With the growing popularity of outdoor hiking activities, participants face increasingly complex and unpredictable safety threats. Existing safety measures are inadequate due to delayed responses, high false alarm rates, and inadequate coverage of extreme environments, particularly when victims become incapacitated and are unable to call for help. To address these issues, this article proposes an intelligent safety agent system, AdventureAgent, that integrates multimodal perception and reasoning based on large language models (LLM). The system leverages common wearable devices and smartphones, following a unified “Perception–Detection–Reasoning–Planning–Action” closed-loop process: it collects and time-aligns multisource data in real time, constructs structured situational prompts, and achieves high-precision identification of dangerous states, dynamic risk scoring, and strategy generation through abductive reasoning. AdventureAgent can trigger a variety of emergency measures, including proactive distress signals, local alarms, and continuous monitoring, bridging the semantic gap between physical perception and symbolic reasoning. Experimental results demonstrate that the system significantly outperforms baseline methods based on rule engines and traditional sequential models in terms of danger detection accuracy, response timeliness, and robustness. It also shows critical advantages in explainability when handling complex and ambiguous scenarios. These findings validate the feasibility of using existing LLMs to empower personal safety applications in dynamic and high-risk environments, providing a new research paradigm to build the next generation of cognitively capable proactive intelligent guardian systems.