📄 中文摘要
代理AI流水线存在一个隐性低效问题：即使面对全新的用户自然语言表达，它们也频繁重建相同的中间逻辑，例如度量标准化或图表骨架构建。传统的边界缓存机制无法解决这种低效，因为它将推理过程视为一个不可分割的黑盒。针对此问题，SemanticALLI被提出，这是一种在Alli（PMG的营销智能平台）中实现的流水线感知架构。SemanticALLI的核心思想是缓存推理过程中的语义片段，而不仅仅是最终响应。通过识别和存储那些独立于具体自然语言表述但语义上等同的中间逻辑步骤，SemanticALLI能够显著减少重复计算。例如，当用户以不同方式询问“上季度销售额”时，虽然表述不同，但背后对“销售额数据提取”、“时间范围过滤”和“求和”等逻辑操作是相同的。SemanticALLI能够识别并缓存这些语义等价的中间逻辑块，从而在后续请求中直接重用已缓存的推理结果，避免从头开始重新构建。这种方法将缓存的粒度从整个推理输出提升到推理过程中的语义单元，从而在用户表达多样化但底层逻辑相似的情况下，大幅提升代理系统的效率和响应速度。它通过深入理解流水线内部结构，将推理过程分解为可缓存的语义组件，从而突破了传统缓存对推理黑盒处理的局限性，有效解决了代理系统中的重复计算问题。
📄 English Summary
SemanticALLI: Caching Reasoning, Not Just Responses, in Agentic Systems
Agentic AI pipelines exhibit a hidden inefficiency: they frequently reconstruct identical intermediate logic, such as metric normalization or chart scaffolding, even when faced with entirely novel user natural language phrasings. Conventional boundary caching mechanisms fail to address this inefficiency because they treat the inference process as a monolithic black box. SemanticALLI is introduced as a pipeline-aware architecture implemented within Alli (PMG's marketing intelligence platform). The core principle of SemanticALLI is to cache semantic fragments of the reasoning process, rather than just the final responses. By identifying and storing intermediate logical steps that are semantically equivalent yet independent of specific natural language expressions, SemanticALLI significantly reduces redundant computations. For instance, when users inquire about “last quarter's sales” using different linguistic formulations, the underlying logical operations – such as “sales data extraction,” “time range filtering,” and “summation” – remain identical. SemanticALLI can recognize and cache these semantically equivalent intermediate logic blocks, allowing direct reuse of cached reasoning results in subsequent requests, thus avoiding reconstruction from scratch. This approach elevates the granularity of caching from entire inference outputs to semantic units within the reasoning process. Consequently, it substantially enhances the efficiency and responsiveness of agentic systems when diverse user expressions correspond to similar underlying logic. By deeply understanding the internal structure of the pipeline and decomposing the reasoning process into cacheable semantic components, SemanticALLI overcomes the limitations of traditional caching's black-box treatment of inference, effectively resolving the issue of redundant computation in agentic systems.