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Perspectives on evaluation, observability, and shipping AI systems with confidence — from the team building Aegis.
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Engineering notes, product updates, and field lessons from the front lines of AI assurance.
On the same ordered test cases and thresholds, we compared pass/fail labels across frameworks. Agreement ranged from strong alignment on several suites to sharp splits where rubrics measure different things.
A straight path from deciding what you measure to running your metric on real data, with room to learn from tooling and published work along the way.
Measures LLM or agent performance to ensure reliable, safe, and production-ready behavior, while helping identify issues and guide improvements.