MemoHarness: Optimizing the Agent Harness
omarsar0 · x · 2026-07-17
This paper focuses on optimizing the agent harness—the external control layer that turns a base LLM into an executable agent.
The core method, MemoHarness, decomposes the harness along the inference process into 6 editable control planes: context, tools, generation, orchestration, memory, and output. Without relying on test-time labels, feedback, gradient updates, or additional search, the system adapts to new cases by retrieving similar historical cases based on diagnostics and cross-case patterns.
It achieves a score of 0.806 on the shell-agent benchmark, outperforming the strongest fixed harness baseline at 0.722, while maintaining a lower per-task cost than the most powerful commercial baseline compared in the text.
More from coding & agent
- Cheaper OpenAI Agents API alternative: sandbox service undercutting E2B by 46% — airesearch12 · 2026-09-11
- His agent kill switch ran for months before he found it was wired to nothing — AnvilandCode · 2026-09-11
- Kernel's Browser Agents Can Now Pay Online Using Aliases, Never Touching Card Data — jeff_weinstein · 2026-09-11
- OpenAI opens up agent sandboxes: BYO or pick from Cloudflare, E2B, Modal, Vercel and more — threepointone · 2026-09-11
- SocialCrawl MCP lets agents search Reddit, YouTube, TikTok, X with one API key — dooddyman · 2026-09-11
- Astra builds a surprisingly polished Catan game in three.js, reusing past UI and 3D assets — FinanceYF5 · 2026-09-11