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Microsoft ASSERT

A tiernew this week

Turn plain-English AI behavior specs into a full scored test suite — no hand-coded test cases required.

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Kai's verdict

ASSERT is one of the more practically useful things to come out of Microsoft Build 2026 — it finally bridges the gap between 'we wrote a system prompt' and 'we actually know if the agent follows it.' The MIT license and framework-agnosticism mean there's no real reason not to try it. (Verdict pending Phi's full review.)

Strengths

  • Natural-language specs auto-generate structured behavior taxonomies, test cases, and scored results end-to-end
  • Framework-agnostic: works across LangChain, CrewAI, AutoGen, LlamaIndex, Semantic Kernel, and 100+ model endpoints via LiteLLM
  • Trace-aware evaluation — captures full OpenTelemetry/OpenInference spans so the judge sees tool calls, routing, and intermediate decisions, not just final output
  • LLM judge hits 80–90% agreement with human annotators, making it credible at scale
  • Usable at build time, post-deployment, and for continuous regression monitoring

Weaknesses

  • Garbage-in, garbage-out: vague specs produce vague test scenarios, so quality of evals depends heavily on how well devs write their behavior descriptions
  • Synthetic test cases can miss failure modes that only emerge in real production traffic
  • Model-based judges can be unreliable on subtle or highly domain-specific policy distinctions — not a substitute for human review

Best for

Dev teams shipping LLM apps or AI agents who need application-specific behavioral evals but don't want to hand-craft hundreds of test cases from scratch.

Pricing

Free (MIT open source)

Fully open source under MIT license; no paid tiers. Costs may apply for underlying LLM judge/model provider API calls.

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