Optimizing LLM Inference Costs in Multi-Agent Systems with Adaptive Model Routing

An adaptive model router can optimize inference costs in multi-agent systems by dynamically selecting the appropriate large language model (LLM) for each specific sub-task. Instead of statically assigning a powerful model to all agents, this architecture uses a lightweight, cheaper model to classify each task's complexity, reasoning requirements, and context size. Based on a combined score, the task is routed to a fast, balanced, or powerful LLM tier, ensuring resources are used efficiently. This just-in-time approach allows for granular cost control and adapts to unpredictable queries by distributing planning to individual agents rather than handling it upfront.