Multi-Agent System

A multi-agent system splits work across agents with distinct roles — a researcher, a writer, a critic — coordinated either by a supervisor agent or by passing work between peers.

The genuine advantages are focus and parallelism. Each agent has a narrower brief and a smaller context, which helps; and independent subtasks can run simultaneously, which is a real speedup on research-style problems that fan out.

The costs are underrated. Every handoff loses context. Errors compound across agents rather than being caught. Cost multiplies, since each agent runs its own model calls. And debugging becomes considerably harder — a wrong final answer may originate several agents upstream.

The pattern that reliably earns its complexity is a critic: one agent produces, another reviews against explicit criteria. That mirrors how review actually improves work.

Multi-agent architectures are heavily marketed and frequently adopted before a single well-orchestrated agent has been tried. The honest question is whether the subtasks are genuinely independent — if they are, it pays; if they aren't, you have bought coordination overhead.

Start with one agent and good orchestration. Add agents when you can name the specific subtask that parallelizes, and add a critic before you add more producers.