Stanford research shows switching agent harnesses mid-run cuts cost and boosts performance
StanfordAILab · x · 2026-10-21
Research shared by Stanford AI Lab (jamesyzou, citing ykwon0407's thread) finds you don't have to stick with one agent harness for an entire task. Switching harnesses mid-run — e.g. Codex → Pi, or starting in Claude Code then moving to Pi or Codex — can dramatically reduce cost and token usage while improving performance. The authors frame this as "harness cascade": different harnesses approach tasks differently and catch mistakes the previous one missed, opening a new Pareto frontier of cost-vs-quality trade-offs.
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