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

Quick Reference

Anti-patterns

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