Semantic Caching Can Cut LLM Token Usage by Half in Repetitive Agent Loops

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Developers building AI agent loops often overlook how frequently near-duplicate prompts are sent to language models, wasting tokens and adding latency. An audit of a controlled prototype found that roughly 35% of prompts were semantically similar to previously answered ones. A semantic cache placed in front of the model endpoint computes similarity scores between incoming and cached prompts, returning stored responses when a set threshold is met. The approach uses character n-gram Jaccard similarity, requiring no external dependencies or embedding APIs. Threshold tuning — settling around 0.92 in the prototype — balances cache hit rate against the risk of returning mismatched answers for subtly different queries.

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