{"id":7917,"date":"2026-06-11T09:00:00","date_gmt":"2026-06-11T16:00:00","guid":{"rendered":"https:\/\/find.codeghost.online\/en-us\/microsoft-copilot\/blog\/?post_type=copilot&p=7917"},"modified":"2026-07-21T16:53:52","modified_gmt":"2026-07-21T23:53:52","slug":"who-evaluates-the-evaluators-the-data-science-behind-agent-evals","status":"publish","type":"copilot","link":"https:\/\/find.codeghost.online\/en-us\/microsoft-copilot\/blog\/copilot-studio\/who-evaluates-the-evaluators-the-data-science-behind-agent-evals\/","title":{"rendered":"Who evaluates the evaluators? The data science behind agent evals"},"content":{"rendered":"\n

\n At Microsoft Copilot Studio<\/a>, we talk a lot about ways to make agents better. You can add knowledge sources, fine-tune models, insert prompts, and more. But how do you know whether an agent is actually getting better? How do you detect regressions before your users do? And perhaps most importantly, how do you trust the signals you’re using to make those decisions?\n<\/p>\n\n\n\n

\n As data scientists, we don\u2019t ship a model without evaluating it. We evaluate it before the first release, and we evaluate it after every meaningful change.\n<\/p>\n\n\n\n

\n We validate offline, track metrics over time, compare variants, look for regressions, and ask a simple question: Did this change actually make the model better?<\/em>\n<\/p>\n\n\n\n

When we started building evaluation features in Copilot Studio<\/a>, we treated them the same way. We asked: Is the evaluation giving the right answer to \u201cis it better\u201d? <\/em>This is the quality question.<\/strong><\/p>\n\n\n

\"Side-by-side\n\t\t\t\n\t\t\t\t\n\t\t\t<\/svg>\n\t\t<\/button><\/figure>\n\n\n\n

To answer it, we’ll explore three core areas of AI evaluation quality:<\/p>\n\n\n\n

    \n
  1. The data behind evaluation, and why generated datasets play such an important role in agent testing.<\/li>\n\n\n\n
  2. The evaluators (graders) themselves, and how we validate that graders produce reliable signals.<\/li>\n\n\n\n
  3. The metrics we use to determine whether those signals are trustworthy enough to support real-world decisions.<\/li>\n<\/ol>\n\n\n\n

    \n Because as organizations increasingly rely on evaluation to improve AI systems, confidence in the evaluation process<\/a> becomes just as important as confidence in the agent.\n<\/p>\n\n\n\n

    \n
    Evaluate your agents with Copilot Studio<\/a><\/div>\n<\/div>\n\n\n\n

    From model evaluation to agent evaluation<\/h2>\n\n\n\n

    \n Traditional machine learning evaluation is relatively well\u2011defined. You have:\n<\/p>\n\n\n\n