MemoHarness: Agent Harnesses That Learn from Experience
An agent harness is the external control layer that turns a base LLM into an executable agent by managing context, tools, orchestration, memory, decoding, and output handling. While harness design strongly affects agent behavior, most automatic improvement methods optimize narrower artifacts such as prompts, pipelines, or workflows, and deployed agents usually reuse a single global harness for all cases. We introduce MemoHarness, an adaptive harness optimization framework that learns from its own executions. MemoHarness decomposes the harness into six editable control dimensions, stores per-case diagnoses and distilled global patterns in a dual-layer experience bank, and adapts the learne...
| Authors | Yue Huang, Wenjie Wang, Han Bao et al. |
|---|---|
| Primary category | cs.AI |
| Published | 14 July 2026 |
| Last updated at source | 14 July 2026 |
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