EvoHarness-RL trains agents to manage their runtime workspace

EvoHarness-RL studies how long-horizon language-model agents should use an external runtime workspace. Agents often need a harness to track state, invoke tools, verify results, and reuse experience, but today that harness behavior is usually hand-written in prompts or custom rules.

The paper frames harness policy learning as an offline training problem. An agent learns how to construct and update external state, then applies that policy online while working through long tasks.

The contribution is about agent infrastructure rather than a new chatbot interface. It reflects a larger shift in AI automation: reliable agents need learned or at least systematically tested control over their environment, not just a bigger model placed inside an improvised loop.