Enterprises using retrieval-augmented generation (RAG) face a recurring operational decision: whether to promote, revise, or block a system version. The paper reports on AGO, a quality gate built specifically for this choice.
The framework is described as evidence-first, meaning it weighs available evidence rather than relying solely on aggregate metrics. That matters because evaluation data is often incomplete, and the LLM judges producing those metrics are fallible.
By framing release decisions as evidence-based, AGO offers a structured way to handle uncertainty in RAG deployment. The abstract does not include experimental results, so the contribution at this stage is the framework itself.