Choosing the right AI system is rarely as simple as picking
the newest or largest model — and agents add their own layer of variability on
top. A model that wins on a public leaderboard can still underperform on the
reasoning, math, or domain-knowledge questions that matter for your
application, and an agent that solves one task well can regress on another
after a prompt or tool change. The only way to know is to measure, on the same
benchmark, under the same conditions, every time something changes.
Benchmarks in Microsoft Foundry (preview) make that kind of
measurement a first-class part of the development workflow. You can run
well-known open-source benchmarks against any model deployment or agent in your
project, compare runs side by side in the evaluation group view, and drive the
whole flow from the portal or the REST API.