AI Architecture Guides

Why these architectures, in this order

The five guides above cover the patterns that show up in nearly every production AI platform we've built or reviewed. They're ordered roughly by where most teams hit friction first. Most teams can ship a usable AI feature with a single LLM call — but the moment you add a second tool, a second tenant, or a second deployment environment, the architecture decisions compound. The guides below surface the design choices that pay off, and the ones that quietly tax every release for years.

When to use which guide

If you're building agents that touch user data or external systems, start with AI Agent Architecture — it covers the security primitives (least-privilege tools, audit trails, rollback workflows) that turn agents from demos into production components. If your retrieval pipeline returns inconsistent or cross-tenant results, the RAG Architecture guide covers hybrid search, metadata filtering, and citation tracking patterns that scale.

For platform teams, the Multi-Tenant SaaS and AI Platform Development Stack guides cover the infrastructure layer — what stays in your monolith, what becomes a service, and which managed providers actually save engineering time versus quietly creating coupling. The AI Platform Security guide is the safety net under all of it: prompt injection defenses, PII detection, model output controls, and the compliance patterns that hold up to enterprise procurement.

These cover the common ground. Every team eventually hits a question the patterns don't answer — multi-region orchestration, custom model fine-tuning, regulated data handling, agent-to-agent protocol design. That's where focused architecture reviews come in.