Learn
Sixteen modules from the Principal Engineer mindset through agent identity, each following the same structure: mental model, architecture, production example, failure modes, and interview questions.
Principal AI Engineer Handbook
Production AI systems, agentic architecture, and distributed infrastructure — documented like it ships, not like it's interview notes.
What this is
Section titled “What this is”A reference for the Principal and Staff AI Engineer track: production architecture, hands-on labs built to the same bar as the systems they describe, and interview material that stays honest about trade-offs instead of listing buzzwords.
The handbook covers a small number of systems in depth and looks at each from four angles — the concepts, a design review, running code, and the interview round it appears in. Start here explains which section to open and shows how the four connect.
Build
Production-quality labs — not toy demos. The Async AI Gateway lab ships JWT auth, distributed rate limiting, health-aware routing, and graceful draining with a full test suite.
Architecture
Reference architectures with request flows, failure modes, cost, and production deployment notes — the shape a design review actually needs.
Interview
Interview questions embedded next to the material that answers them, plus system design and leadership tracks for Principal-level rounds.
How the handbook is organized
Section titled “How the handbook is organized”| Section | Purpose |
|---|---|
| Learn | Sixteen structured modules, mindset through agent identity |
| Build | Production labs living in labs/ |
| Architecture | Reference architectures for recurring AI infrastructure problems |
| Interview | Question tracks mapped to the material that answers them |
| Reference | One-page lookups for tools and primitives (asyncio, Kafka, Raft, …) |
| Decision Records | Why the handbook itself — and its labs — are built the way they are |
| Cheat Sheets | Printable, one-page summaries |
| Roadmap | Current version and what ships next |
Latest
Section titled “Latest”Latest labs
- LangGraph Checkpoint CostOct 8, 2026
- Multi-Tenant MCP ServerOct 8, 2026
- Region Failover BudgetOct 8, 2026
Latest modules
- Module 6: MCPOct 8, 2026
- Module 7: LangGraphOct 8, 2026
- Module 11: CloudOct 8, 2026
Current status
Section titled “Current status”Sixteen Learn modules, eleven labs, seven architecture pages, three interview tracks, thirteen reference lookups, five cheat sheets. Modules 0–15 are live, engineering mindset through agent identity. Seven of the eleven labs have a matching architecture page; the four newest — agent identity, model routing, semantic caching, and evaluation — have a module and a Build page but no design review yet. Reference is complete, and its four fast-moving pages each name the release they were verified against, on a review window CI enforces. The Roadmap says what does not exist yet.