Commerce-agent decoding gains require stronger quality and cost validation

The customer problem is slow structured query generation. Include the workflow owner, platform team and finance. Ask whether inference dominates delay, which outputs require human correction, and whether capacity can actually be released. Offer a bounded comparison for one approved workflow. The value hypothesis is reduced waiting while preserving usable output. Do not quote the paper's GPU-count comparison as cash savings or promise equivalent results for government records, advising or procurement workflows.

Freeze the checkpoint, schema, prompts and hardware allocation, then compare the incumbent and candidate configurations. Require approved draft artifacts and a reproducible environment. Measure successful requests, output length, tail latency and independent human-reviewed task quality at expected peak demand. Keep external agent actions disabled during testing and restrict trace access. Request exact engine versions and rerun the disputed cost comparison before sizing production. A successful proof of value demonstrates a local result, not universal NIM inferiority.

The application service owner should coordinate inference engineers, domain reviewers and finance. Implement shadow evaluation, staged routing and rollback; dependencies include a representative test set and reviewer availability. Train maintainers to detect output-format regressions and users to report incorrect results. Proposed acceptance criteria are no material quality regression against the agreed rubric, lower measured waiting time under peak load, and a documented rollback drill. Review those gates before adoption. Risks include biased evaluation and savings that never become releasable budget.