From Location-Based Services to Agentic Location-Integrated Social-Spatial Services: A Scoping Review of Agentic AI in Location-Based Services

Location-based services (LBS) have traditionally helped users answer questions such as where to go or what is nearby. With the emergence of large language model (LLM)-based agents, LBS are beginning to move toward understanding open-ended goals, coordinating spatial tasks, and supporting decisions about what to do and how to do it. This review refers to LBS that integrate such agentic capabilities into location-dependent service processes as Agentic LBS. To systematically characterize the current state of Agentic LBS and identify directions for future research, we conducted a scoping review and proposed an Autonomy–Innovation–Experience (A-I-E) framework as its analytical lens. Following the PRISMA Extension for Scoping Reviews (PRISMA-ScR), we searched the ACM Digital Library, Web of Science, and Scopus for peer-reviewed, English-language journal articles and conference papers published between 2021 and 2026. The search identified 228 records. After removing 21 duplicates, 207 records underwent screening, and 27 studies were included in the final review. The included studies were systematically charted and analyzed through descriptive mapping and qualitative coding to characterize their application directions, autonomy levels, innovation patterns, and user-experience implications. The results show that: (1) Agentic LBS research remains concentrated at lower autonomy levels, with 23 of the 27 studies (85.18%) classified as L1–L2 and four as L3; nevertheless, the emergence of L3 systems indicates a gradual movement toward more complex task planning and coordination. (2) LLM-based agents are expanding LBS beyond location-based information retrieval toward more personalized, context-aware, and task-oriented service delivery. (3) Reported user-experience benefits are concentrated in usability, accessibility, personalization, and task efficiency, whereas evidence concerning trust, user control, privacy, safety, and long-term use remains limited. (4) By synthesizing these findings across autonomy, innovation, and experience, this review proposes a future-oriented conceptual framework for understanding the emerging trajectory of Agentic LBS.