Living system · production patterns · trade-offs before recipes
Architecture Pattern Library
A reference library for the AI architecture decisions senior engineers actually face. Every pattern names its trade-offs and the case where it's the wrong choice — no recipe without the cost. Four of the seven are proven in this platform's own production system, not borrowed from a whitepaper.
The patterns
How do I stop an agent from running away?
The Bounded Agentic Loop: hard turn and tool-call ceilings, least-privilege tools, and graceful escalation instead of a runaway agentic loop burning budget or taking unsafe actions.
How do I make retrieval answers provable?
RAG with Grounded Citations: every retrieved answer carries its source passages, so a claim can be checked instead of trusted — the difference between a demo and a system you can ship.
How do I stop paying Opus prices for Haiku work?
Role-Based Model Routing: route each call to the cheapest model that meets its quality bar — hints to Haiku, tutoring to Sonnet, evals to Opus — instead of one model for everything.
How do I know a prompt change didn't make things worse?
Eval Harness as Release Gate: a scored eval suite runs before every prompt or model change ships, so regressions get caught by a number, not a user complaint.
Which agent actions need a human sign-off?
Human-in-the-Loop Approval Boundaries: draw the line between what an agent can do autonomously and what needs a human approve step — and where to enforce it so the boundary can't be bypassed.
What should an agent remember, where, and for how long?
Agent Memory and State: separate working context, session history, and durable per-user memory, each with its own storage and lifetime, instead of one growing context window.
One safety filter isn't enough — what are the layers?
Guardrails Defense-in-Depth: stack input filtering, bounded tool scope, output checks, and monitoring, because any single guardrail layer will eventually be bypassed.
Kept current, visibly
A stale pattern page is worse than none. Every page carries a dated last reviewed kicker, and the drifty facts — model names and pricing, guardrail feature sets, exam/framework numbering, provider API shapes — get re-checked on a schedule, not left to rot. That maintenance discipline is what the membership funds.
Learn the patterns, then build the system underneath them.
The patterns are free and public. The aiArch curriculum teaches the engineering underneath them — agents, retrieval, evals, and production architecture — on a platform that runs these same patterns in its own build.
See how aiArch helps senior engineers become AI-native, or compare Professional Membership pricing.
Free sample — no signup · every claim cited · full curriculum is waitlist-only