Agentic AI pipelines suffer from a hidden inefficiency: they frequently reconstruct identical intermediate logic, such as metric normalization or chart scaffolding, even when the user's natural language phrasing is entirely novel. Conventional boundary caching fails to capture this inefficiency because it treats inference as a monolithic black box. We introduce SemanticALLI, a pipeline-aware architecture within Alli (PMG's marketing intelligence platform), designed to operationalize redundant reasoning. By decomposing generation into Analytic Intent Resolution (AIR) and Visualization Synthesis (VS), SemanticALLI elevates structured intermediate representations (IRs) to first-class, cacheable artifacts. The impact of caching within the agentic loop is substantial. In our evaluation, baseline monolithic caching caps at a 38.7% hit rate due to linguistic variance. In contrast, our structured approach allows for an additional stage, the Visualization Synthesis stage, to achieve an 83.10% hit rate, bypassing 4,023 LLM calls with a median latency of just 2.66 ms. This internal reuse reduces total token consumption, offering a practical lesson for AI system design: even when users rarely repeat themselves, the pipeline often does, at stable, structured checkpoints where caching is most reliable.

翻译：智能体AI流程存在一种隐藏的低效问题：即使用户的自然语言表述完全新颖，系统仍频繁重建相同的中间逻辑，例如指标归一化或图表框架构建。传统的边界缓存无法捕捉这种低效性，因为它将推理过程视为单一的黑箱。我们提出了语义ALLI——这是Alli（PMG营销智能平台）内部的一种流程感知架构，旨在实现冗余推理的操作化。通过将生成过程分解为分析意图解析（AIR）和可视化合成（VS），语义ALLI将结构化中间表示（IR）提升为可缓存的一级构件。在智能体循环中进行缓存的影响是显著的。在我们的评估中，基线单一缓存因语言多样性限制在38.7%的命中率。相比之下，我们的结构化方法通过新增可视化合成阶段实现了83.10%的命中率，绕过了4,023次LLM调用，且中位延迟仅为2.66毫秒。这种内部复用降低了总令牌消耗，为AI系统设计提供了重要启示：即使用户很少重复相同表述，流程本身也常在稳定、结构化的检查点重复执行——而这些正是缓存最可靠的环节。