● 30 lessons · Eduardo Falcão
Systems Design for AI-Native Apps
Master the architectural patterns, trade-offs, and engineering practices to build reliable, scalable, production-grade AI-native applications. One lesson per day. Each under 10 minutes.
Week 1: Foundations Week 2: Data & Context Week 3: Reliability Week 4: Multi-Agent & Production
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Week 1Foundations — Thinking in AI-Native
Day 01
What Makes an App "AI-Native"?
Foundations Day 02
LLMs as a Service: Latency, Cost, and Reliability Trade-offs
Foundations Day 03
Prompt as Code: Versioning, Testing, and Deployment
Foundations Day 04
Async-First Design: Why Sync Patterns Fail with AI
Foundations Day 05
Event-Driven Architecture for AI Pipelines
Foundations Day 06
State Management in Agentic Systems
Foundations Day 07
Week 1 Synthesis: Your AI-Native Architecture Checklist
Foundations Week 2Data & Context — Data & Context Management
Day 08
Context Windows as a Resource: Strategies and Patterns
Data & Context Day 09
RAG Architecture Deep Dive
Data & Context Day 10
Vector Databases: Choosing, Indexing, and Querying
Data & Context Day 11
Memory Systems: Short-Term, Long-Term, Episodic
Data & Context Day 12
Streaming Data Pipelines for Real-Time AI Features
Data & Context Day 13
Caching Strategies for LLM Responses
Data & Context Day 14
Week 2 Synthesis: Designing Your Data Layer
Data & Context Week 3Reliability — Reliability, Observability & Cost
Day 15
Failure Modes in AI Systems
Reliability Day 16
Retry Strategies, Fallbacks, and Circuit Breakers for LLMs
Reliability Day 17
Structured Logging and Tracing for AI Pipelines
Reliability Day 18
Evaluations (Evals): Testing AI Behavior at Scale
Reliability Day 19
Cost Control: Monitoring and Optimizing LLM Spend
Reliability Day 20
Rate Limiting and Quota Management
Reliability Day 21
Week 3 Synthesis: Your Reliability Playbook
Reliability Week 4Multi-Agent & Production — Multi-Agent Patterns & Production
Day 22
Multi-Agent Orchestration Patterns
Multi-Agent & Production Day 23
Agent Communication: Tools, Handoffs, Shared Memory
Multi-Agent & Production Day 24
Human-in-the-Loop Design Patterns
Multi-Agent & Production Day 25
Security in AI Systems
Multi-Agent & Production Day 26
Deployment Patterns: Edge vs Cloud vs Hybrid
Multi-Agent & Production Day 27
Versioning and Rollback for AI-Native Systems
Multi-Agent & Production Day 28
Building for Observability from Day 1
Multi-Agent & Production Day 29
Case Study: Designing hawkbot-mission-control as an AI-Native System
Multi-Agent & Production Day 30
Your Personal AI-Native Systems Design Blueprint
Multi-Agent & Production