Agent-Architectures

Most requests to your agent are easy, and you’re paying frontier-model prices for all of them anyway. A routing cascade — try the cheap model, escalate on a measurable confidence signal — cuts spend without touching output quality, if you get the escalation trigger right. Here’s the pattern, where it breaks, and the arithmetic on when it’s worth building.

An agent is an LLM that controls its own control flow — and that autonomy has a price you pay on every run: quadratic token cost, serial latency, and a failure surface you can’t unit-test. Most tasks people reach for an agent on are a fixed pipeline wearing a costume. Here’s the decision checklist I use, the arithmetic on what the agent tax actually costs, and the same task built both ways so you can see the difference.

A short note on what Loop & Retry is for, and the one bias I’m writing against.