On-device deployment often relies on lightweight models that require fine-tuning when deployment environments differ from development conditions or when model performance changes over time. Although recent LLM-enabled agents are capable of reasoning about system states and forming adaptive strategies, they often incur substantial computational and budgetary overhead, making them impractical for resource-constrained edge settings. This paper presents Budgeted Agentic AI for Adaptive Lightweight Model Fine-Tuning (BA2), a framework for budget-constrained lightweight model adaptation that combines bounded optimization steps with auxiliary evaluation, rollback, and compression actions. BA2 enables agents to dynamically adjust adaptation strategies according to real-time budget conditions while maximizing system performance. BA2 explicitly accounts for constrained resources, including limited fine-tuning steps, tool invocation quotas, and token consumption. An Engineer–Manager architecture is introduced, where an LLM-based Engineer generates parameterized candidate operations from system logs, and a lightweight contextual-bandit Manager selects actions conditioned on both environmental states and remaining budgets. Experimental results demonstrate that BA2 achieves superior cost-performance trade-offs compared with static heuristics and inference-time search agents under tight budget constraints, while remaining competitive with strong baselines in higher-budget regimes.
Wang et al. (Fri,) studied this question.