The automated and intelligent processing of massive remote sensing (RS) datasets is critical in Earth observation (EO). Existing automated systems are normally task-specific, lacking a unified framework to manage diverse, end-to-end workflows--from data preprocessing to advanced interpretation--across diverse RS applications. To address this gap, this paper introduces CangLing-KnowFlow, a unified intelligent agent framework that integrates a Procedural Knowledge Base (PKB), Dynamic Workflow Adjustment, and an Evolutionary Memory Module. The PKB, comprising 1,008 expert-validated workflow cases across 162 practical RS tasks, guides planning and substantially reduces hallucinations common in general-purpose agents. During runtime failures, the Dynamic Workflow Adjustment autonomously diagnoses and replans recovery strategies, while the Evolutionary Memory Module continuously learns from these events, iteratively enhancing the agent's knowledge and performance. This synergy enables CangLing-KnowFlow to adapt, learn, and operate reliably across diverse, complex tasks. We evaluated CangLing-KnowFlow on the KnowFlow-Bench, a novel benchmark of 324 workflows inspired by real-world applications, testing its performance across 13 top Large Language Model (LLM) backbones, from open-source to commercial. Across all complex tasks, CangLing-KnowFlow surpassed the Reflexion baseline by at least 4% in Task Success Rate. As the first most comprehensive validation along this emerging field, this research demonstrates the great potential of CangLing-KnowFlow as a robust, efficient, and scalable automated solution for complex EO challenges by leveraging expert knowledge (Knowledge) into adaptive and verifiable procedures (Flow).

翻译：海量遥感数据集的自动化与智能化处理对地球观测至关重要。现有自动化系统通常是任务特定的，缺乏一个统一的框架来管理跨不同遥感应用、从数据预处理到高级解译的多样化端到端工作流。为弥补这一空白，本文提出了CangLing-KnowFlow，一个集成了过程知识库、动态工作流调整和进化记忆模块的统一智能体框架。该过程知识库包含162个实际遥感任务中的1008个专家验证工作流案例，用于指导规划并显著减少通用智能体中常见的幻觉问题。在运行时发生故障时，动态工作流调整模块能够自主诊断并重新规划恢复策略，而进化记忆模块则持续从这些事件中学习，迭代增强智能体的知识与性能。这种协同作用使CangLing-KnowFlow能够适应、学习并在多样复杂的任务中可靠运行。我们在KnowFlow-Bench上评估了CangLing-KnowFlow，这是一个包含324个受真实应用启发的工作流的新基准测试集，测试了其在使用从开源到商业的13种顶级大语言模型作为后端时的性能。在所有复杂任务中，CangLing-KnowFlow的任务成功率至少超过Reflexion基线4%。作为该新兴领域首次最全面的验证，本研究通过将专家知识融入自适应且可验证的过程，展示了CangLing-KnowFlow作为一个稳健、高效、可扩展的自动化解决方案应对复杂地球观测挑战的巨大潜力。