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 learned harness to each test case using retrieved experience without test-time labels, feedback, or additional search. Across shell-agent, code-generation, and analytical-reasoning benchmarks, MemoHarness consistently improves over fixed harness baselines, transfers across unseen suites and base models, and remains cost-effective through reusable cached context. These results suggest that execution experience is a practical substrate for building agent harnesses that are more adaptive than static hand-engineered configurations.