Small language models are attractive in production because they are inexpensive, fast, and easy to specialize, but specialization remains a difficult loop. The hard decisions surround training: acquiring and curating supervision, diagnosing failures, selecting a training strategy, and preventing a targeted fix from breaking behavior that already works. We present Pioneer Agent, a closed-loop system that automates this lifecycle. In cold-start mode, the agent turns a task specification into a trained model by searching jointly over data composition, hyperparameters, and learning strategy. In production mode, it converts judged failures into a failure taxonomy and corrective curriculum, retrains, and retains an update only when it passes regression gates. We introduce AdaptFT-Bench, seven staged scenarios that combine fixable noise with harmful supervision at increasing poison rates. Across eight cold-start benchmarks, Pioneer Agent improves over the base checkpoint on every task. Across all seven AdaptFT-Bench scenarios, it improves or preserves the deployed checkpoint while indiscriminate retraining degrades, with final gaps up to 43 points. In two production-style case studies, the same loop raises intent-classification pass rate from 84.9% to 99.3% and entity F1 from 0.345 to 0.810. These results show that deployment failures can be converted into effective training curricula without manual intervention between iterations.