AI & Agent Evaluation
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Before You Ship Your Agent: A Five-Step Path to Evaluations You Can Trust

engineering blog · source date 2026-07-08 · added 2026-08-07 15:52:43 · updated 2026-08-07 15:52:43 · Open original blog

Problems / challenges / motivations

  • Teams adopt LLM judges to scale agent evaluation and then never check whether the judge itself is reliable.
  • Observability research tends to land as separate studies rather than one usable path.
  • "Ship when evals look good" needs an actual gating mechanism to mean anything.

Key ideas

  • Rolls up five Microsoft Foundry observability studies into one prescriptive five-step path for measuring agents.
  • A dedicated step measures the LLM judges doing the measuring — validity and reliability compared across six judge models.
  • Ends with a benchmark-gating release pattern: run sets like GPQA Diamond or MuSR against your own deployment as a release gate.

Why it matters for evals

  • It is the de-facto production guide for "how to trust your evals," and the judge-of-judges step is what separates it from generic eval advice.
  • The gating pattern is the operational piece most teams are missing: an eval that cannot block a release is a dashboard, not a test.
  • Caveat: vendor content, and the underlying studies are Microsoft-run and not independently validated. Use it as production methodology guidance rather than as independent eval evidence.

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