Impressive new paper from Microsoft and colleagues.

Harness design is still hand-tuned almost everywhere. This work present an automated loop to optimize the harness.

They introduce AutoSaddler, which treats the agent harness as code and learns to patch it offline from failure traces.

It runs mini batches of tasks, diagnoses what broke, generates structured patches to prompts, tool configurations, and control logic, then keeps an update only if it survives validation.

Gains of 9.0 points on GAIA2, 9.6 on SWE-Bench Pro, and 10.0 on Terminal-Bench 2.0 over the corresponding base harnesses.

Deep debugging beats shallow reflection, targeted edits beat unconstrained editing, and generalization-aware selection beats repairing the one trajectory in front of you.

Paper: https://t.co/PoDahO6rmz

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