CogRouter: Cognitive Load-Based Agent Routing Paper
Key Takeaways
- CogRouter: dynamically routes tasks to agents calibrated to cognitive complexity
- Cognitive complexity estimated via task embedding + historical difficulty distribution
- Routes between: fast/cheap (Haiku-class), balanced (Sonnet-class), deep (Opus-class)
- 47% cost reduction vs. always-using-frontier-model with equivalent task completion rate
- Routing errors (over/under-routing) cost more than the savings — threshold tuning critical
Summary
CogRouter addresses agent cost optimization by matching task cognitive demand to model capability. The paper’s central insight: most enterprise agent tasks follow a bimodal distribution — either simple and highly repetitive (routing inquiry, data extraction) or complex and novel (strategic analysis, open-ended research). Using frontier models for simple tasks wastes compute; using lightweight models for complex tasks degrades quality.
The routing mechanism: tasks are embedded and compared against a calibrated complexity distribution derived from historical performance data. A classifier assigns each task to a cognitive tier (fast/balanced/deep), with confidence thresholds that default to the higher tier when uncertain.
The 47% cost reduction result is measured on a 90-day production deployment at a mid-sized e-commerce company. Critical finding: routing threshold miscalibration is more dangerous than no routing — over-routing to cheap models on complex tasks causes cascading failures that are expensive to recover from. The paper recommends conservative initial thresholds with gradual loosening as calibration data accumulates.