Enterprise AI agents increasingly handle complex, long-term tasks, but their effectiveness is limited by the static, manually crafted harnesses that link foundation models to their environments. Xiaomi’s researchers developed HarnessX, a dynamic framework that treats the harness as a modular, upgradable component, enabling autonomous code improvement and adaptation to specific application needs. Through a reinforcement learning-driven system called AEGIS, HarnessX breaks down AI behavior into interchangeable processors and evolves the harness by analyzing execution traces, avoiding pitfalls like reward hacking and catastrophic forgetting. Tested across diverse benchmarks and models—including Claude Opus, GPT-5.4, and Qwen3.5-9B—HarnessX demonstrated significant performance gains, with smaller models benefiting the most. Notably, on some tasks, harness evolution improved performance by up to 44%, and co-evolution with model training added further boosts. Despite current dependencies on powerful meta-agent models, HarnessX highlights that refining AI scaffolding, rather than solely scaling models, is a vital path to advancing AI capabilities.
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