16 KiB
16 KiB
BMAD Context Engineering Enhancement - Success Metrics
📊 Overview
This document defines comprehensive success metrics, measurement methodologies, and validation criteria for the BMAD Context Engineering Enhancement project. These metrics ensure the enhancements meet performance targets while maintaining BMad Method compliance.
🎯 Primary Success Criteria
1. Dev Agent Leanness Metrics
Objective: Ensure dev agents remain lightweight and focused
| Metric | Target | Measurement Method | Priority |
|---|---|---|---|
| Token Limit Compliance | <2000 tokens/session | Automated counting during agent execution | Critical |
| Context Dependencies | <3 dependencies max | Static analysis of agent dependency declarations | Critical |
| Compression Efficiency | 0.9 compression ratio | Token count before/after optimization | Critical |
| Code Relevance | >95% code-related content | Content analysis scoring algorithm | Critical |
| Load Time Performance | <1 second context assembly | Timing measurement during agent initialization | High |
Validation Commands:
# Token limit validation
python -m bmad.metrics.validate_tokens --agent dev --max-tokens 2000
# Dependency compliance check
python -m bmad.metrics.check_dependencies --agent dev --max-deps 3
# Compression ratio verification
python -m bmad.metrics.test_compression --agent dev --target-ratio 0.9
# Code relevance assessment
python -m bmad.metrics.assess_relevance --agent dev --threshold 0.95
2. Planning Agent Rich Context Metrics
Objective: Enable comprehensive context capabilities for strategic agents
| Metric | Target | Measurement Method | Priority |
|---|---|---|---|
| Context Retrieval Accuracy | >85% relevance score | Semantic search result validation | Critical |
| Context Comprehensiveness | >90% domain coverage | Knowledge completeness assessment | High |
| Cross-Agent Handoff Success | 98% successful transfers | Handoff completion rate monitoring | Critical |
| Context Quality Score | >4.0/5.0 average | Multi-factor quality assessment | High |
| Rich Context Capacity | 8000 tokens max utilized | Token usage monitoring and optimization | Medium |
Validation Commands:
# Retrieval accuracy test
python -m bmad.metrics.test_retrieval --min-accuracy 0.85
# Comprehensiveness assessment
python -m bmad.metrics.assess_coverage --min-coverage 0.90
# Handoff success rate monitoring
python -m bmad.metrics.monitor_handoffs --min-success-rate 0.98
# Quality scoring validation
python -m bmad.metrics.score_quality --min-score 4.0
3. BMad Method Compliance Metrics
Objective: Ensure 100% adherence to BMad Method principles
| Metric | Target | Measurement Method | Priority |
|---|---|---|---|
| Natural Language Coverage | 100% markdown format | File format validation | Critical |
| Template Markup Compliance | 100% proper markup | Template syntax validation | Critical |
| File Size Compliance | All files <50KB | File size monitoring | High |
| Agent Type Differentiation | Clear dev/planning separation | Agent capability analysis | High |
| Dynamic Loading Compliance | On-demand resource loading | Resource loading pattern analysis | Medium |
Validation Commands:
# Template markup validation
python -m bmad.compliance.validate_markup --compliance 100%
# File size compliance check
python -m bmad.compliance.check_file_sizes --max-size 50KB
# Agent differentiation verification
python -m bmad.compliance.verify_agent_types --strict
📈 Performance Metrics
1. Context Processing Performance
Measurement Frequency: Continuous monitoring
| Performance Indicator | Target | Baseline | Current | Trend |
|---|---|---|---|---|
| Context Optimization Time | <500ms | TBD | TBD | - |
| Context Compression Speed | <200ms | TBD | TBD | - |
| Semantic Search Response | <2000ms | TBD | TBD | - |
| Agent Context Assembly | <1000ms | TBD | TBD | - |
| Cross-Agent Handoff Latency | <300ms | TBD | TBD | - |
2. Memory and Resource Utilization
Measurement Frequency: Per-session monitoring
| Resource Metric | Target | Acceptable Range | Warning Threshold |
|---|---|---|---|
| Memory Usage per Agent | <100MB | 50-150MB | >200MB |
| Context Storage Size | <10MB/session | 5-20MB | >50MB |
| CPU Usage During Processing | <20% | 10-30% | >50% |
| Disk I/O for Context Operations | <50 ops/sec | 20-100 ops/sec | >200 ops/sec |
3. Scalability Metrics
Measurement Frequency: Load testing cycles
| Scalability Factor | Target | Test Scenario | Success Criteria |
