Super interesting finding on optimizing agent harnesses.
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Super interesting finding on optimizing agent harnesses. When you optimize an agent harness for both quality and cost, apply your cost preference only at the end of the search, since applying it at every step throws away harnesses that lead to better ones. That is the key insight behind Microsoft's ParetoSaddler. At each step, an optimizer edits a parent harness. Both versions run several times, and a Bayesian bootstrap over those runs decides whether the candidate shows credible progress in quality or in cost. Promoted candidates stay available as parents, and the final deployment choice i
Posted by elvis (327.6k followers) 1 h ago · 19 likes · 2.5k views · view the original post on X. Kept by the Dev Radar as AI dev tools. Tools mentioned: arXiv.org.
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