Designing Multi-Agent Intent Classifiers for Enterprise Scale

Architecting Cognitive Agent Routers

Enterprise multi-agent architectures require structured message routing to execute complex task pipelines. In this article, we outline a routing design pattern that routes intents through five specialized agent nodes, maintaining strict contextual isolation and token efficiency.

1. The Intent Router Node

The entry node parses incoming prompts and decides which agent should take action. We discuss prompt boundary weights, temperature tunings, and vector-supported categorization.

2. Managing Context Memory

When multiple LLM agents communicate, context memory can grow exponentially, resulting in high latency and API fees. We describe a sliding context window solution using Redis caches.