Microsoft 365 Copilot Tuning is an Artificial Intelligence customization capability that allows organizations to create task specific Copilot agents by tuning large language models with their own data, domain knowledge, and standards. Unlike general purpose Artificial Intelligence experiences, these tuned agents are optimized for specific, repeatable workflows and operate entirely within the Microsoft 365 tenant boundary so that existing security, compliance, and governance controls continue to apply. The feature is currently offered in the Frontier early access program, which provides experimental functionality that may change over time.
Copilot Tuning emphasizes no code customization via Agent Builder, allowing business users and domain experts to configure agents using templates and curated examples instead of traditional data science skills. Supported scenarios include document writing, document summary, expert answers, document validation, style editing, and optimization agents for tasks such as resource allocation and planning. These agents can generate long form content aligned with organizational templates, produce tailored summaries for specific audiences and purposes, answer domain specific questions grounded in internal content, validate documents against policies and branding standards, refine tone and style to match brand voice, and solve optimization problems using uploaded data and organizational rules. Integration with Microsoft Graph allows tuned agents to reason over live enterprise data while honoring access control lists and sensitivity labels.
Agent tuning is organized into three complementary dimensions: context, tools, and model. Context tuning defines goals, domains, and representative examples, and uses that input to generate subgoals and evaluation rubrics to measure performance. Tool tuning adds specialized tools or other agents into the workflow with custom orchestration instructions, then reevaluates outputs against the same rubrics. Model tuning applies supervised fine tuning and reinforcement learning using high quality organizational examples, runs asynchronously within the tenant, and is published only if evaluation results meet expectations. Tunable templates with predefined inference workflows provide optimized defaults that many organizations can use without additional tuning, while more advanced users can iteratively refine agents based on real usage and feedback. Once created or tuned, agents can be shared across the organization through Microsoft 365 Copilot experiences, providing increased productivity, more accurate and consistent outputs, and broader access to organizational knowledge, subject to strict responsibilities for Artificial Intelligence administrators as data controllers over privacy, copyright, and deletion obligations.
