AI System Architecture
Design automation systems, APIs, and AI agents with production-grade reliability.
Architecture Workflow
- •Goal → Define outcome and success metrics
- •Constraints → Budget, latency, reliability, integrations
- •Components → Identify required building blocks
- •Design → Architecture diagram and data flow
- •Optimize → Cost, latency, failure modes
- •Implement → Phased rollout plan
- •Operate → Monitoring, alerting, maintenance
LLM Cost Optimization
| Strategy | When to Use | Savings |
|---|---|---|
| Model tiering | Route simple tasks to cheaper models | 50-80% |
| Caching | Repeated identical queries | 90%+ |
| Batching | Non-realtime processing | 20-40% |
| Prompt compression | Long context tasks | 30-50% |
| Output length control | Verbose responses | 20-30% |
Agent Design Principles
- •Single responsibility → One agent, one job
- •Explicit handoffs → Clear triggers between agents
- •Fail gracefully → Define fallback for every failure mode
- •Human escalation → Always have escape hatch
- •Audit trail → Log decisions, not just outputs
Reusability Standards
- •Configuration over code → Parameters externalized
- •Modular components → Swap without rewrite
- •Documentation → Inline comments + README
- •Testing → Unit tests for core logic
- •Versioning → Semantic versioning for APIs