremental Processing
```yaml
# Support iterative development
incremental_mode:
  enabled: true
  triggers:
    - requirements_change
    - feature_addition
    - technical_pivot
    
  strategy:
    - identify_affected_agents
    - preserve_unaffected_outputs
    - partial_workflow_execution
    - delta_updates_only
```

## Performance Metrics & Monitoring

### Key Performance Indicators
```yaml
performance_kpis:
  workflow_execution_time:
    target: "< 10 minutes for greenfield-ui"
    target: "< 20 minutes for greenfield-fullstack"
    
  agent_response_time:
    target: "< 90 seconds per agent"
    
  quality_score:
    target: "> 8.0 average across all outputs"
    
  retry_rate:
    target: "< 10% of agent executions"
    
  user_satisfaction:
    target: "> 85% positive feedback"
```

### Monitoring System
```yaml
monitoring:
  real_time_metrics:
    - agent_execution_time
    - context_size
    - template_processing_time
    - quality_scores
    
  alerting:
    - execution_time > threshold
    - quality_score < minimum
    - error_rate > 15%
    
  optimization_recommendations:
    - automatic_workflow_tuning
    - agent_prompt_optimization
    - template_simplification_suggestions
```