Abstract

Proposed herein is a system that includes a control plane that measures whether current artificial intelligence (AI) evaluation coverage in a platform still matches real production usage. The system can provide such features by capturing structured live usage, mapping both production interactions and benchmark items into a shared workflow-intent graph, identifying uncovered usage regions, and surfacing those gaps so teams can decide whether to add or remove benchmark coverage or add or remove guardrails. The technical distinction of the system proposed herein is not ordinary drift monitoring or release gating. Rather, the technical distinction is the continuous alignment of real-world AI usage with evaluation coverage, combined with a concrete mechanism for deciding what the system should support and what it should explicitly block.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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