Layered agent harnesses improve software over repeated autonomous development cycles

Harness-of-Harness coordinates existing coding agents through incremental planning, implementation, testing, and independent evaluation.

Academic
Haoyang Yan · Min-le Su · Hangfan Zhang · Zhanhao Li · Chen Zhang · Shao Zhang · +3 more

Shanghai Artificial Intelligence Laboratory

Research Digest··2 min read

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HoH enables coding agents to continually improve software in autonomous development, achieving 52.25% average gain over standalone harnesses.

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The paper introduces Harness-of-Harness (HoH), a framework that wraps existing coding-agent harnesses and organizes autonomous software development into iterative planning-coding-testing loops. HoH balances repair with capability growth, scopes work into small verifiable increments, separates implementation tests from independent evaluation, and progressively exposes tools and deliverables while encouraging reuse and maintaining versioned histories.

On three benchmarks (GameCraft-Bench, FrontierSWE, ProgramBench) covering game development, repository-level software engineering, and program reconstruction, HoH consistently outperformed standalone harnesses across three different harness-model pairs. After three iterations, the average relative gain was 52.25%, with a maximum of 82.86%. In a multi-day deployment with over 70 iterations, HoH autonomously produced a first-person-shooter game with a coherent storyline, core mechanics, playable experience, polished visuals, and integrated audio, demonstrating that iterative loops with careful state management can sustain improvement over long development trajectories without human intervention.

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Research Digest

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