Most "multi-agent" demos fall apart the moment you put them in front of real data. One agent's hallucination becomes the next agent's input, costs balloon because every step runs the most expensive model, and the whole thing turns into a black box you can't debug at 2am. I run a production system that operates my own company on a tiered agent pipeline, and the patterns that keep it reliable are boring on purpose. This is a walkthrough of the ones that matter, with runnable Python against the Claude API.
I'll use a generic example throughout: an operations-intelligence pipeline that pulls from a few business systems, verifies what it found, and produces a weekly brief. The shape generalizes to almost any "read a bunch of sources, reason across them, produce a trustworthy output" problem.
Match the model tier to the reasoning depth
The single biggest lever on cost and reliability is not using one model for everything. A multi-agent pipeline naturally splits into layers, and each layer wants a different amount of horsepower:
L1 — extraction and routing. Pull structured fields out of raw source data, classify records, decide what goes where. High volume, shallow reasoning. This is Haiku's job.
