A consistent finding across two years of agent-system engineering is that the one giant agent that does everything design is a dead end. It fails the cascading-error math from Scaling Agentic AI. It produces brittle monoliths. It can’t be tested. The pattern that actually works is the small team — three or four specialized agents with clear roles, talking to each other through structured channels — and this short post is the plain-language introduction.
This is the Explained Simply version, written for the engineer hearing about multi-agent systems for the first time. The longer treatment is in the Building a Team of AI Agents monograph, which covers the same territory with the protocols, orchestration patterns, failure modes, and governance fully developed.
What it covers
About ten minutes of reading. Six sections.
§ 1 — Why “team,” not “super-agent.” The cascading-error argument in one paragraph. Why specialization beats generalization at the agent level for the same reason it does at the human level.
§ 2 — The four canonical roles. Planner (decides what to do). Doer (does it). Tool-user (calls APIs). Learner (revises the plan based on what happened). Most production multi-agent systems are some variation on these four.
§ 3 — Feedback loops between agents. How they talk. The handoff protocol. The “is your output complete enough that the next agent can use it?” check.
§ 4 — Orchestration patterns. Sequential, supervisor-led, hierarchical. The three shapes that account for almost every real system.
§ 5 — When multi-agent is theater. The honest caveat. For simple tasks, a multi-agent system is more expensive and slower than a single well-designed loop. The decision tree.
§ 6 — Where to go next. Pointer to the long monograph for the deeper treatment.
Read it
The short version lives at its own URL with a warm-paper layout — four color-coded role chips. Companion to the longer Building a Team of AI Agents monograph.
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