MemoHarness: Adaptive Agent Harnesses That Learn from Execution Experience

Jul 17, 2026

MemoHarness is a framework that adaptively optimizes agent harnesses—the control layer managing context, tools, and memory for LLM-based agents—by learning from past executions. It decomposes the harness into six editable dimensions, stores experiences in a dual-layer bank, and retrieves relevant patterns to adapt to new tasks without additional labels or search. Evaluations on shell-agent, code-generation, and analytical-reasoning benchmarks show that MemoHarness outperforms fixed harnesses and demonstrates selective transfer to unseen tasks and models.

Why it matters: This work demonstrates that execution experience can be leveraged to build more adaptive agent harnesses, potentially reducing manual tuning and improving agent performance across diverse tasks.

Full story at: arXiv AI/ML ↗