地球观测（Earth Observation, EO）正从静态预测转向需对数据、工具及地理空间状态进行协同推理的多步骤分析工作流。尽管基础模型（foundation models）和视觉-语言模型（vision-language models）已在遥感领域推动了表征学习与语言驱动的交互能力，且智能体人工智能（agentic AI）在长程推理与工具调用方面展现出强大潜力，但EO并非通用智能体AI的简单延伸。EO工作流处理的是具有地理参考坐标、多模态且时间结构化的数据；重投影（reprojection）、重采样（resampling）、合成（compositing）与聚合（aggregation）等操作会改变底层状态，并可能制约后续分析。因此，错误可能在各步骤间隐性传播，而正确性不仅取决于内部逻辑一致性，更依赖于地理空间一致性、时间上有效的比较以及物理合理性。本文认为，这些挑战具有结构性而非偶然性。我们审视了通用智能体系统中常见的假设，分析其在地理空间工作流中的失效机制，并刻画多步骤EO流水线中的典型失效模式。随后，我们提出面向EO原生智能体的设计原则，聚焦于结构化地理空间状态建模、工具感知型推理、验证器引导式执行，以及有效性感知的学习与评估。因此，构建可靠的地理空间智能体，需围绕支配EO分析的物理约束、地理空间约束与工作流约束，重新思考智能体的设计范式。

Earth Observation (EO) is moving beyond static prediction toward multi-step analytical workflows that require coordinated reasoning over data, tools, and geospatial state. While foundation models and vision-language models have advanced representation learning and language-grounded interaction in remote sensing, and agentic AI has shown strong potential for long-horizon reasoning and tool use, EO is not a straightforward extension of generic agentic AI. EO workflows operate on georeferenced, multi-modal, and temporally structured data, where operations such as reprojection, resampling, compositing, and aggregation transform the underlying state and can constrain later analysis. As a result, errors may propagate silently across steps, and correctness depends not only on internal coherence but also on geospatial consistency, temporally valid comparisons, and physical validity. This position paper argues that these challenges are structural rather than incidental. We examine the assumptions commonly made in generic agentic systems, analyze how they break in geospatial workflows, and characterize failure modes in multi-step EO pipelines. We then outline design principles for EO-native agents centered on structured geospatial state, tool-aware reasoning, verifier-guided execution, and validity-aware learning and evaluation. Building reliable geospatial agents, therefore, requires rethinking agent design around the physical, geospatial, and workflow constraints that govern EO analysis.