Frontier Tuning: Microsoft Build 2026

Key Takeaways

With Frontier Tuning, we're making it possible for you to create your own enterprise AI. With the models and the harness tuned on your data and your workflows. Let me show you how you can build your own hill climbing machine. M AI Thinking 1 is now available in private preview in the Foundry Model Catalog. You can go ahead and deploy the model as is, or if you want to go on your own hill climbing journey, you can start by clicking the fine-tune button. Now, I'm in the fine-tuning UI. The first thing I'll do is add my data set. Next, I'm going to add a grader, and that's it. I go ahead and submit the job. Couple of hours in, I see how the model is learning. It generates rollouts, it scores them, and we start to hill climb. Now, if you want full control over your RL training loop, let me give you a sneak peek into our low-level training API. You can see here I have the M AI Thinking model. I can also configure my rollout strategy, as well as my hyper parameters, to define exactly how I want my training algorithm to work. In fact, I can also incorporate my own RL gym here by defining the tools that this model interacts with. All right. You just saw all the code. But, as an M365 customer, you're never starting from scratch. Let's hop on to Copilot. As a part of Frontier Tuning, we're introducing a new way to build RL environments based on your data and your workflows. One of our customers, Land O'Lakes, one amongst the largest agribusinesses in the United States, is in fact using this to perfect that butter on your morning toast. Let me walk you through their environment. Now, on a high level, the environment consists of skills, knowledge, and tools. But, in the back end, we create an entire RL gym for your agents to continuously learn the way that you work. Now, let's look at the butter report generation skill here. These tasks are very complex. They require many manual steps and a high degree of precision. Now, in these tasks, even 80% accuracy isn't good enough. To help clients to higher accuracy, we're extending the industry definition of skills to include rubrics on what good looks like. But, this is just one task. How do I scale this to codify all of the tasks in enterprise? You spend a lot of time in M365, in Teams, Outlook, Word, Excel, and PowerPoint. We use those signals to suggest skills and rubrics that truly define the way that you work. Next, you can add your organizational knowledge for grounding, like from OneDrive, SharePoint. Now, the environment comes built in with Microsoft tools, and you can add custom tools to it. Because these tools tap into real workflows, we virtualize them to simulate execution, so the model can learn without actually impacting the live state of your business. And now, my most favorite part, the science. By generalizing all of these learnings into the main model, as well as in the embedding model, we're able to help clients for tasks that require high accuracy. And not only this, we are able to hill climb to more than 90% accuracy for Land O Lakes tasks using the MEI model. In fact, we estimate this model to be 10x more efficient than the baseline models. Now with this environment setup, let's go back to butter reporting. Let's run this task this time using this tuned environment as an inferencing harness. Now usually this task takes a couple of minutes to run. In the meantime, I will show you a cached response. Now as you can see here, the agent has leveraged test time pre-search with multiple models including a fine-tuned model. And what you see here is a summary that doesn't feel generic. It feels undoubtedly Land O Lakes. And that's not it. The task holds itself to high standards and it continuously retrospects and evaluates itself. This way, with frontier tuning, your agent continuously improves with your compliant RL environment on your data encoding the way you work. Now that is what I call frontier tuning as smooth as butter. We cannot wait to see the environments you built. Back to you, Satya.