Multi-Agent AI Engineering

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Principles of Multi-Agent Systems

Principles are cheap to state and expensive to violate.

An agentic system combines model capability with state, tools, control flow, evaluation, and operating policy. It may place those responsibilities inside one loop or divide them across several agents and deterministic services. When multiple agents coordinate, their interfaces and shared state can produce system behavior that no component owns alone, but agent count does not separate "real" agency from an ordinary workflow. A fixed graph can contain model-driven decisions, and a single agent can choose its own path through tools at runtime. The useful questions are where decision authority sits, which effects it may cause, and how the system observes and ...