AI Platform Design Handbook
Engineering production AI as a system
Engineering production AI as a system
The AI Platform Design Handbook presents an opinionated architecture for building AI systems that remain evaluable, governable, observable, recoverable, and economically defensible in production.
It is written for engineers and technical leaders responsible for turning model capabilities into dependable products. Its subject is the surrounding production system: identity, retrieval, orchestration, evaluation, observability, security, deployment, economics, incident response, disaster recovery, and architectural governance.
This edition is an engineering field guide, not a vendor implementation manual. It emphasizes durable boundaries, failure modes, and decision criteria while using current production examples to keep the design concrete.
How to read the handbook
The chapters form a deliberate sequence, but each can also be used independently during design and review:
| Area | Questions addressed |
|---|---|
| Foundations | What is the platform responsible for, who owns it, and which architectural boundaries should hold? |
| Platform runtime | How should models, retrieval, tools, and agent workflows be composed and constrained? |
| Quality and operations | How do teams evaluate behavior, observe production outcomes, and operate degraded modes? |
| Governance and delivery | How are authority, safety, security, release controls, and lifecycle decisions enforced? |
| Economics and resilience | How should cost, capacity, dependency failure, continuity, and recovery shape the design? |
| Decisions and readiness | How are consequential choices recorded and how is production readiness demonstrated? |
Begin with Chapter 1: Purpose, Scope, and Engineering Principles.