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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.
We ran Aegis bias 640 times on one planted example, across nine models and multiple reasoning levels. Higher reasoning helped some judges and made others more lenient — and they still disagreed about nationality confidence.
Five practical metric areas (relevance, factual groundedness, faithfulness to sources, safety, and instruction compliance) that most production LLM stacks should track.
Why choose Aegis as an evaluation platform? Eight things to consider when comparing evaluation platforms.
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.