For agentic workflows, route once per task using task-level feedback instead of routing each LLM call independently—this lets the system learn which models work best for different task types while optimizing accuracy-latency trade-offs.
TRACE-Router is a routing system that assigns agentic AI tasks to different LLMs once at the start, rather than deciding per-call. It learns which model works best for each task type using delayed feedback from task completion, balancing accuracy and speed. This approach outperforms existing routers by aligning decisions with actual task outcomes.