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Microsoft's Orchard is an open-source framework for agentic modeling, providing a Kubernetes-native environment substrate, RL training stack, and datasets for software engineering, GUI, and computer use agents.
The tweet highlights the potential of agent harnesses to capture and distill experts' tacit knowledge as new training data, referencing a Berkeley AI Summit talk by Jianfeng Gao on agentic modeling as an emerging AI paradigm.
The author summarized Professor Jianfeng Gao's presentation at the Berkeley AI Summit 2026, pointing out that the new AI modeling paradigm is based on Agentic Modeling and a new data flywheel, and emphasizing that Harness will become the key to application moats.
Microsoft Research and collaborators introduce Orchard, an open-source framework for scalable agentic modeling featuring a lightweight Kubernetes-native sandbox environment. It achieves state-of-the-art results on SWE-bench Verified (67.5%) and GUI benchmarks (68.4% average) with small models.
Orchard is an open-source framework for scalable agentic modeling that enables training diverse autonomous agents, achieving state-of-the-art results on coding, GUI navigation, and personal assistance tasks.