Microsoft Framework to Cut AI Agent Training Costs

Elena Chertovskikh via Getty ImagesMicrosoft Research released Orchard, an open source framework that aims to make it easier and more cost-effective to train autonomous AI agents.The release by the tech giant’s research division on Monday follows ongoing work since the vendor’s initial publication on this topic in March.Orchard can train and evaluate agents across several tasks, including coding, web browsing and using tools, while reducing complexity by eliminating the need for researchers to build sandbox infrastructure, data pipelines and evaluation systems for individual models and use cases.“While there is excitement around agentic AI’s capabilities, the research community faces a persistent bottleneck. Building state-of-the-art agentic systems often requires proprietary infrastructure … that most researchers and practitioners cannot access or reproduce,” the vendor said in a blog post.Microsoft created Orchard to address this problem. At the core of the new framework is Orchard Env, which Microsoft describes as a “lightweight, Kubernetes environment that provides reusable isolated components".Related:Oracle Brings Google Gemini Models to Enterprise CustomersThese can be used to run and build agents at scale, from collecting training data to reinforcement learning rollouts and evaluation, without rebuilding the underlying architecture.To demonstrate its approach, Microsoft released three domain-specific training recipes -- Orchard-SWE, Orchard-GUI, and Orchard-Claw -- along with the training data and evaluation methods used to build them.Orchard-SWE trains software-engineering agents, Orchard-GUI focuses on browser navigation, and Orchard-Claw covers everyday productivity tasks. Results published for models trained through each showed they were competitive on established benchmarks, Microsoft said.According to Microsoft, the results could have significant implications. It said: “By making the underlying infrastructure open, lightweight, and reusable, Orchard lowers the cost of agentic AI research,” the vendor said in the blog. “Teams no longer need to build custom isolated environments from scratch or depend on proprietary cloud services.”“Looking ahead, we see reusing training experience as a promising direction toward cumulative agent learning. Instead of discarding trajectories once a training run finishes, we treat them as persistent assets -- for example, distilling them into reusable value models.”About the AuthorContributing WriterGraham Hope has worked in automotive journalism in the U.K. for 26 years, including spells as editor of leading consumer news website and weekly Auto Express and respected buying guide CarBuyer.