Great new paper from AWS on agent handoff tax. If you build agents today, you need to understand the so-called handoff tax. (bookmark it) Escalating to a stronger model mid-run is usually the resort when a cheap agent stalls. New work from AWS AI Labs measures how much that switch actually costs. Coding agents run for dozens of model calls, so teams escalate when a weak model struggles and downshift once the hard reasoning is done. Every switch forces the receiving model to continue a trajectory another model wrote. Across pairs of Claude and GPT models, full-trajectory escalation recovers less than half the quality gap between the weak and strong model while adding a substantial cost premium. The authors call that penalty the handoff tax. Downshifting lands at a much better cost-quality point. Cutting the weak model's trajectory information improves escalation quality, while removing the strong model's trajectory hurts downshift quality. Paper: Track more trending AI papers in our academy: