Learn to build custom harnesses, folks! Here is a highly recommended paper from Google on recursive agent harnessing. Great insights if you work on evolving your harnesses. It also offers an important discussion of recursive self-improvement and evolving harnesses for open-world tasks. They focus on improving your agent harness at test time without a verifier. This allows interesting scaling properties. Harness^2 proposes several edits to the harness for the current task, tests them against each other, and keeps the combination that works best. Lessons from those edits then update a second harness that the agent carries to future tasks. Results: It improved GLM-5 and Gemini 3.5 Flash in every configuration tested across WorkBuddy-Bench, JobBench, and LAB. Running five candidate edits in parallel adds another 2.9 to 3.9 points on WorkBuddy-Bench. ReasoningBank, a memory baseline, fell up to 6.6 points below the base harness on coding tasks in the same comparison. https://arxiv.org/abs/2610.14224 Chat with Paper: https://academy.dair.ai/papers/harness-2-recursive-agent-harnessing-for-an-open-world-2610.14224