Most early multi-agent prototypes wire agents together with direct calls: planner calls researcher, researcher calls analyst, analyst calls writer. That works for demos, but it creates brittle dependency chains as soon as the system needs reliability, observability, or live upgrades.
An event-driven model is a better fit for production. Agents subscribe to the work they understand and emit structured events when they complete, fail, escalate, or need more context. That makes it possible to add a new specialist agent, replace an expensive model, or replay a workflow without redeploying the entire system.
For enterprise AI, the architecture around the model matters as much as the model itself. Service buses, durable queues, event envelopes, registries, and cost telemetry are what turn a clever agent chain into software that can survive real usage.