holy sh*t, Google Research just open-sourced an agent that can improve its own harness steal this blueprint for a self-improving agent the prompts, memory, skills and tool guidance around your model can evolve as it works it’s called Harness² the loop: → propose changes to the agent’s setup → run candidate setups on the same task → compare what actually happened → compose a refined harness from that evidence → carry useful lessons into future tasks the clever part: it refines the harness on unfamiliar tasks without a ground-truth answer to guide those edits the architecture: propose → probe → compose → retain lessons → repeat the part builders should steal: treat your agent’s setup as something you can experiment on pick a recurring failure propose a specific harness change compare execution traces on the same task evaluate the result keep the evidence with the change the repo includes OpenCode and Codex executors, parallel and sequential refinement, and a local demo you can run without model credentials save this, then read the full guide for Opus 5.5 Harness Engineering ↓