大语言模型通过扩展的链式推理能够提升最终答案的准确率，但往往在 token 使用上效率低下，且推理时几乎无法进行控制。现有的高效推理方法通过缩短、提前终止或压缩推理轨迹来控制思考长度，但模型的思考方式仍是隐式的。本文提出智能体链式推理引导（ACTS），将推理引导建模为一个马尔可夫决策过程，其中控制器智能体在推理过程中自适应地引导一个冻结的推理器。在每一步，控制器观察当前的推理轨迹和剩余思考预算，然后发出一个引导动作，该动作包含一个推理策略和一个引导短语，用于启动推理器的下一步。这使得在保持推理器生成连续性的同时，能够实现预算感知的策略控制，从而实现高效推理。我们利用构建的合成引导轨迹（含多预算增强）来初始化控制器智能体，并通过带预算条件奖励塑形的强化学习进一步优化。在多个基准测试上的实验表明，ACTS 在实现与完整思考相当的性能的同时，大幅节省了 token，并能在不同的推理器和任务间实现可控的准确率-效率权衡。代码已开源在 https://github.com/Andree-9/ACTS。
Large language models improve final-answer accuracy through extended chain-of-thought reasoning, but often spend tokens inefficiently and offer little inference-time control. Existing efficient reasoning methods control thinking length by shortening, early-stopping, or compressing traces, leaving how the model thinks implicit. In this paper, we propose Agentic Chain-of-Thought Steering (ACTS), which formulates reasoning steering as a Markov decision process where a controller agent adaptively steers a frozen reasoner during inference. At each step, the controller observes the reasoning trace and remaining thinking budget, then issues a steering action consisting of a reasoning strategy and a steering phrase that initiates the next reasoner step. This enables budget-aware strategy control for efficient reasoning while preserving the reasoner's generation continuity. We initialize the controller agent from our constructed synthetic steering trajectories with multi-budget augmentation, and further optimize it via reinforcement learning with budget-conditioned reward shaping. Experiments across multiple benchmarks show that ACTS matches full-thinking performance with substantial token savings, and enables controllable accuracy-efficiency trade-offs across different reasoners and tasks. The code is available at https://github.com/Andree-9/ACTS.