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流程存在一种隐性低效问题：即便用户自然语言表述完全新颖，系统仍频繁重构相同的中间逻辑（如指标归一化或图表框架构建）。传统边界缓存因将推理视为单一黑箱而无法捕捉此低效性。本文提出语义ALLI——这是Alli（PMG营销智能平台）内一种流程感知架构，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）与可视化合成（VS）两个阶段，语义ALLI将结构化中间表示（IR）提升为可缓存的一级实体。在智能体循环内部实施缓存具有显著效益：评估显示，基线单一缓存因语言变异性导致命中率上限仅为38.7%；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，成功规避4,023次LLM调用且中位延迟仅2.66毫秒。这种内部复用机制有效降低了总令牌消耗，为AI系统设计提供了重要启示：即使用户极少重复表述，流程本身仍常在稳定、结构化的检查点重复执行——而这正是缓存机制最可靠的实施场景。