For coding agents, execution feedback from failures creates opportunities for cheap-model recovery that cascade-only systems miss—a learned router with budget calibration can match expensive-model performance while using 35% of the cost.
This paper addresses a key challenge in coding agents: deciding whether to retry a failed task with a cheaper model or escalate to a more expensive one. The authors train a router that learns when cheap recovery attempts are worthwhile based on execution feedback, then add a calibration layer that lets the same router work under different budget constraints without retraining.