Agent-Architectures

A cache that’s never invalidated doesn’t know it’s stopped being correct — it just keeps serving the value it read at write time. Give a multi-agent crew a shared objective instead of a shared cache and you get the identical failure with none of the tooling that usually catches it: every agent read the goal once, at spawn, and nothing tells it when that copy goes stale. This is multi-agent context drift — distinct from goal drift (the target itself moving) and split-brain (the crew’s facts about state disagreeing) — a concrete policy-escalation failure, the mechanism, and what to instrument before a customer hears a promise nobody’s operating on anymore.

Loop drift is one agent staying busy without getting closer to done. Hand the same failure to a crew and every agent stays busy, produces defensible work, and the team’s collective objective still ends up somewhere your original request never sent it — a genuine multi-agent goal drift failure mode, not a mistake in any single agent’s reasoning. A concrete feature-build failure, the four things that make a goal decay one handoff at a time, and what to measure so a ‘done’ check compares against the original ask instead of the nearest upstream spec.

‘When not to build an agent’ made the case against the loop in the abstract — quadratic cost, serial latency, an untestable failure surface. This is the concrete follow-on: three fixed pipeline shapes (linear chain, router-plus-handlers, fan-out/fan-in) that cover most of what people default to a loop for, why each one is cheaper and more testable, and the one test for when a real loop actually earns its cost.

A single agent fails by getting the task wrong. A team of agents fails in ways no single agent can: correlated collapse, diffused responsibility, context fragmentation, and consensus that converges on nothing. The four failure modes that only exist once you have more than one agent — and why adding agents can lower reliability.

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.