Google's Harness²: the agent rewrites its own harness

Claim

Google Research released Harness² (2026-10-06, Apache-2.0, 18.9k stars by 2026-10-24): a framework in which the agent refines its own harness for each task and carries the lessons forward, with model weights fixed. Eight components are editable: system prompt, guardrails, memory, tool guidance, subagents, skills, scripts, plugins. It runs on top of OpenCode or Codex as the executor. This answers Katz's "is the harness dead" with: the hand-designed harness is; a per-task, agent-edited one is the next layer.

Evidence

• README (read 2026-10-24): "A harness is the system around a model that enables it to act as an agent: prompts, tools, memory, skills, orchestration, and more. Harnessing is the process of optimizing that system."
• Two recursions: "Task-level recursion refines the execution harness for the current task through a contrastive Propose-Probe-Compose step: propose edits, probe their effects on the same task, and compose a refined harness from behavioral contrasts between executions, without ground-truth feedback." And: "Harnessing-level recursion updates a persistent improvement harness with adopted harnesses and evidence-supported lessons from each task."
• Results, from the README: "a single Harness² step improves GLM-5 and Gemini 3.5 Flash over their base harnesses, with gains of up to 11.5 points on WorkBuddy-Bench, 4.5 points on JobBench, and 11.8 percentage points on LAB's partial pass rate."
• Executors: "The release supports parallel and sequential recursion, OpenCode and Codex executors." docs/SETUP.md takes the executor model in OpenCode's provider/model format (HARNESS2_SOLVER_MODEL, HARNESS2_IMPROVER_MODEL).
• Paper: Xu, Chen, CuiZhu, Dalvi Mishra, Ming, Yu, Han, Lee, Pfister, "Harness²: Recursive Agent Harnessing for an Open World", arXiv 2610.14224, 2026-10-09. Abstract: "open-world tasks are typically unseen during the harness design phase and novel at test time, leaving no ground-truth verifiers to reliably guide online harness edits."
• Community reading (r/LocalLLaMA, 2026-10-18, top of the week): the thread's sharpest objection is that a self-edited harness "can overfit to the task", which makes benchmark numbers from such systems hard to compare; the same commenter calls it exceptional for repetitive enterprise tasks.

Open questions

• Whether the gains hold with Claude models as executor; the paper reports GLM-5 and Gemini 3.5 Flash only.
• Cost of a Propose-Probe-Compose step per task; the README says only that "model-backed runs incur API costs".
• Whether the improvement harness transfers across domains or only within a benchmark's task family.

Sources

• https://github.com/google-research/harness2 — repo, Mon Oct 05
• https://arxiv.org/abs/2610.14224 — paper, Thu Oct 08
• https://www.reddit.com/r/LocalLLaMA/comments/1x9gfnb/google_open_sources_harness2_for_self_improving/ — blog, Sat Oct 17

AI-assisted research: gathered and summarised with AI from the sources above, each claim checked against them.