Intent classification deployments trade accuracy against retraining effort and inference cost. We study frozen generative reranking and budgeted large-model escalation, introducing Semantic-Candidate Adaptation (SCA): BGE top-5 retrieval followed by joint reranking with one representative training example per candidate. On CLINC150, aligned examples raise Qwen2.5-3B-Instruct from 68.18% label-only accuracy to 76.13% and Gemma-2-2B-it from 66.02% to 76.15%, without LLM parameter updates. CLINC150 wrong-example controls support the role of correct alignment, while BANKING77 and HWU64 provide transfer evidence. Broader comparisons clarify the deployment regimes: direct centroid retrieval reaches 80.71%, while a task-trained MPNet Energy baseline reaches 90.05%; always-on SCA is therefore not a universal replacement for direct or trained classifiers. A validation-selected low-margin SCA fallback provides only a marginal gain at an 8.62% realized test call rate. For real Qwen3.7-Max escalation on CLINC150, routing the lowest BGE top-1 retrieval scores reaches 82.11–82.89% accuracy at a 10% budget and 87.33–88.07% at 20%, with OOS F1 of 70.00–70.13% and 87.47–87.70%, respectively. Learned and token-confidence routing remain alternatives, but no policy dominates every evaluated point.