Performance Optimization Workflow

Systematic approach to finding and fixing performance issues.

Phase 1: Baseline

Agents: performance-engineer

Measure current state:

• •Response times (p50, p95, p99)
• •Memory usage
• •CPU utilization
• •Database query times
• •Bundle sizes (frontend)
• •Render performance

Output: Baseline metrics report

Phase 2: Bottleneck Identification

Agents: performance-engineer

Analysis:

• •Profiling (CPU, memory)
• •Query analysis (slow query log, EXPLAIN)
• •Bundle analysis (webpack-bundle-analyzer)
• •Network analysis (waterfall, latency)

Output: Bottleneck list with priority ranking

Phase 3: Optimization Planning

Agents: requirements-analyst

• •Prioritize by impact vs effort
• •Define expected improvements
• •Determine implementation order
• •Set target metrics

Phase 4: Database Optimization

Agents: database-optimizer

Tasks:

• •Query optimization (rewrite slow queries)
• •Index creation/optimization
• •Caching strategy (Redis, memcached)
• •Connection pooling

Phase 5: Code Optimization

Agents: performance-engineer

Focus:

• •Algorithm efficiency (O(n) → O(log n))
• •Memory management (leaks, allocation)
• •Async operations (parallelize I/O)
• •Application-level caching

Phase 6: Frontend Optimization

Agents: performance-engineer

Tasks:

• •Bundle size reduction
• •Code splitting
• •Lazy loading
• •Asset optimization (images, fonts)
• •Render optimization (virtualization, memoization)

Phase 7: Infrastructure Optimization

Agents: devops-architect

Areas:

• •Scaling strategy (horizontal/vertical)
• •Caching layers (CDN, reverse proxy)
• •Load balancing
• •Resource allocation

Phase 8: Validation

Agents: performance-engineer

Blocking: Must meet targets

Targets:

• •Response time: <200ms (p95)
• •Memory usage: <200MB
• •Bundle size: <500KB

Phase 9: Load Testing

Agents: performance-engineer

Scenarios:

• •Normal load (expected traffic)
• •Peak load (2-3x normal)
• •Stress test (find breaking point)

Duration: 30min per scenario

Phase 10: Monitoring Setup

Agents: devops-architect

• •Performance dashboards
• •Alerting rules (degradation detection)
• •Automated profiling (continuous)

Success Criteria

• • Performance targets met
• • Load tests pass
• • Monitoring in place
• • Documentation complete

Targets

Anti-patterns

• •❌ Optimizing without measuring first
• •❌ Micro-optimizations before algorithmic fixes
• •❌ Optimizing code that isn't the bottleneck
• •❌ No load testing before production