SemanticALLI: Caching Reasoning, Not Just Responses, in Agentic Systems

TL;DR

SemanticALLI introduces a pipeline-aware caching architecture that significantly improves reuse of intermediate reasoning steps in agentic AI systems, reducing redundant computations and latency.

Contribution

It decomposes generation into structured IRs and elevates them as cacheable artifacts, enabling higher cache hit rates and efficiency in AI pipelines.

Findings

Achieved an 83.10% cache hit rate with structured IRs

Reduced 4,023 LLM calls and median latency to 2.66 ms

Outperformed baseline monolithic caching with 38.7% hit rate

Abstract

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.