|---|---|---|---|
| Concurrent Agent Sessions | 50+ sessions | Multi-agent workflow | <10% performance degradation |
| Context Data Volume | 1GB+ total context | Large project simulation | <2x response time increase |
| Context History Depth | 1000+ handoffs | Extended session simulation | <5% accuracy loss |
| Cross-Session Context Sharing | 100+ shared contexts | Multi-session workflow | <1% context corruption |
🎛️ Quality Metrics
1. Context Quality Assessment
Measurement Method: Automated quality scoring + manual validation
| Quality Dimension | Weight | Measurement Criteria | Target Score |
|---|---|---|---|
| Relevance | 30% | Content alignment with task requirements | >4.5/5.0 |
| Completeness | 25% | Information sufficiency for task completion | >4.0/5.0 |
| Accuracy | 25% | Information correctness and validity | >4.5/5.0 |
| Freshness | 10% | Information recency and currency | >3.5/5.0 |
| Coherence | 10% | Logical flow and consistency | >4.0/5.0 |
Overall Quality Score Formula:
Quality Score = (Relevance × 0.3) + (Completeness × 0.25) + (Accuracy × 0.25) + (Freshness × 0.1) + (Coherence × 0.1)
Target: >4.0/5.0
2. User Experience Metrics
Measurement Method: User feedback and behavior analysis
| UX Metric | Target | Measurement Method | Frequency |
|---|---|---|---|
| Context Usefulness Rating | >4.0/5.0 | User surveys | Weekly |
| Agent Response Satisfaction | >85% satisfied | User feedback forms | Per-session |
| Context Clarity Score | >4.2/5.0 | Clarity assessment surveys | Bi-weekly |
| Workflow Efficiency Improvement | >20% faster | Task completion time comparison | Monthly |
🧪 Testing and Validation Framework
1. Automated Testing Suite
Execution Frequency: Continuous integration
Dev Agent Leanness Tests
# Test Suite: dev_agent_leanness
./tests/dev_leanness/test_token_limits.py
./tests/dev_leanness/test_dependency_count.py
./tests/dev_leanness/test_compression_ratio.py
./tests/dev_leanness/test_code_relevance.py
./tests/dev_leanness/test_performance.py
# Expected Results:
# ✅ All tests pass
# ✅ Token limits: <2000 tokens
# ✅ Dependencies: <3 dependencies
# ✅ Compression: 0.9 ratio achieved
# ✅ Code relevance: >95%
Planning Agent Rich Context Tests
# Test Suite: planning_agent_rich_context
./tests/rich_context/test_semantic_search.py
./tests/rich_context/test_context_retrieval.py
./tests/rich_context/test_cross_agent_handoffs.py
./tests/rich_context/test_context_quality.py
./tests/rich_context/test_memory_management.py
# Expected Results:
# ✅ Semantic search accuracy: >85%
# ✅ Context retrieval coverage: >90%
# ✅ Handoff success rate: >98%
# ✅ Quality score: >4.0/5.0
BMad Compliance Tests
# Test Suite: bmad_compliance
./tests/compliance/test_natural_language.py
./tests/compliance/test_template_markup.py
./tests/compliance/test_file_sizes.py
./tests/compliance/test_agent_differentiation.py
# Expected Results:
# ✅ 100% natural language compliance
# ✅ 100% template markup compliance
# ✅ All files <50KB
# ✅ Clear agent type separation
2. Performance Benchmarking
Execution Frequency: Daily automated benchmarks
Benchmark Test Scenarios
# Scenario 1: Single Agent Performance
python -m bmad.benchmarks.single_agent --agent dev --iterations 100
python -m bmad.benchmarks.single_agent --agent architect --iterations 100
# Scenario 2: Multi-Agent Workflow
python -m bmad.benchmarks.multi_agent --agents dev,architect,pm --workflow standard
# Scenario 3: Context-Heavy Operations
python -m bmad.benchmarks.context_operations --context-size large --operations 1000
# Scenario 4: Stress Testing
python -m bmad.benchmarks.stress_test --concurrent-agents 50 --duration 30min
3. Quality Assurance Validation
Execution Frequency: Weekly manual validation
Manual Validation Checklist
- Context Relevance: Review 20 random context optimizations for relevance
- Handoff Quality: Validate 10 cross-agent handoffs for information preservation
- User Experience: Conduct 5 user sessions with feedback collection
- Edge Case Handling: Test 10 edge cases for graceful degradation
- Error Recovery: Validate error handling in 5 failure scenarios
📊 Reporting and Monitoring
1. Real-Time Dashboards
Update Frequency: Real-time
Primary Metrics Dashboard
┌─────────────────────────────────────────────────────────────┐
│ BMAD Context Engineering Metrics │
├─────────────────────────────────────────────────────────────┤
│ Dev Agent Leanness: │
│ ├─ Token Compliance: ✅ 1,847/2,000 (92.4%) │
