In the realm of artificial intelligence, the performance of agents critically depends on their runtime harness, which includes prompts, tools, memory, and control flow that dictate how models observe, reason, and act.
However, today's harnesses remain largely hand-crafted and static; each new model or task requires bespoke scaffolding, and the rich traces produced during execution are rarely distilled back into systematic improvements.
We introduce HarnessX, a foundry for composable, adaptive, and evolvable agent harnesses. HarnessX assembles typed harness primitives via a substitution algebra and adapts them through AEGIS, a trace-driven multi-agent evolution engine that establishes an operational mirror between symbolic adaptation and reinforcement learning. It closes the harness-model loop by converting trajectories into harness updates and model training signals.
Across five benchmarks (ALFWorld, GAIA, WebShop, tau^3-Bench, and SWE-bench Verified), HarnessX yields an average gain of +14.5% (up to +44.0%), with the largest gains where baselines are lowest. These results suggest that agent progress need not come solely from model scaling; composing and evolving runtime interfaces from execution feedback is a viable and complementary lever. The complete codebase will be open-sourced in a future release.
Blogger's Review: The introduction of the HarnessX project presents a fresh perspective on agent development, especially in the integration of dynamic adaptation and evolution. It showcases the immense potential of optimizing agent performance through execution feedback, which could significantly drive research and application advancements in the agent domain. Looking forward to its specific implementation post-open source.