API Architecture for AI Systems: Capability, Authority, and Evidence

AI can produce an endpoint before your team has agreed what it promises. An API is more than a route, schema, or generated interface. It is a promise made to a consumer-and someone must own what that promise means when identity changes, requests repeat, dependencies fail, or the system evolves. API Architecture for AI Systems is a practical field guide for designing APIs as owned capabilities. It connects consumer intent, domain boundaries, authorization, idempotency, compatibility, failure handling, and operational evidence. Use this field guide to: - Define the capability and outcome a consumer is actually promised.- Separate resources, operations, commands, queries, and business outcomes.- Establish ownership, authorization, compatibility, and failure boundaries.- Design idempotency, concurrency, pagination, quotas, and bulk operations deliberately.- Choose appropriately among APIs, events, and durable workflows.- Govern AI-generated APIs and agent tool interfaces.- Leave important architecture decisions with evidence instead of assumptions. The book includes decision diagrams, chapter-ending mnemonics, practical exercises, architecture review questions, and reusable companion artifacts. It is written for software architects, engineers, technical leaders, API owners, platform teams, and anyone responsible for systems that must remain understandable after implementation. This is Field Guide 01 in the AI Systems Architecture Field Guides series. AI can generate the endpoint. You still have to architect the promise.