Towards Long-Horizon Agents: A Survey

With the rapid advancement of LLM capabilities, expectations for AI agents are shifting from solving simple, single-turn tasks toward carrying out long-horizon tasks in the real world. We refer to such systems as long-horizon agents: agents that plan over extended horizons, interact with real-world environments, recover from their own mistakes, and adapt their strategies during execution. This capability is rapidly becoming the central bottleneck for practical agent intelligence. Despite this progress, the field still lacks a shared definition and taxonomy for long-horizon agents. Related concepts such as "self-evolving" or "autonomous" agents are often used interchangeably, none of which clearly captures what it means to build a long-horizon agent. This gap leaves the community without a principled way to attribute advances in long-horizon competence. This paper offers a unified landscape by framing long-horizon agency as the co-evolution of an externalized harness and an internalized optimization, and organizes the paper around six connected perspectives: Foundation, Evolution, Harness, Optimization, Application, and Frontier. We first formalize long-horizon agency as a harness-coupled decision process and distinguish it from neighboring concepts such as long-running execution, autonomy, and self-evolution. We then trace the field's evolution from prompt-level control to runtime agent systems, classifying existing work through the complementary lenses of externalized harnesses and internalized optimization. Building on this view, we organize five application forms of long-horizon agents by their interfaces, and review the corresponding benchmarks and resources. Finally, we discuss key challenges and frontier directions. Looking ahead, we hope this paper serves not only as a reference for existing work, but also as a foundation for building the next generation of capable, reliable long-horizon agents.