Google open-sources Harness², a self-improving agent harness
Google Research has open-sourced Harness², a framework that recursively rewrites an agent's harness without changing model weights, under Apache 2.0 on GitHub with support for OpenCode and Codex. It combines task-level propose-probe-compose edits with a cross-task improvement library that stores evidence-backed lessons. Google reports gains with Gemini 3.5 Flash and GLM-5 on WorkBuddy-Bench, JobBench and LAB — for example, GLM-5 rose from 64.2 to 75.7 on WorkBuddy-Bench under OpenCode — while noting the project is research, not an officially supported product, and that extra recursion costs more test-time compute.
If the vendor-reported results hold, Harness² illustrates a path to stronger agents through runtime harness self-editing rather than weight changes; however, its recursive search requires more model calls, and Google says it remains a research project.
| Google just released Harness2 as open source. It turns any AI harness into a system that improves itself over time. A harness is everything around the model. System prompt, tools, memory, skills, and subagents. Normally a person designs it once and the agent uses that fixed setup for every new task. With Harness2 the agent itself refines the harness for each task. It proposes changes, runs a few candidate versions, and picks a better one by comparing how they perform. No correct answers or graders are needed. The model weights stay the same. What it learns on one task carries over to the next. After finishing a task it saves the improved harness and useful lessons so future edits start smarter instead of from scratch. Google tested it with Gemini 3.5 Flash and GLM-5 on legal, office, and professional work benchmarks. Even one round of edits beat the original harnesses. More rounds pushed scores higher. GitHub https://github.com/google-research/harness2 submitted by /u/chemist_slime [link] [comments] |
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