│ ├─ Dependencies: ✅ 2/3 (66.7%) │
│ ├─ Compression: ✅ 0.91 ratio (target: 0.9) │
│ └─ Code Relevance: ✅ 96.3% (target: >95%) │
│ │
│ Planning Agent Rich Context: │
│ ├─ Retrieval Accuracy: ✅ 87.2% (target: >85%) │
│ ├─ Context Coverage: ✅ 91.8% (target: >90%) │
│ ├─ Handoff Success: ✅ 98.7% (target: >98%) │
│ └─ Quality Score: ✅ 4.2/5.0 (target: >4.0) │
│ │
│ BMad Compliance: │
│ ├─ Natural Language: ✅ 100% (target: 100%) │
│ ├─ Template Markup: ✅ 100% (target: 100%) │
│ ├─ File Size: ✅ Avg 23KB (target: <50KB) │
│ └─ Agent Differentiation: ✅ Clear separation │
└─────────────────────────────────────────────────────────────┘
Performance Monitoring Dashboard
┌─────────────────────────────────────────────────────────────┐
│ Performance Metrics │
├─────────────────────────────────────────────────────────────┤
│ Context Operations: │
│ ├─ Optimization Time: ✅ 347ms (target: <500ms) │
│ ├─ Compression Speed: ✅ 156ms (target: <200ms) │
│ ├─ Search Response: ✅ 1,234ms (target: <2000ms) │
│ └─ Agent Assembly: ✅ 678ms (target: <1000ms) │
│ │
│ Resource Utilization: │
│ ├─ Memory Usage: ✅ 78MB (target: <100MB) │
│ ├─ Context Storage: ✅ 8.3MB (target: <10MB) │
│ ├─ CPU Usage: ✅ 14.2% (target: <20%) │
│ └─ Disk I/O: ✅ 34 ops/sec (target: <50 ops/sec) │
└─────────────────────────────────────────────────────────────┘
2. Weekly Reports
Generation: Automated weekly summary
Weekly Report Template
# BMAD Context Engineering - Weekly Report
## Summary
- **Overall Health**: ✅ All metrics within targets
- **Performance**: ✅ No degradation observed
- **Compliance**: ✅ 100% BMad Method adherence
- **User Satisfaction**: ✅ 4.3/5.0 average rating
## Key Achievements
- Dev agent leanness maintained: 96.3% code relevance
- Planning agent enhancements: 87.2% retrieval accuracy
- Cross-agent handoffs: 98.7% success rate
- Zero compliance violations
## Areas for Improvement
- Context compression speed: Target optimization for faster processing
- Memory usage optimization: Reduce average usage by 10%
- User experience enhancements: Focus on context clarity
## Action Items
1. Investigate compression algorithm optimizations
2. Implement memory usage monitoring alerts
3. Conduct user feedback sessions for UX improvements
## Next Week Focus
- Performance optimization initiatives
- User experience enhancement planning
- Preparation for month-end comprehensive review
3. Monthly Comprehensive Reviews
Frequency: Monthly stakeholder review
Monthly Review Agenda
- Metrics Review: Comprehensive analysis of all success criteria
- Performance Analysis: Trend analysis and performance optimization
- Compliance Validation: BMad Method adherence verification
- User Feedback: Stakeholder satisfaction and improvement suggestions
- Roadmap Updates: Future enhancement planning based on results
🎯 Success Thresholds
Minimum Viable Success (MVP)
- ✅ Dev agents: <2000 tokens, <3 dependencies, 0.9 compression
- ✅ Planning agents: >80% retrieval accuracy, >95% handoff success
- ✅ BMad compliance: >95% natural language, proper markup
- ✅ Performance: No degradation from baseline
Target Success (Preferred)
- ✅ Dev agents: All MVP criteria + >95% code relevance
- ✅ Planning agents: >85% retrieval accuracy, >98% handoff success
- ✅ BMad compliance: 100% adherence across all criteria
- ✅ Performance: 20% improvement over baseline
Exceptional Success (Stretch Goals)
- ✅ Dev agents: All target criteria + <1500 token average usage
- ✅ Planning agents: >90% retrieval accuracy, >99% handoff success
- ✅ BMad compliance: 100% + documentation quality excellence
- ✅ Performance: 50% improvement + scalability validation
🔍 Monitoring Implementation Plan
Phase 1: Basic Monitoring (Week 1-2)
- Implement automated token counting
- Set up dependency validation
- Configure compression ratio monitoring
- Establish baseline performance metrics
Phase 2: Advanced Monitoring (Week 3-4)
- Deploy semantic search accuracy measurement
- Implement context quality scoring
- Configure cross-agent handoff monitoring
- Set up real-time dashboards
Phase 3: Comprehensive Analytics (Week 5-6)
- Deploy user experience tracking
- Implement scalability monitoring
- Configure automated reporting
- Establish alert systems for threshold violations
Document Status: ✅ Complete
Monitoring Ready: All metrics and measurement methods defined
Implementation Priority: Begin with Phase 1 basic monitoring
Review Schedule: Weekly metrics review, monthly comprehensive assessment