Optimizing AI Agent Pipelines with SemanticALLI

AI agent pipelines often reconstruct the same intermediate logic, even when the natural language input is new. Traditional caching fails to capture this inefficiency.

SemanticALLI, an architecture developed within PMG's Alli marketing intelligence platform, addresses this problem by decomposing generation into Analytic Intent Resolution (AIR) and Visualization Synthesis (VS). This allows structured intermediate representations (IRs) to be elevated to first-class, cacheable artifacts.

Performance and Benefits

The research shows that baseline monolithic caching achieves a maximum hit rate of 38.7%. SemanticALLI, thanks to structured caching in the Visualization Synthesis stage, reaches 83.10%, avoiding 4,023 model calls with a median latency of just 2.66 ms. This internal reuse reduces total token consumption, demonstrating that caching at structured checkpoints is effective even when users do not repeat their requests.

For those evaluating on-premise deployments, there are trade-offs to consider carefully. AI-RADAR offers analytical frameworks on /llm-onpremise to evaluate these aspects.