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