If you want to learn how to build self-improving harnesses, check out this paper.
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If you want to learn how to build self-improving harnesses, check out this paper. They present Exo, an agent runtime architecture for in-harness learning and adaptation during ongoing work. Harness optimization usually runs offline, so it needs a representative dataset and many expensive runs before the agent improves. Exo moves that loop into normal task execution. The agent edits its own harness code while it works. A small protected layer keeps the execution history and controls which policy version is active, so every change can be inspected. On AppWorld and SREGym, Exo cuts held-out
Posted by DAIR.AI (136.7k followers) 1 h ago · 6 likes · 836 views · view the original post on X. Kept by the Dev Radar as AI dev tools. Tools mentioned: arXiv.org.
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