One handles guest communication. One manages pricing. One monitors IoT sensors across properties. One does document extraction. And a lightweight orchestrator routes tasks between them based on context. Our single "do-everything" agent was hitting the same wall every monolithic app eventually hits — context window bloat, unreliable tool selection, and that familiar feeling of watching something clever slowly become something fragile.
Sound familiar? It should. This is the monolith-to-microservices migration, just twenty years later with LLMs instead of Java services.
The pattern is identical. You start with one agent because it's simpler. It works great for demos. Then production traffic hits, edge cases multiply, and you realize a single model trying to be an expert at five different domains becomes mediocre at all of them. So you decompose. Specialized agents with narrow tool sets, scoped context windows, and a thin orchestration layer that knows who handles what.
What surprised me: the cost dropped 40%. Not because the models are cheaper, but because most tasks route to smaller, faster models now. Only the hard stuff escalates to frontier. Exactly like how microservices let you scale hot paths independently instead of throwing hardware at the whole monolith.
The catch is also identical — distributed systems are harder to debug than monoliths. Agent-to-agent failures are the new network partitions. But Gartner's tracking a 1,400% spike in multi-agent system inquiries for a reason. The industry figured out this decomposition pattern works twenty years ago. We're just applying it to a new substrate.
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