Topic
Meta Harness
3 episodes
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Harness Zero: Harness Distillation via Agent as Harness
Harness-Zero turns a great, evolved agent harness into training data so the underlying model permanently learns those behaviors and can run on a much simpler harness — often matching or even beating the original setup.
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Meta Harness: End to End Optimization of Model Harnesses
Meta-Harness automates harness engineering by using a coding agent to search over harness code, giving it full access to prior execution traces and scores via a filesystem rather than compressed summaries. On text classification, it improves 7.7 points over prior systems while using 4× fewer context tokens; on math reasoning, a single discovered harness improves IMO-level problems by 4.7 points; on TerminalBench-2, it ranks #1 for Claude Haiku 4.5 agents. The core insight is that harnesses operate over long horizons—a single retrieval or storage choice affects behavior many steps later—so rich, adaptive access to full diagnostic history beats compressed feedback.
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Better Harness: A Recipe for Harness Hill Climbing with Evals
Onyx and Echo dig into Better Harness as a claim about where agent improvement actually lives: not just in model weights, but in the loop that sources evals, edits the harness, and protects generalization. They like the practical recipe, but they keep the mechanism honest, especially where the system can overfit the optimization set or turn useful instructions into token waste.