For real-world deployment of general-purpose LLM agents, the core challenge is often not tool use itself, but efficient domain adaptation under rapidly evolving toolsets, APIs, and protocols. Repeated LoRA or SFT across domains incurs exponentially growing training and maintenance costs, while prompt or schema methods are brittle under distribution shift and complex interfaces. We propose \textbf{Activation Steering Adapter (ASA}), a lightweight, inference-time, training-free mechanism that reads routing signals from intermediate activations and uses an ultra-light router to produce adaptive control strengths for precise domain alignment. Across multiple model scales and domains, ASA achieves LoRA-comparable adaptation with substantially lower overhead and strong cross-model transferability, making it ideally practical for robust, scalable, and efficient multi-domain tool ecosystems with frequent interface churn dynamics.

翻译：对于通用大语言模型智能体的实际部署而言，核心挑战往往并非工具使用本身，而是在快速演变的工具集、API和协议下实现高效的领域自适应。跨领域重复进行LoRA或监督微调会导致训练与维护成本呈指数级增长，而提示或模式方法在分布偏移和复杂接口下则显得脆弱。我们提出**激活引导适配器（ASA）**，这是一种轻量级、推理时、无需训练的机制，它从中间层激活中读取路由信号，并利用一个超轻量级路由器生成自适应控制强度，以实现精确的领域对齐。在多种模型规模与领域中，ASA以显著更低的开销实现了与LoRA相当的适应性能，并具备强大的跨模型可迁移性，这使其特别适用于接口频繁变动的、鲁棒、可扩展且高效的多领域工具生态系统。