Srinivas ☁️🇮🇳

Google open-sourced Harness²! Harness² is a framework for recursively improving the harness around an agent while keeping the underlying model weights fixed. The interesting part is that it does this at two different levels. For the current task, Harness² uses a Propose → Probe → Compose loop. It proposes changes to the harness, runs those variants on the same task, compares how the executions behave, and then composes a better harness from that evidence. That is the task-level recursion. But the process does not reset after every task. Harness² also keeps the harnesses it adopted and the lessons supported by execution evidence, then uses those to guide how future harnesses should be improved on new tasks. That is the harnessing-level recursion. So one loop improves the harness for the task in front of the agent. The second loop improves how the system learns to build better harnesses over time. The editable harness itself can include the system prompt, guardrails, memory, tool guidance, subagents, skills, scripts, and plugins. You can also run the recursion in two ways: Parallel recursion explores more candidate harnesses. Sequential recursion carries the refined harness and accumulated evidence through multiple Propose → Probe → Compose steps. Google reports that even a single Harness² step improved GLM-5 and Gemini 3.5 Flash across JobBench, LAB, and WorkBuddy-Bench, with larger task-level recursion budgets bringing further gains. That is what makes Harness² more interesting than just another agent framework. It is treating the harness itself as something that can be iteratively improved from execution evidence, both within a task and across tasks. 100% open source. I’ve shared the GitHub repo in the comments!