Research & academia
Orchard: An open framework for scalable agentic AI
Orchard treats the agent’s runtime environment as reusable infrastructure: one Kubernetes-based service supports training and evaluation across task domains and real deployment harnesses.
Why it matters: Researchers can reuse the same environment for data generation, reinforcement-learning rollouts, and evaluation, rather than rebuilding infrastructure for each task. Training inside deployment harnesses also avoids the mismatch caused by training on a simplified stand-in.