AutoSaddler: Durable Harness Optimization for Agents
rohanpaul_ai · x · 2026-08-27
Automatically patching an agent's harness is easy, but keeping helpful updates is hard. The paper AutoSaddler finds that without checking generalization, optimized harnesses perform worse than hand-written ones.
Key Approach:
- Spend rollout budget on a held-out split to catch collateral damage.
- AutoSaddler treats the harness as code, patching prompts, tools, and middleware based on failed traces.
- Keeps only updates that improve a held-out development set.
Advice: When tuning agents, hold out tasks not targeted by the patch and score fixes minus regressions rather than fixes alone.
Related event: Microsoft's AutoSaddler Auto-Patches LLM Agent Frameworks(3 posts)→
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