In enterprise operations, the context required for an AI agent task is scattered across systems of record, static information stores, and communication channels. What is stored is system state, a lossy representation of the work that actually happened. The prevailing approach retrieves by matching request content to what is stored; for narrow requests this works well. But synthesis quality depends on knowing what to surface and how to interpret it: knowledge specific to each organization, team, and individual, present in behavioral patterns, absent from any retrieval index. For the agentic task of proposing enterprise-valuable leads to sellers, this approach breaks down: True Lead Rate is low, False Lead Rate is high, and the model has no mechanism to improve. We present X-SYNTH, a framework for enterprise context synthesis grounded in digital human attention, the digitally observable interaction signatures of each worker, encoding what they did, the sequence in which they did it, and implicit reward signals. Behavioral traces preceding positive outcomes are distinguishable from those that did not, without external labeling. X-SYNTH models each individual's behavioral baseline as a Digital Twin Signature (DTS) and selects among seven attention filters, Proportional, Inverse, Differential, Recurrent, Comparative, Sequential, and Collective, per individual and per query, to identify causally relevant activity signatures. A four-stage pipeline assembles ranked context grounded in behavioral patterns rather than query embeddings. A frontier model unaided achieves 9.5% True Lead Rate (TLR) with 90.5% False Lead Rate (FLR). Augmented with X-SYNTH, TLR rises to 61.9% (6.5x) while FLR falls to 18.8%. Enterprise context synthesis is not a retrieval problem. It is a relevance problem, and digital human attention is its most reliable ground truth.

翻译：在企业运营中，AI智能体任务所需的情境分散于记录系统、静态信息存储库和通信渠道中。存储的是系统状态，即对实际发生工作的一种有损表征。主流方法通过将请求内容与存储内容进行匹配来实现检索，对于窄范围请求效果良好。但情境合成质量取决于知道要呈现什么以及如何解读：每个组织、团队和个体特有的知识存在于行为模式中，却不见于任何检索索引。对于向销售者推荐具有企业价值的线索这一智能体任务，该方法失效：真实线索率低、虚假线索率高，且模型缺乏改进机制。我们提出X-SYNTH，一个基于数字人类注意力的企业情境合成框架——数字人类注意力即每个工作者可数字观测的交互特征，编码其行为内容、行为序列及隐含奖励信号。无需外部标注，即可区分积极结果前后的行为轨迹。X-SYNTH将每个个体的行为基线建模为数字孪生特征（DTS），并针对每个个体和每次查询，从七种注意力滤波器（比例、逆比例、差分、循环、比较、序列、集体）中选取，以识别因果相关的活动特征。四阶段流水线基于行为模式（而非查询嵌入）生成排序情境。未经辅助的前沿模型实现9.5%真实线索率（TLR）与90.5%虚假线索率（FLR）；经X-SYNTH增强后，TLR提升至61.9%（6.5倍），FLR降至18.8%。企业情境合成并非检索问题，而是相关性问题的体现，而数字人类注意力是其最可靠的基准事实。