AGO AI Quality Gate Framework for Industrial RAG Assessment
September 30, 2026
The AGO framework provides an evidence-first approach to RAG deployment decisions by integrating deterministic checks, stratified beta-binomial regression risk quantification, and mandatory LLM judge meta-evaluation. It addresses the unreliability of LLM judges by treating missing data and errors as explicit outcomes.
HOW THIS AFFECTS YOU
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builderYou can move beyond simple LLM metrics to more robust, probabilistic quality gates in production RAG systems.
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founderYou can build more reliable enterprise-grade RAG products by implementing evidence-first deployment protocols.